id	sid	tid	token	lemma	pos
esrj-105296	1	1	issn	issn	PROPN
esrj-105296	1	2	1794	1794	NUM
esrj-105296	1	3	-	-	SYM
esrj-105296	1	4	6190	6190	NUM
esrj-105296	1	5	e	e	NOUN
esrj-105296	1	6	-	-	NOUN
esrj-105296	1	7	issn	issn	PROPN
esrj-105296	1	8	2339	2339	NUM
esrj-105296	1	9	-	-	SYM
esrj-105296	1	10	3459	3459	NUM
esrj-105296	1	11	https://doi.org/10.15446/esrj.v28n2.105296	https://doi.org/10.15446/esrj.v28n2.105296	NOUN
esrj-105296	1	12	earth	earth	PROPN
esrj-105296	1	13	sciences	sciences	PROPN
esrj-105296	1	14	research	research	PROPN
esrj-105296	1	15	journal	journal	PROPN
esrj-105296	1	16	earth	earth	PROPN
esrj-105296	1	17	sci	sci	PROPN
esrj-105296	1	18	.	.	PUNCT
esrj-105296	2	1	res	res	PROPN
esrj-105296	2	2	.	.	PUNCT
esrj-105296	3	1	j.	j.	PROPN
esrj-105296	3	2	vol	vol	PROPN
esrj-105296	3	3	.	.	PROPN
esrj-105296	4	1	28	28	NUM
esrj-105296	4	2	,	,	PUNCT
esrj-105296	4	3	no	no	INTJ
esrj-105296	4	4	.	.	NOUN
esrj-105296	4	5	2	2	NUM
esrj-105296	4	6	(	(	PUNCT
esrj-105296	4	7	june	june	PROPN
esrj-105296	4	8	,	,	PUNCT
esrj-105296	4	9	2024	2024	NUM
esrj-105296	4	10	):	):	PUNCT
esrj-105296	4	11	161	161	NUM
esrj-105296	4	12	174	174	NUM
esrj-105296	4	13	g	g	NOUN
esrj-105296	4	14	eo	eo	PROPN
esrj-105296	4	15	ph	ph	PROPN
esrj-105296	4	16	y	y	PROPN
esrj-105296	4	17	si	si	PROPN
esrj-105296	4	18	c	c	NOUN
esrj-105296	4	19	s	s	PROPN
esrj-105296	4	20	abstract	abstract	ADJ
esrj-105296	4	21	investigation	investigation	NOUN
esrj-105296	4	22	of	of	ADP
esrj-105296	4	23	the	the	DET
esrj-105296	4	24	performances	performance	NOUN
esrj-105296	4	25	of	of	ADP
esrj-105296	4	26	support	support	NOUN
esrj-105296	4	27	vector	vector	NOUN
esrj-105296	4	28	machine	machine	NOUN
esrj-105296	4	29	,	,	PUNCT
esrj-105296	4	30	random	random	ADJ
esrj-105296	4	31	forest	forest	NOUN
esrj-105296	4	32	,	,	PUNCT
esrj-105296	4	33	and	and	CCONJ
esrj-105296	4	34	3d-2d	3d-2d	NUM
esrj-105296	4	35	convolutional	convolutional	ADJ
esrj-105296	4	36	neural	neural	ADJ
esrj-105296	4	37	network	network	NOUN
esrj-105296	4	38	for	for	ADP
esrj-105296	4	39	hyperspectral	hyperspectral	ADJ
esrj-105296	4	40	image	image	NOUN
esrj-105296	4	41	classification	classification	NOUN
esrj-105296	5	1	eren	eren	PROPN
esrj-105296	5	2	can	can	AUX
esrj-105296	5	3	seyrek1	seyrek1	PROPN
esrj-105296	5	4	*	*	PROPN
esrj-105296	5	5	and	and	CCONJ
esrj-105296	5	6	murat	murat	PROPN
esrj-105296	5	7	uysal1,2	uysal1,2	PROPN
esrj-105296	5	8	1	1	NUM
esrj-105296	5	9	.	.	PUNCT
esrj-105296	6	1	geomatics	geomatics	PROPN
esrj-105296	6	2	engineering	engineering	NOUN
esrj-105296	6	3	,	,	PUNCT
esrj-105296	6	4	faculty	faculty	NOUN
esrj-105296	6	5	of	of	ADP
esrj-105296	6	6	engineering	engineering	NOUN
esrj-105296	6	7	,	,	PUNCT
esrj-105296	6	8	afyon	afyon	NOUN
esrj-105296	6	9	kocatepe	kocatepe	PROPN
esrj-105296	6	10	university	university	PROPN
esrj-105296	6	11	,	,	PUNCT
esrj-105296	6	12	tr-3200	tr-3200	PROPN
esrj-105296	6	13	,	,	PUNCT
esrj-105296	6	14	afyonkarahisar	afyonkarahisar	PROPN
esrj-105296	6	15	,	,	PUNCT
esrj-105296	6	16	türkiye	türkiye	PROPN
esrj-105296	6	17	2	2	NUM
esrj-105296	6	18	.	.	PUNCT
esrj-105296	6	19	remote	remote	ADJ
esrj-105296	6	20	sensing	sensing	NOUN
esrj-105296	6	21	and	and	CCONJ
esrj-105296	6	22	gis	gis	NOUN
esrj-105296	6	23	application	application	NOUN
esrj-105296	6	24	and	and	CCONJ
esrj-105296	6	25	research	research	NOUN
esrj-105296	6	26	center	center	NOUN
esrj-105296	6	27	,	,	PUNCT
esrj-105296	6	28	afyon	afyon	NOUN
esrj-105296	6	29	kocatepe	kocatepe	PROPN
esrj-105296	6	30	university	university	PROPN
esrj-105296	6	31	,	,	PUNCT
esrj-105296	6	32	tr-3200	tr-3200	PROPN
esrj-105296	6	33	,	,	PUNCT
esrj-105296	6	34	afyonkarahisar	afyonkarahisar	PROPN
esrj-105296	6	35	,	,	PUNCT
esrj-105296	6	36	türkiye	türkiye	PROPN
esrj-105296	6	37	*	*	PUNCT
esrj-105296	6	38	corresponding	correspond	VERB
esrj-105296	6	39	author	author	NOUN
esrj-105296	6	40	:	:	PUNCT
esrj-105296	6	41	ecseyrek@aku.edu.tr	ecseyrek@aku.edu.tr	PROPN
esrj-105296	6	42	record	record	PROPN
esrj-105296	6	43	manuscript	manuscript	NOUN
esrj-105296	6	44	received	receive	VERB
esrj-105296	6	45	:	:	PUNCT
esrj-105296	6	46	15/10/2022	15/10/2022	NUM
esrj-105296	6	47	accepted	accept	VERB
esrj-105296	6	48	for	for	ADP
esrj-105296	6	49	publication	publication	NOUN
esrj-105296	6	50	:	:	PUNCT
esrj-105296	6	51	17/07/2024	17/07/2024	NUM
esrj-105296	6	52	how	how	SCONJ
esrj-105296	6	53	to	to	PART
esrj-105296	6	54	cite	cite	VERB
esrj-105296	6	55	item	item	NOUN
esrj-105296	6	56	:	:	PUNCT
esrj-105296	6	57	seyrek	seyrek	PROPN
esrj-105296	6	58	,	,	PUNCT
esrj-105296	6	59	e.	e.	PROPN
esrj-105296	6	60	c.	c.	PROPN
esrj-105296	6	61	,	,	PUNCT
esrj-105296	6	62	&	&	CCONJ
esrj-105296	6	63	uysal	uysal	PROPN
esrj-105296	6	64	,	,	PUNCT
esrj-105296	6	65	m.	m.	NOUN
esrj-105296	6	66	(	(	PUNCT
esrj-105296	6	67	2024	2024	NUM
esrj-105296	6	68	)	)	PUNCT
esrj-105296	6	69	.	.	PUNCT
esrj-105296	7	1	investigation	investigation	NOUN
esrj-105296	7	2	of	of	ADP
esrj-105296	7	3	the	the	DET
esrj-105296	7	4	performances	performance	NOUN
esrj-105296	7	5	of	of	ADP
esrj-105296	7	6	support	support	NOUN
esrj-105296	7	7	vector	vector	NOUN
esrj-105296	7	8	machine	machine	NOUN
esrj-105296	7	9	,	,	PUNCT
esrj-105296	7	10	random	random	ADJ
esrj-105296	7	11	forest	forest	NOUN
esrj-105296	7	12	,	,	PUNCT
esrj-105296	7	13	and	and	CCONJ
esrj-105296	7	14	3d-2d	3d-2d	NUM
esrj-105296	7	15	convolutional	convolutional	ADJ
esrj-105296	7	16	neural	neural	ADJ
esrj-105296	7	17	network	network	NOUN
esrj-105296	7	18	for	for	ADP
esrj-105296	7	19	hyperspectral	hyperspectral	ADJ
esrj-105296	7	20	image	image	NOUN
esrj-105296	7	21	classification	classification	NOUN
esrj-105296	7	22	.	.	PUNCT
esrj-105296	8	1	earth	earth	PROPN
esrj-105296	8	2	sciences	sciences	PROPN
esrj-105296	8	3	research	research	PROPN
esrj-105296	8	4	journal	journal	PROPN
esrj-105296	8	5	,	,	PUNCT
esrj-105296	8	6	28(2	28(2	NUM
esrj-105296	8	7	)	)	PUNCT
esrj-105296	8	8	,	,	PUNCT
esrj-105296	8	9	161	161	NUM
esrj-105296	8	10	-	-	SYM
esrj-105296	8	11	174	174	NUM
esrj-105296	8	12	.	.	PUNCT
esrj-105296	9	1	https://doi	https://doi	X
esrj-105296	9	2	.	.	PUNCT
esrj-105296	9	3	org/10.15446	org/10.15446	NOUN
esrj-105296	9	4	/	/	SYM
esrj-105296	9	5	esrj.v28n2.105296	esrj.v28n2.105296	NOUN
esrj-105296	9	6	classification	classification	NOUN
esrj-105296	9	7	of	of	ADP
esrj-105296	9	8	the	the	DET
esrj-105296	9	9	hyperspectral	hyperspectral	ADJ
esrj-105296	9	10	images	image	NOUN
esrj-105296	9	11	(	(	PUNCT
esrj-105296	9	12	hsis	hsis	NOUN
esrj-105296	9	13	)	)	PUNCT
esrj-105296	9	14	is	be	AUX
esrj-105296	9	15	one	one	NUM
esrj-105296	9	16	of	of	ADP
esrj-105296	9	17	the	the	DET
esrj-105296	9	18	most	most	ADV
esrj-105296	9	19	challenging	challenging	ADJ
esrj-105296	9	20	tasks	task	NOUN
esrj-105296	9	21	hyperspectral	hyperspectral	ADJ
esrj-105296	9	22	remote	remote	ADJ
esrj-105296	9	23	sensing	sensing	NOUN
esrj-105296	9	24	.	.	PUNCT
esrj-105296	10	1	various	various	ADJ
esrj-105296	10	2	machine	machine	NOUN
esrj-105296	10	3	learning	learn	VERB
esrj-105296	10	4	classification	classification	NOUN
esrj-105296	10	5	algorithms	algorithm	NOUN
esrj-105296	10	6	have	have	AUX
esrj-105296	10	7	been	be	AUX
esrj-105296	10	8	implemented	implement	VERB
esrj-105296	10	9	to	to	ADP
esrj-105296	10	10	hsi	hsi	PROPN
esrj-105296	10	11	classification	classification	NOUN
esrj-105296	10	12	.	.	PUNCT
esrj-105296	11	1	in	in	ADP
esrj-105296	11	2	recent	recent	ADJ
esrj-105296	11	3	years	year	NOUN
esrj-105296	11	4	,	,	PUNCT
esrj-105296	11	5	several	several	ADJ
esrj-105296	11	6	convolutional	convolutional	ADJ
esrj-105296	11	7	neural	neural	ADJ
esrj-105296	11	8	network	network	NOUN
esrj-105296	11	9	(	(	PUNCT
esrj-105296	11	10	cnn	cnn	PROPN
esrj-105296	11	11	)	)	PUNCT
esrj-105296	11	12	architectures	architecture	NOUN
esrj-105296	11	13	were	be	AUX
esrj-105296	11	14	developed	develop	VERB
esrj-105296	11	15	for	for	ADP
esrj-105296	11	16	hsi	hsi	PROPN
esrj-105296	11	17	classification	classification	NOUN
esrj-105296	11	18	.	.	PUNCT
esrj-105296	12	1	the	the	DET
esrj-105296	12	2	aim	aim	NOUN
esrj-105296	12	3	of	of	ADP
esrj-105296	12	4	this	this	DET
esrj-105296	12	5	study	study	NOUN
esrj-105296	12	6	is	be	AUX
esrj-105296	12	7	to	to	PART
esrj-105296	12	8	test	test	VERB
esrj-105296	12	9	the	the	DET
esrj-105296	12	10	performance	performance	NOUN
esrj-105296	12	11	of	of	ADP
esrj-105296	12	12	cnn	cnn	PROPN
esrj-105296	12	13	,	,	PUNCT
esrj-105296	12	14	and	and	CCONJ
esrj-105296	12	15	well	well	ADV
esrj-105296	12	16	-	-	PUNCT
esrj-105296	12	17	known	know	VERB
esrj-105296	12	18	support	support	NOUN
esrj-105296	12	19	vector	vector	NOUN
esrj-105296	12	20	machine	machine	NOUN
esrj-105296	12	21	and	and	CCONJ
esrj-105296	12	22	random	random	ADJ
esrj-105296	12	23	forest	forest	NOUN
esrj-105296	12	24	algorithms	algorithm	NOUN
esrj-105296	12	25	using	use	VERB
esrj-105296	12	26	the	the	DET
esrj-105296	12	27	hyrank	hyrank	NOUN
esrj-105296	12	28	loukia	loukia	NOUN
esrj-105296	12	29	,	,	PUNCT
esrj-105296	12	30	houston	houston	PROPN
esrj-105296	12	31	2013	2013	NUM
esrj-105296	12	32	,	,	PUNCT
esrj-105296	12	33	and	and	CCONJ
esrj-105296	12	34	salinas	salinas	PROPN
esrj-105296	12	35	scene	scene	NOUN
esrj-105296	12	36	datasets	dataset	NOUN
esrj-105296	12	37	.	.	PUNCT
esrj-105296	13	1	the	the	DET
esrj-105296	13	2	findings	finding	NOUN
esrj-105296	13	3	indicate	indicate	VERB
esrj-105296	13	4	that	that	SCONJ
esrj-105296	13	5	the	the	DET
esrj-105296	13	6	modified	modify	VERB
esrj-105296	13	7	hybridsn	hybridsn	PROPN
esrj-105296	13	8	cnn	cnn	PROPN
esrj-105296	13	9	outperformed	outperform	VERB
esrj-105296	13	10	other	other	ADJ
esrj-105296	13	11	algorithms	algorithm	NOUN
esrj-105296	13	12	across	across	ADP
esrj-105296	13	13	all	all	DET
esrj-105296	13	14	datasets	dataset	NOUN
esrj-105296	13	15	,	,	PUNCT
esrj-105296	13	16	as	as	SCONJ
esrj-105296	13	17	demonstrated	demonstrate	VERB
esrj-105296	13	18	by	by	ADP
esrj-105296	13	19	various	various	ADJ
esrj-105296	13	20	performance	performance	NOUN
esrj-105296	13	21	evaluation	evaluation	NOUN
esrj-105296	13	22	metrics	metric	NOUN
esrj-105296	13	23	.	.	PUNCT
esrj-105296	14	1	keywords	keyword	NOUN
esrj-105296	14	2	:	:	PUNCT
esrj-105296	14	3	hyperspectral	hyperspectral	ADJ
esrj-105296	14	4	image	image	NOUN
esrj-105296	14	5	classification	classification	NOUN
esrj-105296	14	6	;	;	PUNCT
esrj-105296	14	7	support	support	NOUN
esrj-105296	14	8	vector	vector	NOUN
esrj-105296	14	9	machine	machine	NOUN
esrj-105296	14	10	;	;	PUNCT
esrj-105296	14	11	random	random	ADJ
esrj-105296	14	12	forest	forest	NOUN
esrj-105296	14	13	;	;	PUNCT
esrj-105296	14	14	convolutional	convolutional	ADJ
esrj-105296	14	15	neural	neural	ADJ
esrj-105296	14	16	networks	network	NOUN
esrj-105296	14	17	;	;	PUNCT
esrj-105296	14	18	houston	houston	PROPN
esrj-105296	14	19	2013	2013	NUM
esrj-105296	14	20	;	;	PUNCT
esrj-105296	14	21	hyrank	hyrank	PROPN
esrj-105296	14	22	;	;	PUNCT
esrj-105296	14	23	salinas	salinas	PROPN
esrj-105296	14	24	scene	scene	PROPN
esrj-105296	14	25	;	;	PUNCT
esrj-105296	14	26	investigación	investigación	PROPN
esrj-105296	14	27	sobre	sobre	PROPN
esrj-105296	14	28	el	el	PROPN
esrj-105296	14	29	desempeño	desempeño	PROPN
esrj-105296	14	30	de	de	PROPN
esrj-105296	14	31	máquinas	máquinas	PROPN
esrj-105296	14	32	de	de	PROPN
esrj-105296	14	33	vectores	vectores	PROPN
esrj-105296	14	34	de	de	X
esrj-105296	14	35	soporte	soporte	NOUN
esrj-105296	14	36	,	,	PUNCT
esrj-105296	14	37	bosque	bosque	PROPN
esrj-105296	14	38	aleatorio	aleatorio	PROPN
esrj-105296	14	39	y	y	PROPN
esrj-105296	14	40	redes	redes	PROPN
esrj-105296	14	41	neuronales	neuronale	VERB
esrj-105296	14	42	convolucionales	convolucionale	NOUN
esrj-105296	14	43	3d	3d	NUM
esrj-105296	14	44	y	y	PROPN
esrj-105296	14	45	2d	2d	PROPN
esrj-105296	14	46	en	en	X
esrj-105296	14	47	la	la	PROPN
esrj-105296	14	48	clasificación	clasificación	PROPN
esrj-105296	14	49	de	de	PROPN
esrj-105296	14	50	imágenes	imágenes	PROPN
esrj-105296	14	51	hiperespectrales	hiperespectrales	PROPN
esrj-105296	14	52	resumen	resumen	PROPN
esrj-105296	14	53	la	la	PROPN
esrj-105296	14	54	clasificación	clasificación	PROPN
esrj-105296	14	55	de	de	PROPN
esrj-105296	14	56	imágenes	imágenes	PROPN
esrj-105296	14	57	hiperespectrales	hiperespectrales	PROPN
esrj-105296	14	58	(	(	PUNCT
esrj-105296	14	59	hsi	hsi	PROPN
esrj-105296	14	60	,	,	PUNCT
esrj-105296	14	61	del	del	PROPN
esrj-105296	14	62	inglés	inglés	NOUN
esrj-105296	14	63	hyperspectral	hyperspectral	ADJ
esrj-105296	14	64	images	image	NOUN
esrj-105296	14	65	)	)	PUNCT
esrj-105296	14	66	es	es	X
esrj-105296	14	67	una	una	PROPN
esrj-105296	14	68	de	de	PROPN
esrj-105296	14	69	las	las	PROPN
esrj-105296	14	70	tareas	tareas	PROPN
esrj-105296	14	71	más	más	PROPN
esrj-105296	14	72	complejas	complejas	PROPN
esrj-105296	14	73	de	de	PROPN
esrj-105296	14	74	la	la	PROPN
esrj-105296	14	75	detección	detección	PROPN
esrj-105296	14	76	remota	remota	PROPN
esrj-105296	14	77	hiperespectral	hiperespectral	PROPN
esrj-105296	14	78	.	.	PUNCT
esrj-105296	15	1	varios	varios	PROPN
esrj-105296	15	2	algoritmos	algoritmos	PROPN
esrj-105296	15	3	de	de	PROPN
esrj-105296	15	4	aprendizaje	aprendizaje	PROPN
esrj-105296	15	5	de	de	PROPN
esrj-105296	15	6	máquinas	máquinas	PROPN
esrj-105296	15	7	se	se	PROPN
esrj-105296	15	8	han	han	PROPN
esrj-105296	15	9	implementado	implementado	PROPN
esrj-105296	15	10	en	en	PROPN
esrj-105296	15	11	la	la	PROPN
esrj-105296	15	12	clasificación	clasificación	PROPN
esrj-105296	15	13	de	de	PROPN
esrj-105296	15	14	las	las	PROPN
esrj-105296	15	15	hsi	hsi	PROPN
esrj-105296	15	16	.	.	PUNCT
esrj-105296	16	1	recientemente	recientemente	PROPN
esrj-105296	16	2	,	,	PUNCT
esrj-105296	16	3	varias	varias	PROPN
esrj-105296	16	4	arquitecturas	arquitecturas	PROPN
esrj-105296	16	5	basadas	basadas	PROPN
esrj-105296	16	6	en	en	PROPN
esrj-105296	16	7	redes	redes	PROPN
esrj-105296	16	8	neuronales	neuronale	VERB
esrj-105296	16	9	convolucionales	convolucionale	NOUN
esrj-105296	16	10	(	(	PUNCT
esrj-105296	16	11	cnn	cnn	PROPN
esrj-105296	16	12	,	,	PUNCT
esrj-105296	16	13	del	del	PROPN
esrj-105296	16	14	inglés	inglés	PROPN
esrj-105296	16	15	convolutional	convolutional	ADJ
esrj-105296	16	16	neural	neural	ADJ
esrj-105296	16	17	networks	network	NOUN
esrj-105296	16	18	)	)	PUNCT
esrj-105296	16	19	se	se	PROPN
esrj-105296	16	20	han	han	PROPN
esrj-105296	16	21	desarrollado	desarrollado	PROPN
esrj-105296	16	22	para	para	PROPN
esrj-105296	16	23	esta	esta	PROPN
esrj-105296	16	24	clasificación	clasificación	PROPN
esrj-105296	16	25	de	de	PROPN
esrj-105296	16	26	imágenes	imágenes	PROPN
esrj-105296	16	27	hiperespectrales	hiperespectrales	PROPN
esrj-105296	16	28	.	.	PUNCT
esrj-105296	17	1	el	el	PROPN
esrj-105296	17	2	objetivo	objetivo	PROPN
esrj-105296	17	3	de	de	PROPN
esrj-105296	17	4	este	este	X
esrj-105296	17	5	estudio	estudio	PROPN
esrj-105296	17	6	es	es	PROPN
esrj-105296	17	7	evaluar	evaluar	PROPN
esrj-105296	17	8	el	el	PROPN
esrj-105296	17	9	desempeño	desempeño	PROPN
esrj-105296	17	10	de	de	PROPN
esrj-105296	17	11	las	las	PROPN
esrj-105296	17	12	cnn	cnn	PROPN
esrj-105296	17	13	y	y	PROPN
esrj-105296	17	14	los	los	PROPN
esrj-105296	17	15	algoritmos	algoritmos	PROPN
esrj-105296	17	16	de	de	PROPN
esrj-105296	17	17	máquinas	máquinas	PROPN
esrj-105296	17	18	de	de	PROPN
esrj-105296	17	19	vectores	vectores	PROPN
esrj-105296	17	20	de	de	PROPN
esrj-105296	17	21	soporte	soporte	PROPN
esrj-105296	17	22	y	y	PROPN
esrj-105296	17	23	de	de	PROPN
esrj-105296	17	24	bosque	bosque	PROPN
esrj-105296	17	25	aleatorio	aleatorio	PROPN
esrj-105296	17	26	con	con	PROPN
esrj-105296	17	27	los	los	PROPN
esrj-105296	17	28	conjuntos	conjuntos	PROPN
esrj-105296	17	29	de	de	PROPN
esrj-105296	17	30	datos	datos	PROPN
esrj-105296	17	31	hyrank	hyrank	PROPN
esrj-105296	17	32	loukia	loukia	PROPN
esrj-105296	17	33	,	,	PUNCT
esrj-105296	17	34	houston	houston	PROPN
esrj-105296	17	35	2013	2013	NUM
esrj-105296	17	36	y	y	PROPN
esrj-105296	17	37	salinas	salinas	PROPN
esrj-105296	17	38	scene	scene	PROPN
esrj-105296	17	39	.	.	PUNCT
esrj-105296	18	1	los	los	PROPN
esrj-105296	18	2	resultados	resultados	PROPN
esrj-105296	18	3	demuestran	demuestran	VERB
esrj-105296	18	4	que	que	PROPN
esrj-105296	18	5	el	el	PROPN
esrj-105296	18	6	modelo	modelo	PROPN
esrj-105296	18	7	modified	modify	VERB
esrj-105296	18	8	hybridsn	hybridsn	PROPN
esrj-105296	18	9	cnn	cnn	PROPN
esrj-105296	18	10	superó	superó	VERB
esrj-105296	18	11	a	a	DET
esrj-105296	18	12	otros	otros	NOUN
esrj-105296	18	13	algoritmos	algoritmos	PROPN
esrj-105296	18	14	en	en	PROPN
esrj-105296	18	15	todos	todos	PROPN
esrj-105296	18	16	los	los	PROPN
esrj-105296	18	17	conjuntos	conjuntos	PROPN
esrj-105296	18	18	de	de	X
esrj-105296	18	19	datos	datos	X
esrj-105296	18	20	de	de	PROPN
esrj-105296	18	21	acuerdo	acuerdo	PROPN
esrj-105296	18	22	con	con	PROPN
esrj-105296	18	23	lo	lo	PROPN
esrj-105296	18	24	que	que	PROPN
esrj-105296	18	25	demuestran	demuestran	PROPN
esrj-105296	18	26	varias	varias	PROPN
esrj-105296	18	27	métricas	métricas	PROPN
esrj-105296	18	28	de	de	PROPN
esrj-105296	18	29	evaluación	evaluación	PROPN
esrj-105296	18	30	de	de	X
esrj-105296	18	31	desempeño	desempeño	PROPN
esrj-105296	18	32	.	.	PUNCT
esrj-105296	19	1	palabras	palabras	PROPN
esrj-105296	19	2	clave	clave	PROPN
esrj-105296	19	3	:	:	PUNCT
esrj-105296	19	4	clasificación	clasificación	PROPN
esrj-105296	19	5	de	de	PROPN
esrj-105296	19	6	imágenes	imágenes	PROPN
esrj-105296	19	7	hiperespectrales	hiperespectrales	PROPN
esrj-105296	19	8	;	;	PUNCT
esrj-105296	19	9	máquinas	máquinas	PROPN
esrj-105296	19	10	de	de	PROPN
esrj-105296	19	11	vectores	vectores	PROPN
esrj-105296	19	12	de	de	PROPN
esrj-105296	19	13	soporte	soporte	NOUN
esrj-105296	19	14	;	;	PUNCT
esrj-105296	19	15	bosque	bosque	PROPN
esrj-105296	19	16	aleatorio	aleatorio	PROPN
esrj-105296	19	17	;	;	PUNCT
esrj-105296	19	18	redes	redes	PROPN
esrj-105296	19	19	neuronales	neuronale	VERB
esrj-105296	19	20	convolucionales	convolucionale	NOUN
esrj-105296	19	21	;	;	PUNCT
esrj-105296	19	22	houston	houston	PROPN
esrj-105296	19	23	2013	2013	NUM
esrj-105296	19	24	;	;	PUNCT
esrj-105296	19	25	hyrank	hyrank	NOUN
esrj-105296	19	26	;	;	PUNCT
esrj-105296	19	27	salinas	salinas	PROPN
esrj-105296	19	28	scene	scene	PROPN
esrj-105296	19	29	https://doi.org/10.15446/esrj.v28n2.105296	https://doi.org/10.15446/esrj.v28n2.105296	VERB
esrj-105296	19	30	mailto:ecseyrek@aku.edu.tr	mailto:ecseyrek@aku.edu.tr	PROPN
esrj-105296	19	31	https://doi.org/10.15446/esrj.v28n2.105296	https://doi.org/10.15446/esrj.v28n2.105296	X
esrj-105296	19	32	https://doi.org/10.15446/esrj.v28n2.105296	https://doi.org/10.15446/esrj.v28n2.105296	NUM
esrj-105296	19	33	162	162	NUM
esrj-105296	19	34	eren	eren	PROPN
esrj-105296	19	35	can	can	AUX
esrj-105296	19	36	seyrek	seyrek	VERB
esrj-105296	19	37	,	,	PUNCT
esrj-105296	19	38	murat	murat	PROPN
esrj-105296	19	39	uysal	uysal	ADJ
esrj-105296	19	40	1	1	NUM
esrj-105296	19	41	.	.	PUNCT
esrj-105296	20	1	introduction	introduction	NOUN
esrj-105296	20	2	hyperspectral	hyperspectral	ADJ
esrj-105296	20	3	imaging	imaging	NOUN
esrj-105296	20	4	sensors	sensor	NOUN
esrj-105296	20	5	acquire	acquire	VERB
esrj-105296	20	6	data	datum	NOUN
esrj-105296	20	7	in	in	ADP
esrj-105296	20	8	hundreds	hundred	NOUN
esrj-105296	20	9	of	of	ADP
esrj-105296	20	10	narrow	narrow	ADJ
esrj-105296	20	11	contiguous	contiguous	ADJ
esrj-105296	20	12	spectral	spectral	ADJ
esrj-105296	20	13	bands	band	NOUN
esrj-105296	20	14	along	along	ADP
esrj-105296	20	15	the	the	DET
esrj-105296	20	16	electromagnetic	electromagnetic	ADJ
esrj-105296	20	17	spectrum	spectrum	NOUN
esrj-105296	20	18	.	.	PUNCT
esrj-105296	21	1	hyperspectral	hyperspectral	ADJ
esrj-105296	21	2	images	image	NOUN
esrj-105296	21	3	(	(	PUNCT
esrj-105296	21	4	hsis	hsis	NOUN
esrj-105296	21	5	)	)	PUNCT
esrj-105296	21	6	offer	offer	VERB
esrj-105296	21	7	detailed	detailed	ADJ
esrj-105296	21	8	spectral	spectral	ADJ
esrj-105296	21	9	and	and	CCONJ
esrj-105296	21	10	spatial	spatial	ADJ
esrj-105296	21	11	information	information	NOUN
esrj-105296	21	12	about	about	ADP
esrj-105296	21	13	the	the	DET
esrj-105296	21	14	surface	surface	NOUN
esrj-105296	21	15	of	of	ADP
esrj-105296	21	16	the	the	DET
esrj-105296	21	17	earth	earth	NOUN
esrj-105296	21	18	,	,	PUNCT
esrj-105296	21	19	enabling	enable	VERB
esrj-105296	21	20	precise	precise	ADJ
esrj-105296	21	21	discrimination	discrimination	NOUN
esrj-105296	21	22	of	of	ADP
esrj-105296	21	23	the	the	DET
esrj-105296	21	24	surface	surface	NOUN
esrj-105296	21	25	objects	object	NOUN
esrj-105296	21	26	and	and	CCONJ
esrj-105296	21	27	land	land	NOUN
esrj-105296	21	28	cover	cover	NOUN
esrj-105296	21	29	types	type	NOUN
esrj-105296	21	30	.	.	PUNCT
esrj-105296	22	1	hsis	hsis	NOUN
esrj-105296	22	2	have	have	VERB
esrj-105296	22	3	a	a	DET
esrj-105296	22	4	wide	wide	ADJ
esrj-105296	22	5	range	range	NOUN
esrj-105296	22	6	of	of	ADP
esrj-105296	22	7	applications	application	NOUN
esrj-105296	22	8	,	,	PUNCT
esrj-105296	22	9	such	such	ADJ
esrj-105296	22	10	as	as	ADP
esrj-105296	22	11	land	land	NOUN
esrj-105296	22	12	cover	cover	NOUN
esrj-105296	22	13	classification	classification	NOUN
esrj-105296	22	14	(	(	PUNCT
esrj-105296	22	15	akar	akar	PROPN
esrj-105296	22	16	&	&	CCONJ
esrj-105296	22	17	tunc	tunc	PROPN
esrj-105296	22	18	gormus	gormus	NOUN
esrj-105296	22	19	,	,	PUNCT
esrj-105296	22	20	2021	2021	NUM
esrj-105296	22	21	)	)	PUNCT
esrj-105296	22	22	,	,	PUNCT
esrj-105296	22	23	land	land	NOUN
esrj-105296	22	24	cover	cover	NOUN
esrj-105296	22	25	change	change	NOUN
esrj-105296	22	26	detection	detection	NOUN
esrj-105296	22	27	(	(	PUNCT
esrj-105296	22	28	erturk	erturk	PROPN
esrj-105296	22	29	et	et	PROPN
esrj-105296	22	30	al	al	PROPN
esrj-105296	22	31	.	.	PROPN
esrj-105296	22	32	,	,	PUNCT
esrj-105296	22	33	2015	2015	NUM
esrj-105296	22	34	)	)	PUNCT
esrj-105296	22	35	,	,	PUNCT
esrj-105296	22	36	precision	precision	NOUN
esrj-105296	22	37	agriculture	agriculture	NOUN
esrj-105296	22	38	(	(	PUNCT
esrj-105296	22	39	bhosle	bhosle	PROPN
esrj-105296	22	40	&	&	CCONJ
esrj-105296	22	41	musande	musande	PROPN
esrj-105296	22	42	,	,	PUNCT
esrj-105296	22	43	2020	2020	NUM
esrj-105296	22	44	;	;	PUNCT
esrj-105296	22	45	park	park	NOUN
esrj-105296	22	46	&	&	CCONJ
esrj-105296	22	47	lu	lu	PROPN
esrj-105296	22	48	,	,	PUNCT
esrj-105296	22	49	2015	2015	NUM
esrj-105296	22	50	;	;	PUNCT
esrj-105296	22	51	teke	teke	PROPN
esrj-105296	22	52	et	et	PROPN
esrj-105296	22	53	al	al	PROPN
esrj-105296	22	54	.	.	PROPN
esrj-105296	22	55	,	,	PUNCT
esrj-105296	22	56	2013	2013	NUM
esrj-105296	22	57	)	)	PUNCT
esrj-105296	22	58	,	,	PUNCT
esrj-105296	22	59	forestry	forestry	NOUN
esrj-105296	22	60	(	(	PUNCT
esrj-105296	22	61	adão	adão	PROPN
esrj-105296	22	62	et	et	PROPN
esrj-105296	22	63	al	al	PROPN
esrj-105296	22	64	.	.	PROPN
esrj-105296	22	65	,	,	PUNCT
esrj-105296	22	66	2017	2017	NUM
esrj-105296	22	67	)	)	PUNCT
esrj-105296	22	68	,	,	PUNCT
esrj-105296	22	69	geology	geology	NOUN
esrj-105296	22	70	(	(	PUNCT
esrj-105296	22	71	van	van	PROPN
esrj-105296	22	72	der	der	PROPN
esrj-105296	22	73	meer	meer	PROPN
esrj-105296	22	74	et	et	PROPN
esrj-105296	22	75	al	al	PROPN
esrj-105296	22	76	.	.	PROPN
esrj-105296	22	77	,	,	PUNCT
esrj-105296	22	78	2012	2012	NUM
esrj-105296	22	79	)	)	PUNCT
esrj-105296	22	80	,	,	PUNCT
esrj-105296	22	81	environmental	environmental	ADJ
esrj-105296	22	82	monitoring	monitoring	NOUN
esrj-105296	22	83	(	(	PUNCT
esrj-105296	22	84	stuart	stuart	PROPN
esrj-105296	22	85	et	et	PROPN
esrj-105296	22	86	al	al	PROPN
esrj-105296	22	87	.	.	PROPN
esrj-105296	22	88	,	,	PUNCT
esrj-105296	22	89	2019	2019	NUM
esrj-105296	22	90	)	)	PUNCT
esrj-105296	22	91	,	,	PUNCT
esrj-105296	22	92	urban	urban	ADJ
esrj-105296	22	93	planning	planning	NOUN
esrj-105296	22	94	(	(	PUNCT
esrj-105296	22	95	heiden	heiden	PROPN
esrj-105296	22	96	et	et	PROPN
esrj-105296	22	97	al	al	PROPN
esrj-105296	22	98	.	.	PROPN
esrj-105296	22	99	,	,	PUNCT
esrj-105296	22	100	2012	2012	NUM
esrj-105296	22	101	)	)	PUNCT
esrj-105296	22	102	,	,	PUNCT
esrj-105296	22	103	medical	medical	ADJ
esrj-105296	22	104	imaging	imaging	NOUN
esrj-105296	22	105	(	(	PUNCT
esrj-105296	22	106	lu	lu	PROPN
esrj-105296	22	107	&	&	CCONJ
esrj-105296	22	108	fei	fei	PROPN
esrj-105296	22	109	,	,	PUNCT
esrj-105296	22	110	2014	2014	NUM
esrj-105296	22	111	)	)	PUNCT
esrj-105296	22	112	,	,	PUNCT
esrj-105296	22	113	food	food	NOUN
esrj-105296	22	114	quality	quality	NOUN
esrj-105296	22	115	control	control	NOUN
esrj-105296	22	116	(	(	PUNCT
esrj-105296	22	117	basantia	basantia	NOUN
esrj-105296	22	118	et	et	PROPN
esrj-105296	22	119	al	al	PROPN
esrj-105296	22	120	.	.	PROPN
esrj-105296	22	121	,	,	PUNCT
esrj-105296	22	122	2018	2018	NUM
esrj-105296	22	123	)	)	PUNCT
esrj-105296	22	124	,	,	PUNCT
esrj-105296	22	125	and	and	CCONJ
esrj-105296	22	126	military	military	ADJ
esrj-105296	22	127	defense	defense	NOUN
esrj-105296	22	128	(	(	PUNCT
esrj-105296	22	129	ardouin	ardouin	VERB
esrj-105296	22	130	et	et	PROPN
esrj-105296	22	131	al	al	PROPN
esrj-105296	22	132	.	.	PROPN
esrj-105296	22	133	,	,	PUNCT
esrj-105296	22	134	2007	2007	NUM
esrj-105296	22	135	)	)	PUNCT
esrj-105296	22	136	.	.	PUNCT
esrj-105296	23	1	consequently	consequently	ADV
esrj-105296	23	2	,	,	PUNCT
esrj-105296	23	3	research	research	NOUN
esrj-105296	23	4	focusing	focus	VERB
esrj-105296	23	5	on	on	ADP
esrj-105296	23	6	the	the	DET
esrj-105296	23	7	processing	processing	NOUN
esrj-105296	23	8	and	and	CCONJ
esrj-105296	23	9	analysis	analysis	NOUN
esrj-105296	23	10	of	of	ADP
esrj-105296	23	11	hsi	hsi	PROPN
esrj-105296	23	12	data	data	PROPN
esrj-105296	23	13	has	have	AUX
esrj-105296	23	14	experienced	experience	VERB
esrj-105296	23	15	significant	significant	ADJ
esrj-105296	23	16	growth	growth	NOUN
esrj-105296	23	17	in	in	ADP
esrj-105296	23	18	recent	recent	ADJ
esrj-105296	23	19	years	year	NOUN
esrj-105296	23	20	.	.	PUNCT
esrj-105296	24	1	classification	classification	NOUN
esrj-105296	24	2	is	be	AUX
esrj-105296	24	3	a	a	DET
esrj-105296	24	4	fundamental	fundamental	ADJ
esrj-105296	24	5	and	and	CCONJ
esrj-105296	24	6	crucial	crucial	ADJ
esrj-105296	24	7	technique	technique	NOUN
esrj-105296	24	8	in	in	ADP
esrj-105296	24	9	hsi	hsi	PROPN
esrj-105296	24	10	processing	processing	NOUN
esrj-105296	24	11	,	,	PUNCT
esrj-105296	24	12	which	which	PRON
esrj-105296	24	13	is	be	AUX
esrj-105296	24	14	utilized	utilize	VERB
esrj-105296	24	15	to	to	PART
esrj-105296	24	16	identify	identify	VERB
esrj-105296	24	17	the	the	DET
esrj-105296	24	18	land	land	NOUN
esrj-105296	24	19	category	category	NOUN
esrj-105296	24	20	of	of	ADP
esrj-105296	24	21	each	each	DET
esrj-105296	24	22	pixel	pixel	NOUN
esrj-105296	24	23	in	in	ADP
esrj-105296	24	24	the	the	DET
esrj-105296	24	25	hsi	hsi	PROPN
esrj-105296	24	26	(	(	PUNCT
esrj-105296	24	27	meng	meng	PROPN
esrj-105296	24	28	et	et	PROPN
esrj-105296	24	29	al	al	PROPN
esrj-105296	24	30	.	.	PROPN
esrj-105296	24	31	,	,	PUNCT
esrj-105296	24	32	2021	2021	NUM
esrj-105296	24	33	)	)	PUNCT
esrj-105296	24	34	.	.	PUNCT
esrj-105296	25	1	hsi	hsi	PROPN
esrj-105296	25	2	classification	classification	NOUN
esrj-105296	25	3	is	be	AUX
esrj-105296	25	4	a	a	DET
esrj-105296	25	5	more	more	ADV
esrj-105296	25	6	challenging	challenging	ADJ
esrj-105296	25	7	process	process	NOUN
esrj-105296	25	8	because	because	SCONJ
esrj-105296	25	9	images	image	NOUN
esrj-105296	25	10	have	have	VERB
esrj-105296	25	11	a	a	DET
esrj-105296	25	12	large	large	ADJ
esrj-105296	25	13	number	number	NOUN
esrj-105296	25	14	of	of	ADP
esrj-105296	25	15	spectral	spectral	ADJ
esrj-105296	25	16	bands	band	NOUN
esrj-105296	25	17	and	and	CCONJ
esrj-105296	25	18	have	have	VERB
esrj-105296	25	19	lower	low	ADJ
esrj-105296	25	20	spatial	spatial	ADJ
esrj-105296	25	21	resolution	resolution	NOUN
esrj-105296	25	22	than	than	ADP
esrj-105296	25	23	multispectral	multispectral	ADJ
esrj-105296	25	24	images	image	NOUN
esrj-105296	25	25	.	.	PUNCT
esrj-105296	26	1	the	the	DET
esrj-105296	26	2	supervised	supervised	ADJ
esrj-105296	26	3	classification	classification	NOUN
esrj-105296	26	4	approach	approach	NOUN
esrj-105296	26	5	generates	generate	VERB
esrj-105296	26	6	decision	decision	NOUN
esrj-105296	26	7	boundaries	boundary	NOUN
esrj-105296	26	8	by	by	ADP
esrj-105296	26	9	learning	learn	VERB
esrj-105296	26	10	from	from	ADP
esrj-105296	26	11	training	training	NOUN
esrj-105296	26	12	samples	sample	NOUN
esrj-105296	26	13	(	(	PUNCT
esrj-105296	26	14	i.e.	i.e.	X
esrj-105296	26	15	,	,	PUNCT
esrj-105296	26	16	ground	ground	NOUN
esrj-105296	26	17	truth	truth	NOUN
esrj-105296	26	18	pixels	pixel	NOUN
esrj-105296	26	19	)	)	PUNCT
esrj-105296	26	20	with	with	ADP
esrj-105296	26	21	the	the	DET
esrj-105296	26	22	aim	aim	NOUN
esrj-105296	26	23	of	of	ADP
esrj-105296	26	24	minimizing	minimize	VERB
esrj-105296	26	25	empirical	empirical	ADJ
esrj-105296	26	26	risk	risk	NOUN
esrj-105296	26	27	and	and	CCONJ
esrj-105296	26	28	structural	structural	ADJ
esrj-105296	26	29	risk	risk	NOUN
esrj-105296	26	30	(	(	PUNCT
esrj-105296	26	31	chen	chen	PROPN
esrj-105296	26	32	et	et	PROPN
esrj-105296	26	33	al	al	PROPN
esrj-105296	26	34	.	.	PROPN
esrj-105296	26	35	,	,	PUNCT
esrj-105296	26	36	2020	2020	NUM
esrj-105296	26	37	)	)	PUNCT
esrj-105296	26	38	.	.	PUNCT
esrj-105296	27	1	in	in	ADP
esrj-105296	27	2	the	the	DET
esrj-105296	27	3	literature	literature	NOUN
esrj-105296	27	4	,	,	PUNCT
esrj-105296	27	5	a	a	DET
esrj-105296	27	6	range	range	NOUN
esrj-105296	27	7	of	of	ADP
esrj-105296	27	8	supervised	supervised	ADJ
esrj-105296	27	9	machine	machine	NOUN
esrj-105296	27	10	learning	learning	NOUN
esrj-105296	27	11	(	(	PUNCT
esrj-105296	27	12	ml	ml	NOUN
esrj-105296	27	13	)	)	PUNCT
esrj-105296	27	14	techniques	technique	NOUN
esrj-105296	27	15	have	have	AUX
esrj-105296	27	16	been	be	AUX
esrj-105296	27	17	proposed	propose	VERB
esrj-105296	27	18	for	for	ADP
esrj-105296	27	19	hsi	hsi	PROPN
esrj-105296	27	20	classification	classification	NOUN
esrj-105296	27	21	.	.	PUNCT
esrj-105296	28	1	for	for	ADP
esrj-105296	28	2	instance	instance	NOUN
esrj-105296	28	3	,	,	PUNCT
esrj-105296	28	4	support	support	VERB
esrj-105296	28	5	vector	vector	NOUN
esrj-105296	28	6	machine	machine	NOUN
esrj-105296	28	7	(	(	PUNCT
esrj-105296	28	8	svm	svm	PROPN
esrj-105296	28	9	)	)	PUNCT
esrj-105296	28	10	(	(	PUNCT
esrj-105296	28	11	gualtieri	gualtieri	NOUN
esrj-105296	28	12	et	et	PROPN
esrj-105296	28	13	al	al	PROPN
esrj-105296	28	14	.	.	PROPN
esrj-105296	28	15	,	,	PUNCT
esrj-105296	28	16	1999	1999	NUM
esrj-105296	28	17	;	;	PUNCT
esrj-105296	28	18	kavzoglu	kavzoglu	PROPN
esrj-105296	28	19	&	&	CCONJ
esrj-105296	28	20	colkesen	colkesen	PROPN
esrj-105296	28	21	,	,	PUNCT
esrj-105296	28	22	2009	2009	NUM
esrj-105296	28	23	;	;	PUNCT
esrj-105296	28	24	melgani	melgani	PROPN
esrj-105296	28	25	&	&	CCONJ
esrj-105296	28	26	bruzzone	bruzzone	NOUN
esrj-105296	28	27	,	,	PUNCT
esrj-105296	28	28	2004	2004	NUM
esrj-105296	28	29	)	)	PUNCT
esrj-105296	28	30	,	,	PUNCT
esrj-105296	28	31	random	random	ADJ
esrj-105296	28	32	forest	forest	NOUN
esrj-105296	28	33	(	(	PUNCT
esrj-105296	28	34	rf	rf	NOUN
esrj-105296	28	35	)	)	PUNCT
esrj-105296	28	36	(	(	PUNCT
esrj-105296	28	37	chan	chan	PROPN
esrj-105296	28	38	&	&	CCONJ
esrj-105296	28	39	paelinckx	paelinckx	PROPN
esrj-105296	28	40	,	,	PUNCT
esrj-105296	28	41	2008	2008	NUM
esrj-105296	28	42	;	;	PUNCT
esrj-105296	28	43	waske	waske	PROPN
esrj-105296	28	44	et	et	PROPN
esrj-105296	28	45	al	al	PROPN
esrj-105296	28	46	.	.	PROPN
esrj-105296	28	47	,	,	PUNCT
esrj-105296	28	48	2009	2009	NUM
esrj-105296	28	49	)	)	PUNCT
esrj-105296	28	50	,	,	PUNCT
esrj-105296	28	51	adaboost	adaboost	ADV
esrj-105296	28	52	(	(	PUNCT
esrj-105296	28	53	chan	chan	PROPN
esrj-105296	28	54	&	&	CCONJ
esrj-105296	28	55	paelinckx	paelinckx	PROPN
esrj-105296	28	56	,	,	PUNCT
esrj-105296	28	57	2008	2008	NUM
esrj-105296	28	58	)	)	PUNCT
esrj-105296	28	59	,	,	PUNCT
esrj-105296	28	60	canonical	canonical	ADJ
esrj-105296	28	61	correlation	correlation	NOUN
esrj-105296	28	62	forest	forest	NOUN
esrj-105296	28	63	(	(	PUNCT
esrj-105296	28	64	xia	xia	PROPN
esrj-105296	28	65	et	et	PROPN
esrj-105296	28	66	al	al	PROPN
esrj-105296	28	67	.	.	PROPN
esrj-105296	28	68	,	,	PUNCT
esrj-105296	28	69	2016	2016	NUM
esrj-105296	28	70	)	)	PUNCT
esrj-105296	28	71	extreme	extreme	ADJ
esrj-105296	28	72	gradient	gradient	NOUN
esrj-105296	28	73	boosting	boost	VERB
esrj-105296	28	74	(	(	PUNCT
esrj-105296	28	75	loggenberg	loggenberg	PROPN
esrj-105296	28	76	et	et	PROPN
esrj-105296	28	77	al	al	PROPN
esrj-105296	28	78	.	.	PROPN
esrj-105296	28	79	,	,	PUNCT
esrj-105296	28	80	2018	2018	NUM
esrj-105296	28	81	)	)	PUNCT
esrj-105296	28	82	,	,	PUNCT
esrj-105296	28	83	light	light	ADJ
esrj-105296	28	84	gradient	gradient	NOUN
esrj-105296	28	85	boosting	boost	VERB
esrj-105296	28	86	machines	machine	NOUN
esrj-105296	28	87	(	(	PUNCT
esrj-105296	28	88	ustuner	ustuner	NOUN
esrj-105296	28	89	,	,	PUNCT
esrj-105296	28	90	2024	2024	NUM
esrj-105296	28	91	)	)	PUNCT
esrj-105296	28	92	,	,	PUNCT
esrj-105296	28	93	etc	etc	X
esrj-105296	28	94	.	.	X
esrj-105296	28	95	have	have	AUX
esrj-105296	28	96	been	be	AUX
esrj-105296	28	97	utilized	utilize	VERB
esrj-105296	28	98	for	for	ADP
esrj-105296	28	99	this	this	DET
esrj-105296	28	100	purpose	purpose	NOUN
esrj-105296	28	101	.	.	PUNCT
esrj-105296	29	1	however	however	ADV
esrj-105296	29	2	,	,	PUNCT
esrj-105296	29	3	many	many	ADJ
esrj-105296	29	4	of	of	ADP
esrj-105296	29	5	these	these	DET
esrj-105296	29	6	algorithms	algorithm	NOUN
esrj-105296	29	7	require	require	VERB
esrj-105296	29	8	a	a	DET
esrj-105296	29	9	complex	complex	ADJ
esrj-105296	29	10	and	and	CCONJ
esrj-105296	29	11	precise	precise	ADJ
esrj-105296	29	12	user	user	NOUN
esrj-105296	29	13	-	-	PUNCT
esrj-105296	29	14	defined	define	VERB
esrj-105296	29	15	parameter	parameter	NOUN
esrj-105296	29	16	-	-	PUNCT
esrj-105296	29	17	tuning	tuning	NOUN
esrj-105296	29	18	process	process	NOUN
esrj-105296	29	19	.	.	PUNCT
esrj-105296	30	1	moreover	moreover	ADV
esrj-105296	30	2	,	,	PUNCT
esrj-105296	30	3	they	they	PRON
esrj-105296	30	4	generally	generally	ADV
esrj-105296	30	5	perform	perform	VERB
esrj-105296	30	6	pixel	pixel	ADJ
esrj-105296	30	7	-	-	ADJ
esrj-105296	30	8	wise	wise	ADJ
esrj-105296	30	9	classification	classification	NOUN
esrj-105296	30	10	based	base	VERB
esrj-105296	30	11	solely	solely	ADV
esrj-105296	30	12	on	on	ADP
esrj-105296	30	13	spectral	spectral	ADJ
esrj-105296	30	14	information	information	NOUN
esrj-105296	30	15	,	,	PUNCT
esrj-105296	30	16	neglecting	neglect	VERB
esrj-105296	30	17	the	the	DET
esrj-105296	30	18	spatial	spatial	ADJ
esrj-105296	30	19	relationship	relationship	NOUN
esrj-105296	30	20	between	between	ADP
esrj-105296	30	21	adjacent	adjacent	ADJ
esrj-105296	30	22	pixels	pixel	NOUN
esrj-105296	30	23	in	in	ADP
esrj-105296	30	24	the	the	DET
esrj-105296	30	25	classification	classification	NOUN
esrj-105296	30	26	process	process	NOUN
esrj-105296	30	27	(	(	PUNCT
esrj-105296	30	28	chen	chen	PROPN
esrj-105296	30	29	et	et	PROPN
esrj-105296	30	30	al	al	PROPN
esrj-105296	30	31	.	.	PROPN
esrj-105296	30	32	,	,	PUNCT
esrj-105296	30	33	2020	2020	NUM
esrj-105296	30	34	)	)	PUNCT
esrj-105296	30	35	.	.	PUNCT
esrj-105296	31	1	in	in	ADP
esrj-105296	31	2	recent	recent	ADJ
esrj-105296	31	3	years	year	NOUN
esrj-105296	31	4	,	,	PUNCT
esrj-105296	31	5	deep	deep	ADJ
esrj-105296	31	6	learning	learning	NOUN
esrj-105296	31	7	,	,	PUNCT
esrj-105296	31	8	an	an	DET
esrj-105296	31	9	emerging	emerge	VERB
esrj-105296	31	10	field	field	NOUN
esrj-105296	31	11	within	within	ADP
esrj-105296	31	12	ml	ml	NOUN
esrj-105296	31	13	,	,	PUNCT
esrj-105296	31	14	has	have	AUX
esrj-105296	31	15	grown	grow	VERB
esrj-105296	31	16	in	in	ADP
esrj-105296	31	17	popularity	popularity	NOUN
esrj-105296	31	18	as	as	ADP
esrj-105296	31	19	a	a	DET
esrj-105296	31	20	research	research	NOUN
esrj-105296	31	21	subject	subject	NOUN
esrj-105296	31	22	due	due	ADP
esrj-105296	31	23	to	to	ADP
esrj-105296	31	24	its	its	PRON
esrj-105296	31	25	ability	ability	NOUN
esrj-105296	31	26	to	to	PART
esrj-105296	31	27	extract	extract	VERB
esrj-105296	31	28	different	different	ADJ
esrj-105296	31	29	levels	level	NOUN
esrj-105296	31	30	of	of	ADP
esrj-105296	31	31	features	feature	NOUN
esrj-105296	31	32	from	from	ADP
esrj-105296	31	33	various	various	ADJ
esrj-105296	31	34	types	type	NOUN
esrj-105296	31	35	of	of	ADP
esrj-105296	31	36	datasets	dataset	NOUN
esrj-105296	31	37	,	,	PUNCT
esrj-105296	31	38	including	include	VERB
esrj-105296	31	39	image	image	NOUN
esrj-105296	31	40	,	,	PUNCT
esrj-105296	31	41	video	video	NOUN
esrj-105296	31	42	,	,	PUNCT
esrj-105296	31	43	and	and	CCONJ
esrj-105296	31	44	voice	voice	NOUN
esrj-105296	31	45	.	.	PUNCT
esrj-105296	32	1	convolutional	convolutional	ADJ
esrj-105296	32	2	neural	neural	ADJ
esrj-105296	32	3	networks	network	NOUN
esrj-105296	32	4	(	(	PUNCT
esrj-105296	32	5	cnns	cnns	PROPN
esrj-105296	32	6	)	)	PUNCT
esrj-105296	32	7	are	be	AUX
esrj-105296	32	8	a	a	DET
esrj-105296	32	9	widely	widely	ADV
esrj-105296	32	10	used	use	VERB
esrj-105296	32	11	deep	deep	ADJ
esrj-105296	32	12	learning	learning	NOUN
esrj-105296	32	13	method	method	NOUN
esrj-105296	32	14	for	for	ADP
esrj-105296	32	15	processing	processing	NOUN
esrj-105296	32	16	data	datum	NOUN
esrj-105296	32	17	in	in	ADP
esrj-105296	32	18	multiple	multiple	ADJ
esrj-105296	32	19	arrays	array	NOUN
esrj-105296	32	20	,	,	PUNCT
esrj-105296	32	21	such	such	ADJ
esrj-105296	32	22	as	as	ADP
esrj-105296	32	23	signals	signal	NOUN
esrj-105296	32	24	,	,	PUNCT
esrj-105296	32	25	sequences	sequence	NOUN
esrj-105296	32	26	,	,	PUNCT
esrj-105296	32	27	images	image	NOUN
esrj-105296	32	28	,	,	PUNCT
esrj-105296	32	29	and	and	CCONJ
esrj-105296	32	30	videos	video	NOUN
esrj-105296	32	31	.	.	PUNCT
esrj-105296	33	1	the	the	DET
esrj-105296	33	2	cnns	cnn	NOUN
esrj-105296	33	3	have	have	AUX
esrj-105296	33	4	demonstrated	demonstrate	VERB
esrj-105296	33	5	remarkable	remarkable	ADJ
esrj-105296	33	6	performances	performance	NOUN
esrj-105296	33	7	in	in	ADP
esrj-105296	33	8	tasks	task	NOUN
esrj-105296	33	9	including	include	VERB
esrj-105296	33	10	speech	speech	NOUN
esrj-105296	33	11	recognition	recognition	NOUN
esrj-105296	33	12	,	,	PUNCT
esrj-105296	33	13	face	face	NOUN
esrj-105296	33	14	detection	detection	NOUN
esrj-105296	33	15	,	,	PUNCT
esrj-105296	33	16	object	object	NOUN
esrj-105296	33	17	detection	detection	NOUN
esrj-105296	33	18	,	,	PUNCT
esrj-105296	33	19	and	and	CCONJ
esrj-105296	33	20	image	image	NOUN
esrj-105296	33	21	classification	classification	NOUN
esrj-105296	33	22	(	(	PUNCT
esrj-105296	33	23	akın	akın	PROPN
esrj-105296	33	24	&	&	CCONJ
esrj-105296	33	25	cömert	cömert	PROPN
esrj-105296	33	26	,	,	PUNCT
esrj-105296	33	27	2023	2023	NUM
esrj-105296	33	28	;	;	PUNCT
esrj-105296	34	1	lecun	lecun	PROPN
esrj-105296	34	2	et	et	PROPN
esrj-105296	34	3	al	al	PROPN
esrj-105296	34	4	.	.	PROPN
esrj-105296	34	5	,	,	PUNCT
esrj-105296	34	6	2015	2015	NUM
esrj-105296	34	7	)	)	PUNCT
esrj-105296	34	8	.	.	PUNCT
esrj-105296	35	1	as	as	ADP
esrj-105296	35	2	a	a	DET
esrj-105296	35	3	consequence	consequence	NOUN
esrj-105296	35	4	of	of	ADP
esrj-105296	35	5	their	their	PRON
esrj-105296	35	6	ability	ability	NOUN
esrj-105296	35	7	to	to	PART
esrj-105296	35	8	achieve	achieve	VERB
esrj-105296	35	9	state	state	NOUN
esrj-105296	35	10	-	-	PUNCT
esrj-105296	35	11	of	of	ADP
esrj-105296	35	12	-	-	PUNCT
esrj-105296	35	13	the	the	DET
esrj-105296	35	14	-	-	PUNCT
esrj-105296	35	15	art	art	NOUN
esrj-105296	35	16	performance	performance	NOUN
esrj-105296	35	17	,	,	PUNCT
esrj-105296	35	18	cnns	cnn	NOUN
esrj-105296	35	19	have	have	AUX
esrj-105296	35	20	become	become	VERB
esrj-105296	35	21	increasingly	increasingly	ADV
esrj-105296	35	22	prominent	prominent	ADJ
esrj-105296	35	23	in	in	ADP
esrj-105296	35	24	remote	remote	ADJ
esrj-105296	35	25	sensing	sense	VERB
esrj-105296	35	26	applications	application	NOUN
esrj-105296	35	27	(	(	PUNCT
esrj-105296	35	28	abdikan	abdikan	PROPN
esrj-105296	35	29	et	et	PROPN
esrj-105296	35	30	al	al	PROPN
esrj-105296	35	31	.	.	PROPN
esrj-105296	35	32	,	,	PUNCT
esrj-105296	35	33	2023	2023	NUM
esrj-105296	35	34	;	;	PUNCT
esrj-105296	35	35	cheng	cheng	PROPN
esrj-105296	35	36	et	et	PROPN
esrj-105296	35	37	al	al	PROPN
esrj-105296	35	38	.	.	PROPN
esrj-105296	35	39	,	,	PUNCT
esrj-105296	35	40	2022	2022	NUM
esrj-105296	35	41	;	;	PUNCT
esrj-105296	35	42	ghanbari	ghanbari	PROPN
esrj-105296	35	43	et	et	PROPN
esrj-105296	35	44	al	al	PROPN
esrj-105296	35	45	.	.	PROPN
esrj-105296	35	46	,	,	PUNCT
esrj-105296	35	47	2021	2021	NUM
esrj-105296	35	48	)	)	PUNCT
esrj-105296	35	49	.	.	PUNCT
esrj-105296	36	1	notably	notably	ADV
esrj-105296	36	2	,	,	PUNCT
esrj-105296	36	3	a	a	DET
esrj-105296	36	4	variety	variety	NOUN
esrj-105296	36	5	of	of	ADP
esrj-105296	36	6	cnn	cnn	PROPN
esrj-105296	36	7	architectures	architecture	NOUN
esrj-105296	36	8	have	have	AUX
esrj-105296	36	9	been	be	AUX
esrj-105296	36	10	developed	develop	VERB
esrj-105296	36	11	specifically	specifically	ADV
esrj-105296	36	12	for	for	ADP
esrj-105296	36	13	the	the	DET
esrj-105296	36	14	classification	classification	NOUN
esrj-105296	36	15	of	of	ADP
esrj-105296	36	16	hsis	hsis	NOUN
esrj-105296	36	17	(	(	PUNCT
esrj-105296	36	18	chen	chen	PROPN
esrj-105296	36	19	et	et	PROPN
esrj-105296	36	20	al	al	PROPN
esrj-105296	36	21	.	.	PROPN
esrj-105296	36	22	,	,	PUNCT
esrj-105296	36	23	2020	2020	NUM
esrj-105296	36	24	;	;	PUNCT
esrj-105296	36	25	chen	chen	PROPN
esrj-105296	36	26	et	et	PROPN
esrj-105296	36	27	al	al	PROPN
esrj-105296	36	28	.	.	PROPN
esrj-105296	36	29	,	,	PUNCT
esrj-105296	36	30	2016	2016	NUM
esrj-105296	36	31	;	;	PUNCT
esrj-105296	36	32	fırat	fırat	PROPN
esrj-105296	36	33	et	et	PROPN
esrj-105296	36	34	al	al	PROPN
esrj-105296	36	35	.	.	PROPN
esrj-105296	36	36	,	,	PUNCT
esrj-105296	36	37	2022	2022	NUM
esrj-105296	36	38	;	;	PUNCT
esrj-105296	36	39	hang	hang	VERB
esrj-105296	36	40	et	et	PROPN
esrj-105296	36	41	al	al	PROPN
esrj-105296	36	42	.	.	PROPN
esrj-105296	36	43	,	,	PUNCT
esrj-105296	36	44	2020	2020	NUM
esrj-105296	36	45	;	;	PUNCT
esrj-105296	36	46	luo	luo	PROPN
esrj-105296	36	47	et	et	PROPN
esrj-105296	36	48	al	al	PROPN
esrj-105296	36	49	.	.	PROPN
esrj-105296	36	50	,	,	PUNCT
esrj-105296	36	51	2018	2018	NUM
esrj-105296	36	52	;	;	PUNCT
esrj-105296	36	53	roy	roy	PROPN
esrj-105296	36	54	et	et	PROPN
esrj-105296	36	55	al	al	PROPN
esrj-105296	36	56	.	.	PROPN
esrj-105296	36	57	,	,	PUNCT
esrj-105296	36	58	2019	2019	NUM
esrj-105296	36	59	)	)	PUNCT
esrj-105296	36	60	.	.	PUNCT
esrj-105296	37	1	however	however	ADV
esrj-105296	37	2	,	,	PUNCT
esrj-105296	37	3	there	there	PRON
esrj-105296	37	4	is	be	VERB
esrj-105296	37	5	a	a	DET
esrj-105296	37	6	lack	lack	NOUN
esrj-105296	37	7	of	of	ADP
esrj-105296	37	8	publicly	publicly	ADV
esrj-105296	37	9	available	available	ADJ
esrj-105296	37	10	hsi	hsi	PROPN
esrj-105296	37	11	datasets	dataset	NOUN
esrj-105296	37	12	including	include	VERB
esrj-105296	37	13	ground	ground	NOUN
esrj-105296	37	14	truth	truth	NOUN
esrj-105296	37	15	data	datum	NOUN
esrj-105296	37	16	.	.	PUNCT
esrj-105296	38	1	many	many	ADJ
esrj-105296	38	2	studies	study	NOUN
esrj-105296	38	3	within	within	ADP
esrj-105296	38	4	the	the	DET
esrj-105296	38	5	literature	literature	NOUN
esrj-105296	38	6	have	have	AUX
esrj-105296	38	7	relied	rely	VERB
esrj-105296	38	8	upon	upon	SCONJ
esrj-105296	38	9	commonly	commonly	ADV
esrj-105296	38	10	used	use	VERB
esrj-105296	38	11	datasets	dataset	NOUN
esrj-105296	38	12	such	such	ADJ
esrj-105296	38	13	as	as	ADP
esrj-105296	38	14	indian	indian	ADJ
esrj-105296	38	15	pines	pine	NOUN
esrj-105296	38	16	,	,	PUNCT
esrj-105296	38	17	salinas	salinas	PROPN
esrj-105296	38	18	scene	scene	PROPN
esrj-105296	38	19	,	,	PUNCT
esrj-105296	38	20	university	university	NOUN
esrj-105296	38	21	of	of	ADP
esrj-105296	38	22	pavia	pavia	PROPN
esrj-105296	38	23	,	,	PUNCT
esrj-105296	38	24	and	and	CCONJ
esrj-105296	38	25	kennedy	kennedy	PROPN
esrj-105296	38	26	space	space	PROPN
esrj-105296	38	27	center	center	PROPN
esrj-105296	38	28	hsi	hsi	PROPN
esrj-105296	38	29	datasets	dataset	NOUN
esrj-105296	38	30	to	to	PART
esrj-105296	38	31	evaluate	evaluate	VERB
esrj-105296	38	32	the	the	DET
esrj-105296	38	33	performances	performance	NOUN
esrj-105296	38	34	of	of	ADP
esrj-105296	38	35	the	the	DET
esrj-105296	38	36	classification	classification	NOUN
esrj-105296	38	37	algorithms	algorithm	NOUN
esrj-105296	38	38	.	.	PUNCT
esrj-105296	39	1	in	in	ADP
esrj-105296	39	2	recent	recent	ADJ
esrj-105296	39	3	years	year	NOUN
esrj-105296	39	4	,	,	PUNCT
esrj-105296	39	5	several	several	ADJ
esrj-105296	39	6	novel	novel	NOUN
esrj-105296	39	7	hsi	hsi	PROPN
esrj-105296	39	8	datasets	dataset	NOUN
esrj-105296	39	9	were	be	AUX
esrj-105296	39	10	introduced	introduce	VERB
esrj-105296	39	11	,	,	PUNCT
esrj-105296	39	12	such	such	ADJ
esrj-105296	39	13	as	as	ADP
esrj-105296	39	14	houston	houston	PROPN
esrj-105296	39	15	2013	2013	NUM
esrj-105296	39	16	,	,	PUNCT
esrj-105296	39	17	houston	houston	PROPN
esrj-105296	39	18	2018	2018	NUM
esrj-105296	39	19	,	,	PUNCT
esrj-105296	39	20	hyrank	hyrank	PROPN
esrj-105296	39	21	(	(	PUNCT
esrj-105296	39	22	karantzalos	karantzalo	NOUN
esrj-105296	39	23	et	et	PROPN
esrj-105296	39	24	al	al	PROPN
esrj-105296	39	25	.	.	PROPN
esrj-105296	39	26	2018	2018	NUM
esrj-105296	39	27	)	)	PUNCT
esrj-105296	39	28	,	,	PUNCT
esrj-105296	39	29	and	and	CCONJ
esrj-105296	39	30	whu	whu	PROPN
esrj-105296	39	31	-	-	PUNCT
esrj-105296	39	32	hi	hi	INTJ
esrj-105296	39	33	(	(	PUNCT
esrj-105296	39	34	zhong	zhong	PROPN
esrj-105296	39	35	et	et	PROPN
esrj-105296	39	36	al	al	PROPN
esrj-105296	39	37	.	.	PROPN
esrj-105296	39	38	,	,	PUNCT
esrj-105296	39	39	2020	2020	NUM
esrj-105296	39	40	)	)	PUNCT
esrj-105296	39	41	.	.	PUNCT
esrj-105296	40	1	in	in	ADP
esrj-105296	40	2	light	light	NOUN
esrj-105296	40	3	of	of	ADP
esrj-105296	40	4	these	these	DET
esrj-105296	40	5	advancements	advancement	NOUN
esrj-105296	40	6	,	,	PUNCT
esrj-105296	40	7	conducting	conduct	VERB
esrj-105296	40	8	algorithm	algorithm	NOUN
esrj-105296	40	9	evaluations	evaluation	NOUN
esrj-105296	40	10	on	on	ADP
esrj-105296	40	11	recently	recently	ADV
esrj-105296	40	12	introduced	introduce	VERB
esrj-105296	40	13	benchmark	benchmark	NOUN
esrj-105296	40	14	hsi	hsi	PROPN
esrj-105296	40	15	datasets	dataset	NOUN
esrj-105296	40	16	allows	allow	VERB
esrj-105296	40	17	for	for	ADP
esrj-105296	40	18	a	a	DET
esrj-105296	40	19	more	more	ADV
esrj-105296	40	20	comprehensive	comprehensive	ADJ
esrj-105296	40	21	and	and	CCONJ
esrj-105296	40	22	impartial	impartial	ADJ
esrj-105296	40	23	examination	examination	NOUN
esrj-105296	40	24	of	of	ADP
esrj-105296	40	25	the	the	DET
esrj-105296	40	26	tests	test	NOUN
esrj-105296	40	27	within	within	ADP
esrj-105296	40	28	a	a	DET
esrj-105296	40	29	broader	broad	ADJ
esrj-105296	40	30	context	context	NOUN
esrj-105296	40	31	.	.	PUNCT
esrj-105296	41	1	this	this	DET
esrj-105296	41	2	study	study	NOUN
esrj-105296	41	3	evaluated	evaluate	VERB
esrj-105296	41	4	the	the	DET
esrj-105296	41	5	performance	performance	NOUN
esrj-105296	41	6	of	of	ADP
esrj-105296	41	7	the	the	DET
esrj-105296	41	8	various	various	ADJ
esrj-105296	41	9	ml	ml	NOUN
esrj-105296	41	10	algorithms	algorithm	NOUN
esrj-105296	41	11	using	use	VERB
esrj-105296	41	12	a	a	DET
esrj-105296	41	13	variety	variety	NOUN
esrj-105296	41	14	of	of	ADP
esrj-105296	41	15	hsi	hsi	PROPN
esrj-105296	41	16	datasets	dataset	NOUN
esrj-105296	41	17	.	.	PUNCT
esrj-105296	42	1	traditional	traditional	ADJ
esrj-105296	42	2	ml	ml	NOUN
esrj-105296	42	3	classifiers	classifier	NOUN
esrj-105296	42	4	,	,	PUNCT
esrj-105296	42	5	such	such	ADJ
esrj-105296	42	6	as	as	ADP
esrj-105296	42	7	svm	svm	PROPN
esrj-105296	42	8	and	and	CCONJ
esrj-105296	42	9	rf	rf	NOUN
esrj-105296	42	10	,	,	PUNCT
esrj-105296	42	11	were	be	AUX
esrj-105296	42	12	compared	compare	VERB
esrj-105296	42	13	with	with	ADP
esrj-105296	42	14	a	a	DET
esrj-105296	42	15	deep	deep	ADJ
esrj-105296	42	16	learning	learning	NOUN
esrj-105296	42	17	approach	approach	NOUN
esrj-105296	42	18	represented	represent	VERB
esrj-105296	42	19	by	by	ADP
esrj-105296	42	20	cnn	cnn	PROPN
esrj-105296	42	21	.	.	PUNCT
esrj-105296	43	1	hybridsn	hybridsn	PROPN
esrj-105296	43	2	cnn	cnn	PROPN
esrj-105296	43	3	(	(	PUNCT
esrj-105296	43	4	roy	roy	PROPN
esrj-105296	43	5	et	et	PROPN
esrj-105296	43	6	al	al	PROPN
esrj-105296	43	7	.	.	PROPN
esrj-105296	43	8	,	,	PUNCT
esrj-105296	43	9	2019	2019	NUM
esrj-105296	43	10	)	)	PUNCT
esrj-105296	43	11	was	be	AUX
esrj-105296	43	12	chosen	choose	VERB
esrj-105296	43	13	as	as	ADP
esrj-105296	43	14	the	the	DET
esrj-105296	43	15	cnn	cnn	PROPN
esrj-105296	43	16	architecture	architecture	NOUN
esrj-105296	43	17	,	,	PUNCT
esrj-105296	43	18	but	but	CCONJ
esrj-105296	43	19	in	in	ADP
esrj-105296	43	20	light	light	NOUN
esrj-105296	43	21	of	of	ADP
esrj-105296	43	22	literature	literature	NOUN
esrj-105296	43	23	suggesting	suggest	VERB
esrj-105296	43	24	that	that	SCONJ
esrj-105296	43	25	employing	employ	VERB
esrj-105296	43	26	various	various	ADJ
esrj-105296	43	27	activation	activation	NOUN
esrj-105296	43	28	functions	function	NOUN
esrj-105296	43	29	and	and	CCONJ
esrj-105296	43	30	optimizers	optimizer	NOUN
esrj-105296	43	31	can	can	AUX
esrj-105296	43	32	enhance	enhance	VERB
esrj-105296	43	33	classification	classification	NOUN
esrj-105296	43	34	performance	performance	NOUN
esrj-105296	43	35	(	(	PUNCT
esrj-105296	43	36	agarwal	agarwal	PROPN
esrj-105296	43	37	et	et	PROPN
esrj-105296	43	38	al	al	PROPN
esrj-105296	43	39	.	.	PROPN
esrj-105296	43	40	,	,	PUNCT
esrj-105296	43	41	2021	2021	NUM
esrj-105296	43	42	;	;	PUNCT
esrj-105296	43	43	bera	bera	NOUN
esrj-105296	43	44	&	&	CCONJ
esrj-105296	43	45	shrivastava	shrivastava	PROPN
esrj-105296	43	46	,	,	PUNCT
esrj-105296	43	47	2020	2020	NUM
esrj-105296	43	48	;	;	PUNCT
esrj-105296	43	49	dubey	dubey	PROPN
esrj-105296	43	50	et	et	PROPN
esrj-105296	43	51	al	al	PROPN
esrj-105296	43	52	.	.	PROPN
esrj-105296	43	53	,	,	PUNCT
esrj-105296	43	54	2022	2022	NUM
esrj-105296	43	55	;	;	PUNCT
esrj-105296	43	56	hao	hao	PROPN
esrj-105296	43	57	et	et	PROPN
esrj-105296	43	58	al	al	PROPN
esrj-105296	43	59	.	.	PROPN
esrj-105296	43	60	,	,	PUNCT
esrj-105296	43	61	2020	2020	NUM
esrj-105296	43	62	;	;	PUNCT
esrj-105296	43	63	seyrek	seyrek	NOUN
esrj-105296	43	64	&	&	CCONJ
esrj-105296	43	65	uysal	uysal	PROPN
esrj-105296	43	66	,	,	PUNCT
esrj-105296	43	67	2024	2024	NUM
esrj-105296	43	68	;	;	PUNCT
esrj-105296	43	69	vani	vani	PROPN
esrj-105296	43	70	&	&	CCONJ
esrj-105296	43	71	rao	rao	PROPN
esrj-105296	43	72	,	,	PUNCT
esrj-105296	43	73	2019	2019	NUM
esrj-105296	43	74	)	)	PUNCT
esrj-105296	43	75	,	,	PUNCT
esrj-105296	43	76	a	a	DET
esrj-105296	43	77	modified	modify	VERB
esrj-105296	43	78	version	version	NOUN
esrj-105296	43	79	called	call	VERB
esrj-105296	43	80	modified	modified	ADJ
esrj-105296	43	81	hybridsn	hybridsn	NOUN
esrj-105296	43	82	was	be	AUX
esrj-105296	43	83	developed	develop	VERB
esrj-105296	43	84	,	,	PUNCT
esrj-105296	43	85	integrating	integrate	VERB
esrj-105296	43	86	mish	mish	ADJ
esrj-105296	43	87	activation	activation	NOUN
esrj-105296	43	88	function	function	NOUN
esrj-105296	43	89	(	(	PUNCT
esrj-105296	43	90	misra	misra	ADJ
esrj-105296	43	91	,	,	PUNCT
esrj-105296	43	92	2019	2019	NUM
esrj-105296	43	93	)	)	PUNCT
esrj-105296	43	94	and	and	CCONJ
esrj-105296	43	95	adamax	adamax	NOUN
esrj-105296	43	96	optimizer	optimizer	NOUN
esrj-105296	43	97	(	(	PUNCT
esrj-105296	43	98	kingma	kingma	PROPN
esrj-105296	43	99	&	&	CCONJ
esrj-105296	43	100	ba	ba	PROPN
esrj-105296	43	101	,	,	PUNCT
esrj-105296	43	102	2014	2014	NUM
esrj-105296	43	103	)	)	PUNCT
esrj-105296	43	104	.	.	PUNCT
esrj-105296	44	1	the	the	DET
esrj-105296	44	2	hyrank	hyrank	PROPN
esrj-105296	44	3	loukia	loukia	NOUN
esrj-105296	44	4	,	,	PUNCT
esrj-105296	44	5	houston	houston	PROPN
esrj-105296	44	6	2013	2013	NUM
esrj-105296	44	7	,	,	PUNCT
esrj-105296	44	8	and	and	CCONJ
esrj-105296	44	9	salinas	salinas	PROPN
esrj-105296	44	10	scene	scene	NOUN
esrj-105296	44	11	datasets	dataset	NOUN
esrj-105296	44	12	were	be	AUX
esrj-105296	44	13	employed	employ	VERB
esrj-105296	44	14	for	for	ADP
esrj-105296	44	15	testing	test	VERB
esrj-105296	44	16	the	the	DET
esrj-105296	44	17	classification	classification	NOUN
esrj-105296	44	18	performances	performance	NOUN
esrj-105296	44	19	of	of	ADP
esrj-105296	44	20	the	the	DET
esrj-105296	44	21	algorithms	algorithm	NOUN
esrj-105296	44	22	to	to	PART
esrj-105296	44	23	provide	provide	VERB
esrj-105296	44	24	diversity	diversity	NOUN
esrj-105296	44	25	in	in	ADP
esrj-105296	44	26	terms	term	NOUN
esrj-105296	44	27	of	of	ADP
esrj-105296	44	28	spectral	spectral	ADJ
esrj-105296	44	29	and	and	CCONJ
esrj-105296	44	30	spatial	spatial	ADJ
esrj-105296	44	31	resolution	resolution	NOUN
esrj-105296	44	32	of	of	ADP
esrj-105296	44	33	the	the	DET
esrj-105296	44	34	data	datum	NOUN
esrj-105296	44	35	.	.	PUNCT
esrj-105296	45	1	to	to	PART
esrj-105296	45	2	mitigate	mitigate	VERB
esrj-105296	45	3	the	the	DET
esrj-105296	45	4	hughes	hughes	NOUN
esrj-105296	45	5	phenomena	phenomena	PROPN
esrj-105296	45	6	resulting	result	VERB
esrj-105296	45	7	from	from	ADP
esrj-105296	45	8	limited	limited	ADJ
esrj-105296	45	9	ground	ground	NOUN
esrj-105296	45	10	truth	truth	NOUN
esrj-105296	45	11	data	datum	NOUN
esrj-105296	45	12	and	and	CCONJ
esrj-105296	45	13	high	high	ADJ
esrj-105296	45	14	spectral	spectral	ADJ
esrj-105296	45	15	band	band	NOUN
esrj-105296	45	16	correlation	correlation	NOUN
esrj-105296	45	17	between	between	ADP
esrj-105296	45	18	within	within	ADP
esrj-105296	45	19	the	the	DET
esrj-105296	45	20	hsi	hsi	PROPN
esrj-105296	45	21	datasets	dataset	NOUN
esrj-105296	45	22	,	,	PUNCT
esrj-105296	45	23	principal	principal	ADJ
esrj-105296	45	24	component	component	NOUN
esrj-105296	45	25	analysis	analysis	NOUN
esrj-105296	45	26	(	(	PUNCT
esrj-105296	45	27	pca	pca	NOUN
esrj-105296	45	28	)	)	PUNCT
esrj-105296	45	29	transformation	transformation	NOUN
esrj-105296	45	30	was	be	AUX
esrj-105296	45	31	applied	apply	VERB
esrj-105296	45	32	during	during	ADP
esrj-105296	45	33	the	the	DET
esrj-105296	45	34	data	data	NOUN
esrj-105296	45	35	preparation	preparation	NOUN
esrj-105296	45	36	stage	stage	NOUN
esrj-105296	45	37	.	.	PUNCT
esrj-105296	46	1	therefore	therefore	ADV
esrj-105296	46	2	,	,	PUNCT
esrj-105296	46	3	the	the	DET
esrj-105296	46	4	classification	classification	NOUN
esrj-105296	46	5	performance	performance	NOUN
esrj-105296	46	6	of	of	ADP
esrj-105296	46	7	the	the	DET
esrj-105296	46	8	algorithms	algorithm	NOUN
esrj-105296	46	9	was	be	AUX
esrj-105296	46	10	investigated	investigate	VERB
esrj-105296	46	11	without	without	ADP
esrj-105296	46	12	employing	employ	VERB
esrj-105296	46	13	any	any	DET
esrj-105296	46	14	preprocessing	preprocessing	NOUN
esrj-105296	46	15	steps	step	NOUN
esrj-105296	46	16	apart	apart	ADV
esrj-105296	46	17	from	from	ADP
esrj-105296	46	18	pca	pca	PROPN
esrj-105296	46	19	transformation	transformation	NOUN
esrj-105296	46	20	for	for	ADP
esrj-105296	46	21	dimensionality	dimensionality	NOUN
esrj-105296	46	22	reduction	reduction	NOUN
esrj-105296	46	23	to	to	ADP
esrj-105296	46	24	hsi	hsi	PROPN
esrj-105296	46	25	datasets	dataset	NOUN
esrj-105296	46	26	.	.	PUNCT
esrj-105296	47	1	to	to	PART
esrj-105296	47	2	ensure	ensure	VERB
esrj-105296	47	3	consistent	consistent	ADJ
esrj-105296	47	4	evaluation	evaluation	NOUN
esrj-105296	47	5	conditions	condition	NOUN
esrj-105296	47	6	across	across	ADP
esrj-105296	47	7	all	all	DET
esrj-105296	47	8	algorithms	algorithm	NOUN
esrj-105296	47	9	,	,	PUNCT
esrj-105296	47	10	the	the	DET
esrj-105296	47	11	data	datum	NOUN
esrj-105296	47	12	were	be	AUX
esrj-105296	47	13	split	split	VERB
esrj-105296	47	14	into	into	ADP
esrj-105296	47	15	training	training	NOUN
esrj-105296	47	16	and	and	CCONJ
esrj-105296	47	17	testing	testing	NOUN
esrj-105296	47	18	sets	set	NOUN
esrj-105296	47	19	with	with	ADP
esrj-105296	47	20	a	a	DET
esrj-105296	47	21	ratio	ratio	NOUN
esrj-105296	47	22	of	of	ADP
esrj-105296	47	23	10	10	NUM
esrj-105296	47	24	%	%	NOUN
esrj-105296	47	25	for	for	ADP
esrj-105296	47	26	training	training	NOUN
esrj-105296	47	27	and	and	CCONJ
esrj-105296	47	28	90	90	NUM
esrj-105296	47	29	%	%	NOUN
esrj-105296	47	30	for	for	ADP
esrj-105296	47	31	testing	testing	NOUN
esrj-105296	47	32	.	.	PUNCT
esrj-105296	48	1	the	the	DET
esrj-105296	48	2	performance	performance	NOUN
esrj-105296	48	3	of	of	ADP
esrj-105296	48	4	the	the	DET
esrj-105296	48	5	algorithms	algorithm	NOUN
esrj-105296	48	6	was	be	AUX
esrj-105296	48	7	assessed	assess	VERB
esrj-105296	48	8	by	by	ADP
esrj-105296	48	9	comparing	compare	VERB
esrj-105296	48	10	metrics	metric	NOUN
esrj-105296	48	11	such	such	ADJ
esrj-105296	48	12	as	as	ADP
esrj-105296	48	13	overall	overall	ADJ
esrj-105296	48	14	accuracy	accuracy	NOUN
esrj-105296	48	15	(	(	PUNCT
esrj-105296	48	16	oa	oa	INTJ
esrj-105296	48	17	)	)	PUNCT
esrj-105296	48	18	,	,	PUNCT
esrj-105296	48	19	kappa	kappa	PROPN
esrj-105296	48	20	coefficient	coefficient	NOUN
esrj-105296	48	21	(	(	PUNCT
esrj-105296	48	22	κ	κ	NOUN
esrj-105296	48	23	)	)	PUNCT
esrj-105296	48	24	,	,	PUNCT
esrj-105296	48	25	producer	producer	NOUN
esrj-105296	48	26	accuracy	accuracy	NOUN
esrj-105296	48	27	(	(	PUNCT
esrj-105296	48	28	pa	pa	PROPN
esrj-105296	48	29	)	)	PUNCT
esrj-105296	48	30	,	,	PUNCT
esrj-105296	48	31	user	user	NOUN
esrj-105296	48	32	accuracy	accuracy	NOUN
esrj-105296	48	33	(	(	PUNCT
esrj-105296	48	34	ua	ua	NOUN
esrj-105296	48	35	)	)	PUNCT
esrj-105296	48	36	,	,	PUNCT
esrj-105296	48	37	and	and	CCONJ
esrj-105296	48	38	f	f	PROPN
esrj-105296	48	39	score	score	NOUN
esrj-105296	48	40	,	,	PUNCT
esrj-105296	48	41	respectively	respectively	ADV
esrj-105296	48	42	.	.	PUNCT
esrj-105296	49	1	in	in	ADP
esrj-105296	49	2	addition	addition	NOUN
esrj-105296	49	3	,	,	PUNCT
esrj-105296	49	4	differences	difference	NOUN
esrj-105296	49	5	in	in	ADP
esrj-105296	49	6	performances	performance	NOUN
esrj-105296	49	7	between	between	ADP
esrj-105296	49	8	algorithms	algorithm	NOUN
esrj-105296	49	9	were	be	AUX
esrj-105296	49	10	analyzed	analyze	VERB
esrj-105296	49	11	by	by	ADP
esrj-105296	49	12	performing	perform	VERB
esrj-105296	49	13	mcnemar	mcnemar	NOUN
esrj-105296	49	14	’s	’s	PART
esrj-105296	49	15	test	test	NOUN
esrj-105296	49	16	.	.	PUNCT
esrj-105296	50	1	2	2	X
esrj-105296	50	2	.	.	X
esrj-105296	50	3	machine	machine	NOUN
esrj-105296	50	4	learning	learning	NOUN
esrj-105296	50	5	algorithms	algorithms	ADJ
esrj-105296	50	6	2.1	2.1	NUM
esrj-105296	50	7	.	.	PUNCT
esrj-105296	51	1	support	support	NOUN
esrj-105296	51	2	vector	vector	NOUN
esrj-105296	51	3	machines	machine	NOUN
esrj-105296	51	4	(	(	PUNCT
esrj-105296	51	5	svm	svm	ADJ
esrj-105296	51	6	)	)	PUNCT
esrj-105296	51	7	svm	svm	NOUN
esrj-105296	51	8	is	be	AUX
esrj-105296	51	9	a	a	DET
esrj-105296	51	10	well	well	ADV
esrj-105296	51	11	-	-	PUNCT
esrj-105296	51	12	known	know	VERB
esrj-105296	51	13	non	non	ADJ
esrj-105296	51	14	-	-	ADJ
esrj-105296	51	15	parametric	parametric	ADJ
esrj-105296	51	16	and	and	CCONJ
esrj-105296	51	17	supervised	supervise	VERB
esrj-105296	51	18	ml	ml	NUM
esrj-105296	51	19	algorithm	algorithm	NOUN
esrj-105296	51	20	based	base	VERB
esrj-105296	51	21	on	on	ADP
esrj-105296	51	22	statistical	statistical	ADJ
esrj-105296	51	23	learning	learning	NOUN
esrj-105296	51	24	theory	theory	NOUN
esrj-105296	51	25	,	,	PUNCT
esrj-105296	51	26	developed	develop	VERB
esrj-105296	51	27	by	by	ADP
esrj-105296	51	28	vapnik	vapnik	X
esrj-105296	51	29	(	(	PUNCT
esrj-105296	51	30	cortes	corte	NOUN
esrj-105296	51	31	&	&	CCONJ
esrj-105296	51	32	vapnik	vapnik	X
esrj-105296	51	33	,	,	PUNCT
esrj-105296	51	34	1995	1995	NUM
esrj-105296	51	35	)	)	PUNCT
esrj-105296	51	36	.	.	PUNCT
esrj-105296	52	1	the	the	DET
esrj-105296	52	2	algorithm	algorithm	NOUN
esrj-105296	52	3	is	be	AUX
esrj-105296	52	4	based	base	VERB
esrj-105296	52	5	on	on	ADP
esrj-105296	52	6	the	the	DET
esrj-105296	52	7	principle	principle	NOUN
esrj-105296	52	8	of	of	ADP
esrj-105296	52	9	determining	determine	VERB
esrj-105296	52	10	the	the	DET
esrj-105296	52	11	hyperplane	hyperplane	NOUN
esrj-105296	52	12	that	that	PRON
esrj-105296	52	13	optimally	optimally	ADV
esrj-105296	52	14	separates	separate	VERB
esrj-105296	52	15	two	two	NUM
esrj-105296	52	16	classes	class	NOUN
esrj-105296	52	17	.	.	PUNCT
esrj-105296	53	1	svm	svm	PROPN
esrj-105296	53	2	constructs	construct	VERB
esrj-105296	53	3	its	its	PRON
esrj-105296	53	4	model	model	NOUN
esrj-105296	53	5	without	without	ADP
esrj-105296	53	6	making	make	VERB
esrj-105296	53	7	any	any	DET
esrj-105296	53	8	assumptions	assumption	NOUN
esrj-105296	53	9	regarding	regard	VERB
esrj-105296	53	10	the	the	DET
esrj-105296	53	11	data	datum	NOUN
esrj-105296	53	12	distribution	distribution	NOUN
esrj-105296	53	13	.	.	PUNCT
esrj-105296	54	1	in	in	ADP
esrj-105296	54	2	the	the	DET
esrj-105296	54	3	case	case	NOUN
esrj-105296	54	4	of	of	ADP
esrj-105296	54	5	twoclass	twoclass	NOUN
esrj-105296	54	6	classification	classification	NOUN
esrj-105296	54	7	problems	problem	NOUN
esrj-105296	54	8	where	where	SCONJ
esrj-105296	54	9	classes	class	NOUN
esrj-105296	54	10	are	be	AUX
esrj-105296	54	11	linearly	linearly	ADV
esrj-105296	54	12	separable	separable	ADJ
esrj-105296	54	13	,	,	PUNCT
esrj-105296	54	14	svm	svm	ADJ
esrj-105296	54	15	selects	select	VERB
esrj-105296	54	16	one	one	NUM
esrj-105296	54	17	decision	decision	NOUN
esrj-105296	54	18	boundary	boundary	ADJ
esrj-105296	54	19	that	that	PRON
esrj-105296	54	20	minimizes	minimize	VERB
esrj-105296	54	21	generalization	generalization	NOUN
esrj-105296	54	22	error	error	NOUN
esrj-105296	54	23	from	from	ADP
esrj-105296	54	24	an	an	DET
esrj-105296	54	25	infinite	infinite	ADJ
esrj-105296	54	26	number	number	NOUN
esrj-105296	54	27	of	of	ADP
esrj-105296	54	28	linear	linear	ADJ
esrj-105296	54	29	decision	decision	NOUN
esrj-105296	54	30	boundaries	boundary	NOUN
esrj-105296	54	31	.	.	PUNCT
esrj-105296	55	1	as	as	ADP
esrj-105296	55	2	a	a	DET
esrj-105296	55	3	result	result	NOUN
esrj-105296	55	4	,	,	PUNCT
esrj-105296	55	5	the	the	DET
esrj-105296	55	6	selected	select	VERB
esrj-105296	55	7	decision	decision	NOUN
esrj-105296	55	8	boundary	boundary	NOUN
esrj-105296	55	9	provides	provide	VERB
esrj-105296	55	10	the	the	DET
esrj-105296	55	11	largest	large	ADJ
esrj-105296	55	12	margin	margin	NOUN
esrj-105296	55	13	between	between	ADP
esrj-105296	55	14	classes	class	NOUN
esrj-105296	55	15	on	on	ADP
esrj-105296	55	16	the	the	DET
esrj-105296	55	17	feature	feature	NOUN
esrj-105296	55	18	space	space	NOUN
esrj-105296	55	19	,	,	PUNCT
esrj-105296	55	20	where	where	SCONJ
esrj-105296	55	21	the	the	DET
esrj-105296	55	22	margin	margin	NOUN
esrj-105296	55	23	is	be	AUX
esrj-105296	55	24	determined	determine	VERB
esrj-105296	55	25	as	as	ADP
esrj-105296	55	26	the	the	DET
esrj-105296	55	27	sum	sum	NOUN
esrj-105296	55	28	of	of	ADP
esrj-105296	55	29	the	the	DET
esrj-105296	55	30	distances	distance	NOUN
esrj-105296	55	31	from	from	ADP
esrj-105296	55	32	the	the	DET
esrj-105296	55	33	closest	close	ADJ
esrj-105296	55	34	samples	sample	NOUN
esrj-105296	55	35	of	of	ADP
esrj-105296	55	36	classes	class	NOUN
esrj-105296	55	37	(	(	PUNCT
esrj-105296	55	38	support	support	NOUN
esrj-105296	55	39	vectors	vector	NOUN
esrj-105296	55	40	)	)	PUNCT
esrj-105296	55	41	to	to	ADP
esrj-105296	55	42	the	the	DET
esrj-105296	55	43	hyperplane	hyperplane	NOUN
esrj-105296	55	44	(	(	PUNCT
esrj-105296	55	45	vapnik	vapnik	X
esrj-105296	55	46	,	,	PUNCT
esrj-105296	55	47	1995	1995	NUM
esrj-105296	55	48	)	)	PUNCT
esrj-105296	55	49	.	.	PUNCT
esrj-105296	56	1	due	due	ADP
esrj-105296	56	2	to	to	ADP
esrj-105296	56	3	its	its	PRON
esrj-105296	56	4	consideration	consideration	NOUN
esrj-105296	56	5	of	of	ADP
esrj-105296	56	6	support	support	NOUN
esrj-105296	56	7	vectors	vector	NOUN
esrj-105296	56	8	located	locate	VERB
esrj-105296	56	9	on	on	ADP
esrj-105296	56	10	the	the	DET
esrj-105296	56	11	decision	decision	NOUN
esrj-105296	56	12	boundaries	boundary	NOUN
esrj-105296	56	13	of	of	ADP
esrj-105296	56	14	the	the	DET
esrj-105296	56	15	classification	classification	NOUN
esrj-105296	56	16	process	process	NOUN
esrj-105296	56	17	,	,	PUNCT
esrj-105296	56	18	svm	svm	PROPN
esrj-105296	56	19	is	be	AUX
esrj-105296	56	20	particularly	particularly	ADV
esrj-105296	56	21	useful	useful	ADJ
esrj-105296	56	22	for	for	ADP
esrj-105296	56	23	small	small	ADJ
esrj-105296	56	24	training	training	NOUN
esrj-105296	56	25	data	datum	NOUN
esrj-105296	56	26	sets	set	NOUN
esrj-105296	56	27	(	(	PUNCT
esrj-105296	56	28	mountrakis	mountraki	NOUN
esrj-105296	56	29	et	et	NOUN
esrj-105296	56	30	al	al	PROPN
esrj-105296	56	31	.	.	PROPN
esrj-105296	56	32	,	,	PUNCT
esrj-105296	56	33	2011	2011	NUM
esrj-105296	56	34	)	)	PUNCT
esrj-105296	56	35	.	.	PUNCT
esrj-105296	57	1	in	in	ADP
esrj-105296	57	2	the	the	DET
esrj-105296	57	3	majority	majority	NOUN
esrj-105296	57	4	of	of	ADP
esrj-105296	57	5	classification	classification	NOUN
esrj-105296	57	6	scenarios	scenario	NOUN
esrj-105296	57	7	,	,	PUNCT
esrj-105296	57	8	including	include	VERB
esrj-105296	57	9	the	the	DET
esrj-105296	57	10	classification	classification	NOUN
esrj-105296	57	11	of	of	ADP
esrj-105296	57	12	remotely	remotely	ADV
esrj-105296	57	13	sensed	sense	VERB
esrj-105296	57	14	images	image	NOUN
esrj-105296	57	15	based	base	VERB
esrj-105296	57	16	on	on	ADP
esrj-105296	57	17	sample	sample	NOUN
esrj-105296	57	18	pixels	pixel	NOUN
esrj-105296	57	19	,	,	PUNCT
esrj-105296	57	20	the	the	DET
esrj-105296	57	21	separation	separation	NOUN
esrj-105296	57	22	of	of	ADP
esrj-105296	57	23	classes	class	NOUN
esrj-105296	57	24	can	can	AUX
esrj-105296	57	25	not	not	PART
esrj-105296	57	26	be	be	AUX
esrj-105296	57	27	achieved	achieve	VERB
esrj-105296	57	28	through	through	ADP
esrj-105296	57	29	a	a	DET
esrj-105296	57	30	linear	linear	ADJ
esrj-105296	57	31	hyperplane	hyperplane	NOUN
esrj-105296	57	32	within	within	ADP
esrj-105296	57	33	the	the	DET
esrj-105296	57	34	input	input	NOUN
esrj-105296	57	35	space	space	NOUN
esrj-105296	57	36	.	.	PUNCT
esrj-105296	58	1	to	to	PART
esrj-105296	58	2	address	address	VERB
esrj-105296	58	3	this	this	DET
esrj-105296	58	4	challenge	challenge	NOUN
esrj-105296	58	5	,	,	PUNCT
esrj-105296	58	6	boser	boser	NOUN
esrj-105296	58	7	et	et	PROPN
esrj-105296	58	8	al	al	PROPN
esrj-105296	58	9	.	.	PROPN
esrj-105296	59	1	(	(	PUNCT
esrj-105296	59	2	1992	1992	NUM
esrj-105296	59	3	)	)	PUNCT
esrj-105296	59	4	proposed	propose	VERB
esrj-105296	59	5	a	a	DET
esrj-105296	59	6	method	method	NOUN
esrj-105296	59	7	of	of	ADP
esrj-105296	59	8	transforming	transform	VERB
esrj-105296	59	9	the	the	DET
esrj-105296	59	10	input	input	NOUN
esrj-105296	59	11	data	datum	NOUN
esrj-105296	59	12	onto	onto	ADP
esrj-105296	59	13	a	a	DET
esrj-105296	59	14	higher	high	ADJ
esrj-105296	59	15	dimensional	dimensional	ADJ
esrj-105296	59	16	input	input	NOUN
esrj-105296	59	17	space	space	NOUN
esrj-105296	59	18	by	by	ADP
esrj-105296	59	19	using	use	VERB
esrj-105296	59	20	kernel	kernel	NOUN
esrj-105296	59	21	functions	function	NOUN
esrj-105296	59	22	.	.	PUNCT
esrj-105296	60	1	while	while	SCONJ
esrj-105296	60	2	linear	linear	ADJ
esrj-105296	60	3	,	,	PUNCT
esrj-105296	60	4	polynomial	polynomial	ADJ
esrj-105296	60	5	,	,	PUNCT
esrj-105296	60	6	radial	radial	ADJ
esrj-105296	60	7	basis	basis	NOUN
esrj-105296	60	8	function	function	NOUN
esrj-105296	60	9	(	(	PUNCT
esrj-105296	60	10	rbf	rbf	PROPN
esrj-105296	60	11	)	)	PUNCT
esrj-105296	60	12	,	,	PUNCT
esrj-105296	60	13	and	and	CCONJ
esrj-105296	60	14	sigmoid	sigmoid	NOUN
esrj-105296	60	15	kernels	kernel	NOUN
esrj-105296	60	16	are	be	AUX
esrj-105296	60	17	commonly	commonly	ADV
esrj-105296	60	18	used	use	VERB
esrj-105296	60	19	kernel	kernel	NOUN
esrj-105296	60	20	functions	function	NOUN
esrj-105296	60	21	on	on	ADP
esrj-105296	60	22	implementing	implement	VERB
esrj-105296	60	23	the	the	DET
esrj-105296	60	24	nonlinear	nonlinear	ADJ
esrj-105296	60	25	svm	svm	NOUN
esrj-105296	60	26	in	in	ADP
esrj-105296	60	27	the	the	DET
esrj-105296	60	28	literature	literature	NOUN
esrj-105296	60	29	,	,	PUNCT
esrj-105296	60	30	polynomial	polynomial	ADJ
esrj-105296	60	31	and	and	CCONJ
esrj-105296	60	32	rbf	rbf	PROPN
esrj-105296	60	33	kernels	kernel	NOUN
esrj-105296	60	34	are	be	AUX
esrj-105296	60	35	the	the	DET
esrj-105296	60	36	most	most	ADV
esrj-105296	60	37	suitable	suitable	ADJ
esrj-105296	60	38	functions	function	NOUN
esrj-105296	60	39	for	for	ADP
esrj-105296	60	40	remotely	remotely	ADV
esrj-105296	60	41	sensed	sense	VERB
esrj-105296	60	42	image	image	NOUN
esrj-105296	60	43	classification	classification	NOUN
esrj-105296	60	44	problems	problem	NOUN
esrj-105296	60	45	(	(	PUNCT
esrj-105296	60	46	kavzoglu	kavzoglu	PROPN
esrj-105296	60	47	&	&	CCONJ
esrj-105296	60	48	colkesen	colkesen	PROPN
esrj-105296	60	49	,	,	PUNCT
esrj-105296	60	50	2009	2009	NUM
esrj-105296	60	51	;	;	PUNCT
esrj-105296	60	52	melgani	melgani	PROPN
esrj-105296	60	53	&	&	CCONJ
esrj-105296	60	54	bruzzone	bruzzone	NOUN
esrj-105296	60	55	,	,	PUNCT
esrj-105296	60	56	2004	2004	NUM
esrj-105296	60	57	;	;	PUNCT
esrj-105296	60	58	pal	pal	NOUN
esrj-105296	60	59	&	&	CCONJ
esrj-105296	60	60	mather	mather	PROPN
esrj-105296	60	61	,	,	PUNCT
esrj-105296	60	62	2005	2005	NUM
esrj-105296	60	63	)	)	PUNCT
esrj-105296	60	64	.	.	PUNCT
esrj-105296	61	1	furthermore	furthermore	ADV
esrj-105296	61	2	,	,	PUNCT
esrj-105296	61	3	mountrakis	mountraki	NOUN
esrj-105296	61	4	et	et	PROPN
esrj-105296	61	5	al	al	PROPN
esrj-105296	61	6	.	.	PUNCT
esrj-105296	61	7	(	(	PUNCT
esrj-105296	61	8	2011	2011	NUM
esrj-105296	61	9	)	)	PUNCT
esrj-105296	61	10	emphasized	emphasize	VERB
esrj-105296	61	11	that	that	SCONJ
esrj-105296	61	12	the	the	DET
esrj-105296	61	13	rbf	rbf	PROPN
esrj-105296	61	14	kernel	kernel	PROPN
esrj-105296	61	15	function	function	PROPN
esrj-105296	61	16	is	be	AUX
esrj-105296	61	17	the	the	DET
esrj-105296	61	18	most	most	ADV
esrj-105296	61	19	suitable	suitable	ADJ
esrj-105296	61	20	kernel	kernel	NOUN
esrj-105296	61	21	function	function	NOUN
esrj-105296	61	22	for	for	ADP
esrj-105296	61	23	remote	remote	ADJ
esrj-105296	61	24	sensing	sense	VERB
esrj-105296	61	25	data	datum	NOUN
esrj-105296	61	26	analysis	analysis	NOUN
esrj-105296	61	27	.	.	PUNCT
esrj-105296	62	1	in	in	ADP
esrj-105296	62	2	this	this	DET
esrj-105296	62	3	study	study	NOUN
esrj-105296	62	4	,	,	PUNCT
esrj-105296	62	5	the	the	DET
esrj-105296	62	6	rbf	rbf	PROPN
esrj-105296	62	7	kernel	kernel	PROPN
esrj-105296	62	8	was	be	AUX
esrj-105296	62	9	employed	employ	VERB
esrj-105296	62	10	to	to	PART
esrj-105296	62	11	classify	classify	VERB
esrj-105296	62	12	hsis	hsis	NOUN
esrj-105296	62	13	using	use	VERB
esrj-105296	62	14	the	the	DET
esrj-105296	62	15	svm	svm	ADJ
esrj-105296	62	16	algorithm	algorithm	NOUN
esrj-105296	62	17	.	.	PUNCT
esrj-105296	63	1	two	two	NUM
esrj-105296	63	2	user	user	NOUN
esrj-105296	63	3	-	-	PUNCT
esrj-105296	63	4	defined	define	VERB
esrj-105296	63	5	parameters	parameter	NOUN
esrj-105296	63	6	,	,	PUNCT
esrj-105296	63	7	namely	namely	ADV
esrj-105296	63	8	the	the	DET
esrj-105296	63	9	regularization	regularization	NOUN
esrj-105296	63	10	parameter	parameter	NOUN
esrj-105296	63	11	c	c	PROPN
esrj-105296	63	12	and	and	CCONJ
esrj-105296	63	13	kernel	kernel	PROPN
esrj-105296	63	14	width	width	PROPN
esrj-105296	63	15	γ	γ	PROPN
esrj-105296	63	16	,	,	PUNCT
esrj-105296	63	17	must	must	AUX
esrj-105296	63	18	be	be	AUX
esrj-105296	63	19	specified	specify	VERB
esrj-105296	63	20	for	for	ADP
esrj-105296	63	21	the	the	DET
esrj-105296	63	22	rbf	rbf	PROPN
esrj-105296	63	23	kernel	kernel	PROPN
esrj-105296	63	24	.	.	PUNCT
esrj-105296	64	1	as	as	SCONJ
esrj-105296	64	2	the	the	DET
esrj-105296	64	3	optimal	optimal	ADJ
esrj-105296	64	4	parameter	parameter	NOUN
esrj-105296	64	5	combinations	combination	NOUN
esrj-105296	64	6	for	for	ADP
esrj-105296	64	7	achieving	achieve	VERB
esrj-105296	64	8	the	the	DET
esrj-105296	64	9	best	good	ADJ
esrj-105296	64	10	classification	classification	NOUN
esrj-105296	64	11	performance	performance	NOUN
esrj-105296	64	12	are	be	AUX
esrj-105296	64	13	unknown	unknown	ADJ
esrj-105296	64	14	for	for	ADP
esrj-105296	64	15	a	a	DET
esrj-105296	64	16	given	give	VERB
esrj-105296	64	17	dataset	dataset	NOUN
esrj-105296	64	18	,	,	PUNCT
esrj-105296	64	19	an	an	DET
esrj-105296	64	20	exhaustive	exhaustive	ADJ
esrj-105296	64	21	search	search	NOUN
esrj-105296	64	22	for	for	ADP
esrj-105296	64	23	optimal	optimal	ADJ
esrj-105296	64	24	parameters	parameter	NOUN
esrj-105296	64	25	is	be	AUX
esrj-105296	64	26	necessary	necessary	ADJ
esrj-105296	64	27	(	(	PUNCT
esrj-105296	64	28	hsu	hsu	PROPN
esrj-105296	64	29	et	et	PROPN
esrj-105296	64	30	al	al	PROPN
esrj-105296	64	31	.	.	PROPN
esrj-105296	64	32	,	,	PUNCT
esrj-105296	64	33	2003	2003	NUM
esrj-105296	64	34	)	)	PUNCT
esrj-105296	64	35	.	.	PUNCT
esrj-105296	65	1	in	in	ADP
esrj-105296	65	2	these	these	DET
esrj-105296	65	3	experiments	experiment	NOUN
esrj-105296	65	4	,	,	PUNCT
esrj-105296	65	5	the	the	DET
esrj-105296	65	6	c	c	NOUN
esrj-105296	65	7	and	and	CCONJ
esrj-105296	65	8	γ	γ	PROPN
esrj-105296	65	9	parameters	parameter	NOUN
esrj-105296	65	10	of	of	ADP
esrj-105296	65	11	the	the	DET
esrj-105296	65	12	rbf	rbf	PROPN
esrj-105296	65	13	kernel	kernel	PROPN
esrj-105296	65	14	were	be	AUX
esrj-105296	65	15	determined	determine	VERB
esrj-105296	65	16	using	use	VERB
esrj-105296	65	17	a	a	DET
esrj-105296	65	18	grid	grid	NOUN
esrj-105296	65	19	search	search	NOUN
esrj-105296	65	20	method	method	NOUN
esrj-105296	65	21	coupled	couple	VERB
esrj-105296	65	22	with	with	ADP
esrj-105296	65	23	cross	cross	NOUN
esrj-105296	65	24	-	-	NOUN
esrj-105296	65	25	validation	validation	NOUN
esrj-105296	65	26	.	.	PUNCT
esrj-105296	66	1	2.2	2.2	NUM
esrj-105296	66	2	.	.	PUNCT
esrj-105296	66	3	random	random	ADJ
esrj-105296	66	4	forest	forest	NOUN
esrj-105296	66	5	(	(	PUNCT
esrj-105296	66	6	rf	rf	NOUN
esrj-105296	66	7	)	)	PUNCT
esrj-105296	66	8	the	the	DET
esrj-105296	66	9	rf	rf	ADJ
esrj-105296	66	10	algorithm	algorithm	NOUN
esrj-105296	66	11	is	be	AUX
esrj-105296	66	12	a	a	DET
esrj-105296	66	13	commonly	commonly	ADV
esrj-105296	66	14	used	use	VERB
esrj-105296	66	15	ensemble	ensemble	ADJ
esrj-105296	66	16	learning	learning	NOUN
esrj-105296	66	17	algorithm	algorithm	NOUN
esrj-105296	66	18	based	base	VERB
esrj-105296	66	19	on	on	ADP
esrj-105296	66	20	decision	decision	NOUN
esrj-105296	66	21	trees	tree	NOUN
esrj-105296	66	22	,	,	PUNCT
esrj-105296	66	23	utilized	utilize	VERB
esrj-105296	66	24	for	for	ADP
esrj-105296	66	25	tasks	task	NOUN
esrj-105296	66	26	such	such	ADJ
esrj-105296	66	27	as	as	ADP
esrj-105296	66	28	classification	classification	NOUN
esrj-105296	66	29	,	,	PUNCT
esrj-105296	66	30	regression	regression	NOUN
esrj-105296	66	31	,	,	PUNCT
esrj-105296	66	32	and	and	CCONJ
esrj-105296	66	33	feature	feature	NOUN
esrj-105296	66	34	selection	selection	NOUN
esrj-105296	66	35	.	.	PUNCT
esrj-105296	67	1	breiman	breiman	NOUN
esrj-105296	67	2	(	(	PUNCT
esrj-105296	67	3	2001	2001	NUM
esrj-105296	67	4	)	)	PUNCT
esrj-105296	67	5	developed	develop	VERB
esrj-105296	67	6	the	the	DET
esrj-105296	67	7	bagging	bagging	NOUN
esrj-105296	67	8	algorithm	algorithm	NOUN
esrj-105296	67	9	with	with	ADP
esrj-105296	67	10	the	the	DET
esrj-105296	67	11	philosophy	philosophy	NOUN
esrj-105296	67	12	that	that	SCONJ
esrj-105296	67	13	an	an	DET
esrj-105296	67	14	ensemble	ensemble	NOUN
esrj-105296	67	15	of	of	ADP
esrj-105296	67	16	classifiers	classifier	NOUN
esrj-105296	67	17	can	can	AUX
esrj-105296	67	18	achieve	achieve	VERB
esrj-105296	67	19	superior	superior	ADJ
esrj-105296	67	20	classification	classification	NOUN
esrj-105296	67	21	performance	performance	NOUN
esrj-105296	67	22	compared	compare	VERB
esrj-105296	67	23	to	to	ADP
esrj-105296	67	24	a	a	DET
esrj-105296	67	25	single	single	ADJ
esrj-105296	67	26	classifier	classifier	NOUN
esrj-105296	67	27	.	.	PUNCT
esrj-105296	68	1	the	the	DET
esrj-105296	68	2	rf	rf	ADJ
esrj-105296	68	3	algorithm	algorithm	NOUN
esrj-105296	68	4	combines	combine	VERB
esrj-105296	68	5	multiple	multiple	ADJ
esrj-105296	68	6	multivariate	multivariate	NOUN
esrj-105296	68	7	decision	decision	NOUN
esrj-105296	68	8	trees	tree	NOUN
esrj-105296	68	9	,	,	PUNCT
esrj-105296	68	10	with	with	SCONJ
esrj-105296	68	11	each	each	DET
esrj-105296	68	12	tree	tree	NOUN
esrj-105296	68	13	trained	train	VERB
esrj-105296	68	14	on	on	ADP
esrj-105296	68	15	a	a	DET
esrj-105296	68	16	distinct	distinct	ADJ
esrj-105296	68	17	asset	asset	NOUN
esrj-105296	68	18	of	of	ADP
esrj-105296	68	19	different	different	ADJ
esrj-105296	68	20	training	training	NOUN
esrj-105296	68	21	data	datum	NOUN
esrj-105296	68	22	(	(	PUNCT
esrj-105296	68	23	breiman	breiman	NOUN
esrj-105296	68	24	,	,	PUNCT
esrj-105296	68	25	2001	2001	NUM
esrj-105296	68	26	;	;	PUNCT
esrj-105296	68	27	rodriguez	rodriguez	NOUN
esrj-105296	68	28	-	-	PUNCT
esrj-105296	68	29	galiano	galiano	PROPN
esrj-105296	68	30	et	et	NOUN
esrj-105296	68	31	al	al	PROPN
esrj-105296	68	32	.	.	PROPN
esrj-105296	68	33	,	,	PUNCT
esrj-105296	68	34	2012	2012	NUM
esrj-105296	68	35	)	)	PUNCT
esrj-105296	68	36	.	.	PUNCT
esrj-105296	69	1	to	to	PART
esrj-105296	69	2	construct	construct	VERB
esrj-105296	69	3	decision	decision	NOUN
esrj-105296	69	4	trees	tree	NOUN
esrj-105296	69	5	within	within	ADP
esrj-105296	69	6	rf	rf	NOUN
esrj-105296	69	7	,	,	PUNCT
esrj-105296	69	8	the	the	DET
esrj-105296	69	9	classification	classification	NOUN
esrj-105296	69	10	and	and	CCONJ
esrj-105296	69	11	regression	regression	NOUN
esrj-105296	69	12	trees	tree	NOUN
esrj-105296	69	13	methodology	methodology	NOUN
esrj-105296	69	14	is	be	AUX
esrj-105296	69	15	applied	apply	VERB
esrj-105296	69	16	without	without	ADP
esrj-105296	69	17	pruning	prune	VERB
esrj-105296	69	18	(	(	PUNCT
esrj-105296	69	19	breiman	breiman	NOUN
esrj-105296	69	20	,	,	PUNCT
esrj-105296	69	21	2001	2001	NUM
esrj-105296	69	22	;	;	PUNCT
esrj-105296	69	23	breiman	breiman	NOUN
esrj-105296	69	24	et	et	PROPN
esrj-105296	69	25	al	al	PROPN
esrj-105296	69	26	.	.	PROPN
esrj-105296	69	27	,	,	PUNCT
esrj-105296	69	28	1984	1984	NUM
esrj-105296	69	29	)	)	PUNCT
esrj-105296	69	30	.	.	PUNCT
esrj-105296	70	1	the	the	DET
esrj-105296	70	2	gini	gini	PROPN
esrj-105296	70	3	index	index	NOUN
esrj-105296	70	4	serves	serve	VERB
esrj-105296	70	5	as	as	ADP
esrj-105296	70	6	the	the	DET
esrj-105296	70	7	attribute	attribute	NOUN
esrj-105296	70	8	selection	selection	NOUN
esrj-105296	70	9	criterion	criterion	NOUN
esrj-105296	70	10	,	,	PUNCT
esrj-105296	70	11	evaluating	evaluate	VERB
esrj-105296	70	12	the	the	DET
esrj-105296	70	13	impurity	impurity	NOUN
esrj-105296	70	14	of	of	ADP
esrj-105296	70	15	attributes	attribute	NOUN
esrj-105296	70	16	(	(	PUNCT
esrj-105296	70	17	e.g.	e.g.	ADV
esrj-105296	70	18	,	,	PUNCT
esrj-105296	70	19	spectral	spectral	ADJ
esrj-105296	70	20	bands	band	NOUN
esrj-105296	70	21	in	in	ADP
esrj-105296	70	22	hyperspectral	hyperspectral	ADJ
esrj-105296	70	23	data	datum	NOUN
esrj-105296	70	24	)	)	PUNCT
esrj-105296	70	25	with	with	ADP
esrj-105296	70	26	respect	respect	NOUN
esrj-105296	70	27	to	to	ADP
esrj-105296	70	28	classes	class	NOUN
esrj-105296	70	29	during	during	ADP
esrj-105296	70	30	the	the	DET
esrj-105296	70	31	constructing	constructing	NOUN
esrj-105296	70	32	of	of	ADP
esrj-105296	70	33	the	the	DET
esrj-105296	70	34	model	model	NOUN
esrj-105296	70	35	(	(	PUNCT
esrj-105296	70	36	pal	pal	NOUN
esrj-105296	70	37	,	,	PUNCT
esrj-105296	70	38	2005	2005	NUM
esrj-105296	70	39	)	)	PUNCT
esrj-105296	70	40	.	.	PUNCT
esrj-105296	71	1	the	the	DET
esrj-105296	71	2	gini	gini	PROPN
esrj-105296	71	3	index	index	NOUN
esrj-105296	71	4	for	for	ADP
esrj-105296	71	5	a	a	DET
esrj-105296	71	6	given	give	VERB
esrj-105296	71	7	training	training	NOUN
esrj-105296	71	8	dataset	dataset	NOUN
esrj-105296	71	9	t	t	PROPN
esrj-105296	71	10	can	can	AUX
esrj-105296	71	11	be	be	AUX
esrj-105296	71	12	expressed	express	VERB
esrj-105296	71	13	as	as	SCONJ
esrj-105296	71	14	follows	follow	VERB
esrj-105296	71	15	:	:	PUNCT
esrj-105296	71	16	163investigation	163investigation	PROPN
esrj-105296	71	17	of	of	ADP
esrj-105296	71	18	the	the	DET
esrj-105296	71	19	performances	performance	NOUN
esrj-105296	71	20	of	of	ADP
esrj-105296	71	21	support	support	NOUN
esrj-105296	71	22	vector	vector	NOUN
esrj-105296	71	23	machine	machine	NOUN
esrj-105296	71	24	,	,	PUNCT
esrj-105296	71	25	random	random	ADJ
esrj-105296	71	26	forest	forest	NOUN
esrj-105296	71	27	,	,	PUNCT
esrj-105296	71	28	and	and	CCONJ
esrj-105296	71	29	3d-2d	3d-2d	NUM
esrj-105296	71	30	convolutional	convolutional	ADJ
esrj-105296	71	31	neural	neural	ADJ
esrj-105296	71	32	network	network	NOUN
esrj-105296	71	33	for	for	ADP
esrj-105296	71	34	hyperspectral	hyperspectral	ADJ
esrj-105296	71	35	image	image	NOUN
esrj-105296	71	36	classification	classification	NOUN
esrj-105296	71	37	=	=	SYM
esrj-105296	71	38	≠	≠	PROPN
esrj-105296	71	39	,	,	PUNCT
esrj-105296	71	40	(	(	PUNCT
esrj-105296	71	41	)	)	PUNCT
esrj-105296	72	1	|	|	ADV
esrj-105296	72	2	|	|	ADV
esrj-105296	72	3	,	,	PUNCT
esrj-105296	72	4	(	(	PUNCT
esrj-105296	72	5	)	)	PUNCT
esrj-105296	72	6	|	|	ADV
esrj-105296	72	7	|	|	ADV
esrj-105296	72	8	(	(	PUNCT
esrj-105296	72	9	1	1	X
esrj-105296	72	10	)	)	PUNCT
esrj-105296	72	11	where	where	SCONJ
esrj-105296	72	12	the	the	DET
esrj-105296	72	13	f	f	X
esrj-105296	72	14	(	(	PUNCT
esrj-105296	72	15	cj	cj	PROPN
esrj-105296	72	16	,	,	PUNCT
esrj-105296	72	17	t	t	PROPN
esrj-105296	72	18	)	)	PUNCT
esrj-105296	72	19	/	/	SYM
esrj-105296	72	20	|t|	|t|	NOUN
esrj-105296	72	21	denotes	denote	VERB
esrj-105296	72	22	the	the	DET
esrj-105296	72	23	probability	probability	NOUN
esrj-105296	72	24	that	that	SCONJ
esrj-105296	72	25	a	a	DET
esrj-105296	72	26	selected	select	VERB
esrj-105296	72	27	case	case	NOUN
esrj-105296	72	28	(	(	PUNCT
esrj-105296	72	29	e.g.	e.g.	ADV
esrj-105296	72	30	,	,	PUNCT
esrj-105296	72	31	a	a	DET
esrj-105296	72	32	pixel	pixel	NOUN
esrj-105296	72	33	)	)	PUNCT
esrj-105296	72	34	belongs	belong	VERB
esrj-105296	72	35	to	to	ADP
esrj-105296	72	36	the	the	DET
esrj-105296	72	37	class	class	NOUN
esrj-105296	72	38	ci	ci	PROPN
esrj-105296	72	39	.	.	PUNCT
esrj-105296	73	1	during	during	ADP
esrj-105296	73	2	the	the	DET
esrj-105296	73	3	training	training	NOUN
esrj-105296	73	4	of	of	ADP
esrj-105296	73	5	the	the	DET
esrj-105296	73	6	rf	rf	ADJ
esrj-105296	73	7	model	model	NOUN
esrj-105296	73	8	,	,	PUNCT
esrj-105296	73	9	approximately	approximately	ADV
esrj-105296	73	10	2/3	2/3	NUM
esrj-105296	73	11	of	of	ADP
esrj-105296	73	12	the	the	DET
esrj-105296	73	13	samples	sample	NOUN
esrj-105296	73	14	(	(	PUNCT
esrj-105296	73	15	referred	refer	VERB
esrj-105296	73	16	to	to	ADP
esrj-105296	73	17	as	as	ADP
esrj-105296	73	18	“	"	PUNCT
esrj-105296	73	19	in	in	ADP
esrj-105296	73	20	-	-	PUNCT
esrj-105296	73	21	bag	bag	NOUN
esrj-105296	73	22	”	"	PUNCT
esrj-105296	73	23	)	)	PUNCT
esrj-105296	73	24	within	within	ADP
esrj-105296	73	25	each	each	DET
esrj-105296	73	26	randomly	randomly	ADV
esrj-105296	73	27	selected	select	VERB
esrj-105296	73	28	subset	subset	NOUN
esrj-105296	73	29	are	be	AUX
esrj-105296	73	30	utilized	utilize	VERB
esrj-105296	73	31	to	to	PART
esrj-105296	73	32	construct	construct	VERB
esrj-105296	73	33	each	each	DET
esrj-105296	73	34	decision	decision	NOUN
esrj-105296	73	35	tree	tree	NOUN
esrj-105296	73	36	in	in	ADP
esrj-105296	73	37	the	the	DET
esrj-105296	73	38	forest	forest	NOUN
esrj-105296	73	39	.	.	PUNCT
esrj-105296	74	1	the	the	DET
esrj-105296	74	2	remaining	remain	VERB
esrj-105296	74	3	1/3	1/3	NUM
esrj-105296	74	4	of	of	ADP
esrj-105296	74	5	the	the	DET
esrj-105296	74	6	samples	sample	NOUN
esrj-105296	74	7	(	(	PUNCT
esrj-105296	74	8	referred	refer	VERB
esrj-105296	74	9	to	to	ADP
esrj-105296	74	10	as	as	ADP
esrj-105296	74	11	“	"	PUNCT
esrj-105296	74	12	out	out	ADP
esrj-105296	74	13	-	-	PUNCT
esrj-105296	74	14	of	of	ADP
esrj-105296	74	15	-	-	PUNCT
esrj-105296	74	16	bag	bag	NOUN
esrj-105296	74	17	”	"	PUNCT
esrj-105296	74	18	)	)	PUNCT
esrj-105296	74	19	are	be	AUX
esrj-105296	74	20	employed	employ	VERB
esrj-105296	74	21	to	to	PART
esrj-105296	74	22	validate	validate	VERB
esrj-105296	74	23	the	the	DET
esrj-105296	74	24	performance	performance	NOUN
esrj-105296	74	25	of	of	ADP
esrj-105296	74	26	the	the	DET
esrj-105296	74	27	final	final	ADJ
esrj-105296	74	28	ensemble	ensemble	NOUN
esrj-105296	74	29	using	use	VERB
esrj-105296	74	30	an	an	DET
esrj-105296	74	31	internal	internal	ADJ
esrj-105296	74	32	cross	cross	ADJ
esrj-105296	74	33	-	-	ADJ
esrj-105296	74	34	validation	validation	ADJ
esrj-105296	74	35	approach	approach	NOUN
esrj-105296	74	36	(	(	PUNCT
esrj-105296	74	37	sahin	sahin	PROPN
esrj-105296	74	38	et	et	PROPN
esrj-105296	74	39	al	al	PROPN
esrj-105296	74	40	.	.	PROPN
esrj-105296	74	41	,	,	PUNCT
esrj-105296	74	42	2020	2020	NUM
esrj-105296	74	43	)	)	PUNCT
esrj-105296	74	44	.	.	PUNCT
esrj-105296	75	1	to	to	PART
esrj-105296	75	2	determine	determine	VERB
esrj-105296	75	3	the	the	DET
esrj-105296	75	4	final	final	ADJ
esrj-105296	75	5	label	label	NOUN
esrj-105296	75	6	of	of	ADP
esrj-105296	75	7	an	an	DET
esrj-105296	75	8	unknown	unknown	ADJ
esrj-105296	75	9	sample	sample	NOUN
esrj-105296	75	10	(	(	PUNCT
esrj-105296	75	11	e.g.	e.g.	ADV
esrj-105296	75	12	,	,	PUNCT
esrj-105296	75	13	a	a	DET
esrj-105296	75	14	pixel	pixel	NOUN
esrj-105296	75	15	)	)	PUNCT
esrj-105296	75	16	,	,	PUNCT
esrj-105296	75	17	the	the	DET
esrj-105296	75	18	classification	classification	NOUN
esrj-105296	75	19	results	result	NOUN
esrj-105296	75	20	of	of	ADP
esrj-105296	75	21	each	each	DET
esrj-105296	75	22	decision	decision	NOUN
esrj-105296	75	23	tree	tree	NOUN
esrj-105296	75	24	in	in	ADP
esrj-105296	75	25	the	the	DET
esrj-105296	75	26	forest	forest	NOUN
esrj-105296	75	27	are	be	AUX
esrj-105296	75	28	combined	combine	VERB
esrj-105296	75	29	by	by	ADP
esrj-105296	75	30	performing	perform	VERB
esrj-105296	75	31	majority	majority	NOUN
esrj-105296	75	32	voting	voting	NOUN
esrj-105296	75	33	,	,	PUNCT
esrj-105296	75	34	with	with	ADP
esrj-105296	75	35	each	each	DET
esrj-105296	75	36	classifier	classifier	NOUN
esrj-105296	75	37	vote	vote	NOUN
esrj-105296	75	38	carrying	carry	VERB
esrj-105296	75	39	equal	equal	ADJ
esrj-105296	75	40	weight	weight	NOUN
esrj-105296	75	41	(	(	PUNCT
esrj-105296	75	42	chan	chan	PROPN
esrj-105296	75	43	&	&	CCONJ
esrj-105296	75	44	paelinckx	paelinckx	PROPN
esrj-105296	75	45	,	,	PUNCT
esrj-105296	75	46	2008	2008	NUM
esrj-105296	75	47	)	)	PUNCT
esrj-105296	75	48	.	.	PUNCT
esrj-105296	76	1	implementing	implement	VERB
esrj-105296	76	2	an	an	DET
esrj-105296	76	3	rf	rf	ADJ
esrj-105296	76	4	model	model	NOUN
esrj-105296	76	5	requires	require	VERB
esrj-105296	76	6	setting	set	VERB
esrj-105296	76	7	two	two	NUM
esrj-105296	76	8	user	user	NOUN
esrj-105296	76	9	-	-	PUNCT
esrj-105296	76	10	defined	define	VERB
esrj-105296	76	11	parameters	parameter	NOUN
esrj-105296	76	12	:	:	PUNCT
esrj-105296	76	13	the	the	DET
esrj-105296	76	14	number	number	NOUN
esrj-105296	76	15	of	of	ADP
esrj-105296	76	16	trees	tree	NOUN
esrj-105296	76	17	(	(	PUNCT
esrj-105296	76	18	ntree	ntree	NOUN
esrj-105296	76	19	)	)	PUNCT
esrj-105296	76	20	and	and	CCONJ
esrj-105296	76	21	the	the	DET
esrj-105296	76	22	number	number	NOUN
esrj-105296	76	23	of	of	ADP
esrj-105296	76	24	randomly	randomly	ADV
esrj-105296	76	25	selected	select	VERB
esrj-105296	76	26	features	feature	NOUN
esrj-105296	76	27	at	at	ADP
esrj-105296	76	28	each	each	DET
esrj-105296	76	29	node	node	NOUN
esrj-105296	76	30	(	(	PUNCT
esrj-105296	76	31	mtry	mtry	NOUN
esrj-105296	76	32	)	)	PUNCT
esrj-105296	76	33	(	(	PUNCT
esrj-105296	76	34	sheykhmousa	sheykhmousa	NOUN
esrj-105296	76	35	et	et	PROPN
esrj-105296	76	36	al	al	PROPN
esrj-105296	76	37	.	.	PROPN
esrj-105296	76	38	,	,	PUNCT
esrj-105296	76	39	2020	2020	NUM
esrj-105296	76	40	)	)	PUNCT
esrj-105296	76	41	.	.	PUNCT
esrj-105296	77	1	the	the	DET
esrj-105296	77	2	parameter	parameter	NOUN
esrj-105296	77	3	mtry	mtry	NOUN
esrj-105296	77	4	is	be	AUX
esrj-105296	77	5	typically	typically	ADV
esrj-105296	77	6	set	set	VERB
esrj-105296	77	7	the	the	DET
esrj-105296	77	8	square	square	ADJ
esrj-105296	77	9	root	root	NOUN
esrj-105296	77	10	of	of	ADP
esrj-105296	77	11	the	the	DET
esrj-105296	77	12	total	total	ADJ
esrj-105296	77	13	number	number	NOUN
esrj-105296	77	14	of	of	ADP
esrj-105296	77	15	input	input	NOUN
esrj-105296	77	16	features	feature	NOUN
esrj-105296	77	17	,	,	PUNCT
esrj-105296	77	18	while	while	SCONJ
esrj-105296	77	19	the	the	DET
esrj-105296	77	20	ntree	ntree	ADJ
esrj-105296	77	21	parameter	parameter	NOUN
esrj-105296	77	22	is	be	AUX
esrj-105296	77	23	commonly	commonly	ADV
esrj-105296	77	24	determined	determine	VERB
esrj-105296	77	25	as	as	ADP
esrj-105296	77	26	500	500	NUM
esrj-105296	77	27	,	,	PUNCT
esrj-105296	77	28	as	as	SCONJ
esrj-105296	77	29	the	the	DET
esrj-105296	77	30	error	error	NOUN
esrj-105296	77	31	tends	tend	VERB
esrj-105296	77	32	to	to	PART
esrj-105296	77	33	stabilize	stabilize	VERB
esrj-105296	77	34	prior	prior	ADV
esrj-105296	77	35	to	to	ADP
esrj-105296	77	36	this	this	DET
esrj-105296	77	37	number	number	NOUN
esrj-105296	77	38	of	of	ADP
esrj-105296	77	39	classification	classification	NOUN
esrj-105296	77	40	trees	tree	NOUN
esrj-105296	77	41	(	(	PUNCT
esrj-105296	77	42	belgiu	belgiu	ADP
esrj-105296	77	43	&	&	CCONJ
esrj-105296	77	44	drăguţ	drăguţ	NOUN
esrj-105296	77	45	,	,	PUNCT
esrj-105296	77	46	2016	2016	NUM
esrj-105296	77	47	)	)	PUNCT
esrj-105296	77	48	.	.	PUNCT
esrj-105296	78	1	moreover	moreover	ADV
esrj-105296	78	2	,	,	PUNCT
esrj-105296	78	3	both	both	CCONJ
esrj-105296	78	4	theoretical	theoretical	ADJ
esrj-105296	78	5	and	and	CCONJ
esrj-105296	78	6	empirical	empirical	ADJ
esrj-105296	78	7	studies	study	NOUN
esrj-105296	78	8	have	have	AUX
esrj-105296	78	9	reported	report	VERB
esrj-105296	78	10	that	that	SCONJ
esrj-105296	78	11	the	the	DET
esrj-105296	78	12	classification	classification	NOUN
esrj-105296	78	13	accuracy	accuracy	NOUN
esrj-105296	78	14	of	of	ADP
esrj-105296	78	15	the	the	DET
esrj-105296	78	16	rfs	rfs	NOUN
esrj-105296	78	17	is	be	AUX
esrj-105296	78	18	less	less	ADV
esrj-105296	78	19	sensitive	sensitive	ADJ
esrj-105296	78	20	to	to	ADP
esrj-105296	78	21	the	the	DET
esrj-105296	78	22	parameter	parameter	NOUN
esrj-105296	78	23	ntree	ntree	NOUN
esrj-105296	78	24	compared	compare	VERB
esrj-105296	78	25	to	to	ADP
esrj-105296	78	26	mtry	mtry	NOUN
esrj-105296	78	27	(	(	PUNCT
esrj-105296	78	28	kulkarni	kulkarni	PROPN
esrj-105296	78	29	&	&	CCONJ
esrj-105296	78	30	sinha	sinha	PROPN
esrj-105296	78	31	,	,	PUNCT
esrj-105296	78	32	2012	2012	NUM
esrj-105296	78	33	)	)	PUNCT
esrj-105296	78	34	.	.	PUNCT
esrj-105296	79	1	nevertheless	nevertheless	ADV
esrj-105296	79	2	,	,	PUNCT
esrj-105296	79	3	the	the	DET
esrj-105296	79	4	parameters	parameter	NOUN
esrj-105296	79	5	ntree	ntree	VERB
esrj-105296	79	6	and	and	CCONJ
esrj-105296	79	7	mrty	mrty	NUM
esrj-105296	79	8	were	be	AUX
esrj-105296	79	9	determined	determine	VERB
esrj-105296	79	10	through	through	ADP
esrj-105296	79	11	a	a	DET
esrj-105296	79	12	grid	grid	NOUN
esrj-105296	79	13	search	search	NOUN
esrj-105296	79	14	method	method	NOUN
esrj-105296	79	15	utilizing	utilize	VERB
esrj-105296	79	16	cross	cross	NOUN
esrj-105296	79	17	-	-	NOUN
esrj-105296	79	18	validation	validation	NOUN
esrj-105296	79	19	.	.	PUNCT
esrj-105296	80	1	2.3	2.3	NUM
esrj-105296	80	2	.	.	PUNCT
esrj-105296	81	1	convolutional	convolutional	ADJ
esrj-105296	81	2	neural	neural	ADJ
esrj-105296	81	3	networks	network	NOUN
esrj-105296	81	4	(	(	PUNCT
esrj-105296	81	5	cnn	cnn	PROPN
esrj-105296	81	6	)	)	PUNCT
esrj-105296	81	7	cnn	cnn	PROPN
esrj-105296	81	8	is	be	AUX
esrj-105296	81	9	a	a	DET
esrj-105296	81	10	specialized	specialized	ADJ
esrj-105296	81	11	neural	neural	ADJ
esrj-105296	81	12	network	network	NOUN
esrj-105296	81	13	structure	structure	NOUN
esrj-105296	81	14	for	for	ADP
esrj-105296	81	15	processing	processing	NOUN
esrj-105296	81	16	data	datum	NOUN
esrj-105296	81	17	organized	organize	VERB
esrj-105296	81	18	in	in	ADP
esrj-105296	81	19	a	a	DET
esrj-105296	81	20	grid	grid	NOUN
esrj-105296	81	21	-	-	PUNCT
esrj-105296	81	22	like	like	ADJ
esrj-105296	81	23	topology	topology	NOUN
esrj-105296	81	24	,	,	PUNCT
esrj-105296	81	25	such	such	ADJ
esrj-105296	81	26	as	as	ADP
esrj-105296	81	27	a	a	DET
esrj-105296	81	28	digital	digital	ADJ
esrj-105296	81	29	image	image	NOUN
esrj-105296	81	30	formed	form	VERB
esrj-105296	81	31	by	by	ADP
esrj-105296	81	32	three	three	NUM
esrj-105296	81	33	2	2	NUM
esrj-105296	81	34	-	-	PUNCT
esrj-105296	81	35	dimensional	dimensional	ADJ
esrj-105296	81	36	(	(	PUNCT
esrj-105296	81	37	2d	2d	NOUN
esrj-105296	81	38	)	)	PUNCT
esrj-105296	81	39	arrays	array	VERB
esrj-105296	81	40	.	.	PUNCT
esrj-105296	82	1	unlike	unlike	ADP
esrj-105296	82	2	conventional	conventional	ADJ
esrj-105296	82	3	neural	neural	ADJ
esrj-105296	82	4	networks	network	NOUN
esrj-105296	82	5	that	that	PRON
esrj-105296	82	6	primarily	primarily	ADV
esrj-105296	82	7	utilize	utilize	VERB
esrj-105296	82	8	matrix	matrix	NOUN
esrj-105296	82	9	multiplication	multiplication	NOUN
esrj-105296	82	10	,	,	PUNCT
esrj-105296	82	11	cnn	cnn	PROPN
esrj-105296	82	12	contains	contain	VERB
esrj-105296	82	13	at	at	ADV
esrj-105296	82	14	least	least	ADV
esrj-105296	82	15	one	one	NUM
esrj-105296	82	16	convolution	convolution	NOUN
esrj-105296	82	17	(	(	PUNCT
esrj-105296	82	18	lecun	lecun	PROPN
esrj-105296	82	19	et	et	PROPN
esrj-105296	82	20	al	al	PROPN
esrj-105296	82	21	.	.	PROPN
esrj-105296	82	22	,	,	PUNCT
esrj-105296	82	23	1998	1998	NUM
esrj-105296	82	24	)	)	PUNCT
esrj-105296	82	25	.	.	PUNCT
esrj-105296	83	1	cnn	cnn	PROPN
esrj-105296	83	2	is	be	AUX
esrj-105296	83	3	designed	design	VERB
esrj-105296	83	4	to	to	PART
esrj-105296	83	5	handle	handle	VERB
esrj-105296	83	6	input	input	NOUN
esrj-105296	83	7	data	datum	NOUN
esrj-105296	83	8	in	in	ADP
esrj-105296	83	9	multiple	multiple	ADJ
esrj-105296	83	10	array	array	NOUN
esrj-105296	83	11	formats	format	NOUN
esrj-105296	83	12	,	,	PUNCT
esrj-105296	83	13	such	such	ADJ
esrj-105296	83	14	as	as	ADP
esrj-105296	83	15	1	1	NUM
esrj-105296	83	16	-	-	PUNCT
esrj-105296	83	17	dimensional	dimensional	ADJ
esrj-105296	83	18	data	datum	NOUN
esrj-105296	83	19	,	,	PUNCT
esrj-105296	83	20	including	include	VERB
esrj-105296	83	21	signals	signal	NOUN
esrj-105296	83	22	and	and	CCONJ
esrj-105296	83	23	sequences	sequence	NOUN
esrj-105296	83	24	;	;	PUNCT
esrj-105296	83	25	2d	2d	NUM
esrj-105296	83	26	data	datum	NOUN
esrj-105296	83	27	,	,	PUNCT
esrj-105296	83	28	including	include	VERB
esrj-105296	83	29	images	image	NOUN
esrj-105296	83	30	and	and	CCONJ
esrj-105296	83	31	audio	audio	NOUN
esrj-105296	83	32	spectrograms	spectrogram	NOUN
esrj-105296	83	33	;	;	PUNCT
esrj-105296	83	34	and	and	CCONJ
esrj-105296	83	35	3	3	X
esrj-105296	83	36	-	-	ADJ
esrj-105296	83	37	dimensional	dimensional	ADJ
esrj-105296	83	38	(	(	PUNCT
esrj-105296	83	39	3d	3d	NOUN
esrj-105296	83	40	)	)	PUNCT
esrj-105296	83	41	data	datum	NOUN
esrj-105296	83	42	,	,	PUNCT
esrj-105296	83	43	including	include	VERB
esrj-105296	83	44	video	video	NOUN
esrj-105296	83	45	and	and	CCONJ
esrj-105296	83	46	volumetric	volumetric	NOUN
esrj-105296	83	47	images	image	NOUN
esrj-105296	83	48	(	(	PUNCT
esrj-105296	83	49	lecun	lecun	PROPN
esrj-105296	83	50	et	et	PROPN
esrj-105296	83	51	al	al	PROPN
esrj-105296	83	52	.	.	PROPN
esrj-105296	83	53	,	,	PUNCT
esrj-105296	83	54	2015	2015	NUM
esrj-105296	83	55	)	)	PUNCT
esrj-105296	83	56	.	.	PUNCT
esrj-105296	84	1	cnn	cnn	PROPN
esrj-105296	84	2	is	be	AUX
esrj-105296	84	3	typically	typically	ADV
esrj-105296	84	4	consisting	consist	VERB
esrj-105296	84	5	of	of	ADP
esrj-105296	84	6	three	three	NUM
esrj-105296	84	7	fundamental	fundamental	ADJ
esrj-105296	84	8	components	component	NOUN
esrj-105296	84	9	:	:	PUNCT
esrj-105296	84	10	convolution	convolution	NOUN
esrj-105296	84	11	layers	layer	NOUN
esrj-105296	84	12	,	,	PUNCT
esrj-105296	84	13	pooling	pool	VERB
esrj-105296	84	14	layers	layer	NOUN
esrj-105296	84	15	,	,	PUNCT
esrj-105296	84	16	and	and	CCONJ
esrj-105296	84	17	fully	fully	ADV
esrj-105296	84	18	connected	connected	ADJ
esrj-105296	84	19	layers	layer	NOUN
esrj-105296	84	20	,	,	PUNCT
esrj-105296	84	21	each	each	PRON
esrj-105296	84	22	serving	serve	VERB
esrj-105296	84	23	different	different	ADJ
esrj-105296	84	24	roles	role	NOUN
esrj-105296	84	25	(	(	PUNCT
esrj-105296	84	26	li	li	PROPN
esrj-105296	84	27	et	et	PROPN
esrj-105296	84	28	al	al	PROPN
esrj-105296	84	29	.	.	PROPN
esrj-105296	84	30	,	,	PUNCT
esrj-105296	84	31	2018	2018	NUM
esrj-105296	84	32	)	)	PUNCT
esrj-105296	84	33	.	.	PUNCT
esrj-105296	85	1	within	within	ADP
esrj-105296	85	2	the	the	DET
esrj-105296	85	3	convolution	convolution	NOUN
esrj-105296	85	4	layer	layer	NOUN
esrj-105296	85	5	,	,	PUNCT
esrj-105296	85	6	input	input	NOUN
esrj-105296	85	7	data	datum	NOUN
esrj-105296	85	8	subjects	subject	NOUN
esrj-105296	85	9	to	to	PART
esrj-105296	85	10	convolution	convolution	VERB
esrj-105296	85	11	operation	operation	NOUN
esrj-105296	85	12	with	with	ADP
esrj-105296	85	13	learnable	learnable	ADJ
esrj-105296	85	14	kernels	kernel	NOUN
esrj-105296	85	15	,	,	PUNCT
esrj-105296	85	16	followed	follow	VERB
esrj-105296	85	17	by	by	ADP
esrj-105296	85	18	an	an	DET
esrj-105296	85	19	activation	activation	NOUN
esrj-105296	85	20	function	function	NOUN
esrj-105296	85	21	to	to	PART
esrj-105296	85	22	generate	generate	VERB
esrj-105296	85	23	output	output	NOUN
esrj-105296	85	24	feature	feature	NOUN
esrj-105296	85	25	maps	map	NOUN
esrj-105296	85	26	.	.	PUNCT
esrj-105296	86	1	the	the	DET
esrj-105296	86	2	2d	2d	PROPN
esrj-105296	86	3	and	and	CCONJ
esrj-105296	86	4	3d	3d	NUM
esrj-105296	86	5	convolution	convolution	NOUN
esrj-105296	86	6	operations	operation	NOUN
esrj-105296	86	7	can	can	AUX
esrj-105296	86	8	be	be	AUX
esrj-105296	86	9	mathematically	mathematically	ADV
esrj-105296	86	10	represented	represent	VERB
esrj-105296	86	11	as	as	ADP
esrj-105296	86	12	equation	equation	NOUN
esrj-105296	86	13	(	(	PUNCT
esrj-105296	86	14	2	2	NUM
esrj-105296	86	15	)	)	PUNCT
esrj-105296	86	16	and	and	CCONJ
esrj-105296	86	17	equation	equation	NOUN
esrj-105296	86	18	(	(	PUNCT
esrj-105296	86	19	3	3	NUM
esrj-105296	86	20	)	)	PUNCT
esrj-105296	86	21	,	,	PUNCT
esrj-105296	86	22	respectively	respectively	ADV
esrj-105296	86	23	.	.	PUNCT
esrj-105296	87	1	=	=	PUNCT
esrj-105296	88	1	+	+	PUNCT
esrj-105296	89	1	=	=	SYM
esrj-105296	89	2	0	0	NUM
esrj-105296	89	3	−1	−1	NOUN
esrj-105296	89	4	=	=	NOUN
esrj-105296	89	5	0	0	NUM
esrj-105296	89	6	−1	−1	NOUN
esrj-105296	89	7	=	=	NOUN
esrj-105296	89	8	0	0	NUM
esrj-105296	89	9	−1	−1	NOUN
esrj-105296	89	10	−1	−1	NOUN
esrj-105296	89	11	(	(	PUNCT
esrj-105296	89	12	)	)	PUNCT
esrj-105296	89	13	+	+	ADJ
esrj-105296	89	14	(	(	PUNCT
esrj-105296	89	15	)	)	PUNCT
esrj-105296	89	16	+	+	ADJ
esrj-105296	89	17	(	(	PUNCT
esrj-105296	89	18	)	)	PUNCT
esrj-105296	89	19	(	(	PUNCT
esrj-105296	89	20	)	)	PUNCT
esrj-105296	89	21	(	(	PUNCT
esrj-105296	89	22	2	2	X
esrj-105296	89	23	)	)	PUNCT
esrj-105296	89	24	=	=	PUNCT
esrj-105296	90	1	+	+	PUNCT
esrj-105296	90	2	=	=	SYM
esrj-105296	90	3	0	0	NUM
esrj-105296	90	4	−1	−1	NOUN
esrj-105296	90	5	=	=	NOUN
esrj-105296	90	6	0	0	NUM
esrj-105296	90	7	−1	−1	NOUN
esrj-105296	90	8	=	=	NOUN
esrj-105296	90	9	0	0	NUM
esrj-105296	90	10	−1	−1	NOUN
esrj-105296	90	11	=	=	NOUN
esrj-105296	90	12	0	0	NUM
esrj-105296	90	13	−1	−1	NOUN
esrj-105296	90	14	−1	−1	NOUN
esrj-105296	90	15	(	(	PUNCT
esrj-105296	90	16	)	)	PUNCT
esrj-105296	91	1	+	+	ADJ
esrj-105296	91	2	(	(	PUNCT
esrj-105296	91	3	)	)	PUNCT
esrj-105296	91	4	+	+	ADJ
esrj-105296	91	5	(	(	PUNCT
esrj-105296	91	6	)	)	PUNCT
esrj-105296	91	7	+	+	ADJ
esrj-105296	91	8	(	(	PUNCT
esrj-105296	91	9	)	)	PUNCT
esrj-105296	91	10	(	(	PUNCT
esrj-105296	91	11	)	)	PUNCT
esrj-105296	91	12	(	(	PUNCT
esrj-105296	91	13	3	3	X
esrj-105296	91	14	)	)	PUNCT
esrj-105296	91	15	where	where	SCONJ
esrj-105296	91	16	v	v	NOUN
esrj-105296	91	17	denotes	denote	VERB
esrj-105296	91	18	the	the	DET
esrj-105296	91	19	output	output	NOUN
esrj-105296	91	20	variable	variable	NOUN
esrj-105296	91	21	in	in	ADP
esrj-105296	91	22	the	the	DET
esrj-105296	91	23	feature	feature	NOUN
esrj-105296	91	24	map	map	NOUN
esrj-105296	91	25	.	.	PUNCT
esrj-105296	92	1	s	s	X
esrj-105296	92	2	,	,	PUNCT
esrj-105296	92	3	q	q	X
esrj-105296	92	4	,	,	PUNCT
esrj-105296	92	5	and	and	CCONJ
esrj-105296	92	6	r	r	NOUN
esrj-105296	92	7	represent	represent	VERB
esrj-105296	92	8	spectral	spectral	ADJ
esrj-105296	92	9	and	and	CCONJ
esrj-105296	92	10	spatial	spatial	ADJ
esrj-105296	92	11	dimensions	dimension	NOUN
esrj-105296	92	12	of	of	ADP
esrj-105296	92	13	the	the	DET
esrj-105296	92	14	kernel	kernel	NOUN
esrj-105296	92	15	,	,	PUNCT
esrj-105296	92	16	respectively	respectively	ADV
esrj-105296	92	17	.	.	PUNCT
esrj-105296	93	1	the	the	DET
esrj-105296	93	2	indices	index	NOUN
esrj-105296	93	3	of	of	ADP
esrj-105296	93	4	the	the	DET
esrj-105296	93	5	kernel	kernel	NOUN
esrj-105296	93	6	are	be	AUX
esrj-105296	93	7	indicated	indicate	VERB
esrj-105296	93	8	by	by	ADP
esrj-105296	93	9	(	(	PUNCT
esrj-105296	93	10	s	s	X
esrj-105296	93	11	,	,	PUNCT
esrj-105296	93	12	q	q	NOUN
esrj-105296	93	13	,	,	PUNCT
esrj-105296	93	14	r	r	NOUN
esrj-105296	93	15	)	)	PUNCT
esrj-105296	93	16	,	,	PUNCT
esrj-105296	93	17	while	while	SCONJ
esrj-105296	93	18	(	(	PUNCT
esrj-105296	93	19	z	z	NOUN
esrj-105296	93	20	,	,	PUNCT
esrj-105296	93	21	x	x	NOUN
esrj-105296	93	22	,	,	PUNCT
esrj-105296	93	23	y	y	PROPN
esrj-105296	93	24	)	)	PUNCT
esrj-105296	93	25	represent	represent	VERB
esrj-105296	93	26	the	the	DET
esrj-105296	93	27	indices	index	NOUN
esrj-105296	93	28	of	of	ADP
esrj-105296	93	29	the	the	DET
esrj-105296	93	30	feature	feature	NOUN
esrj-105296	93	31	map	map	NOUN
esrj-105296	93	32	,	,	PUNCT
esrj-105296	93	33	corresponding	correspond	VERB
esrj-105296	93	34	to	to	ADP
esrj-105296	93	35	the	the	DET
esrj-105296	93	36	two	two	NUM
esrj-105296	93	37	spatial	spatial	ADJ
esrj-105296	93	38	and	and	CCONJ
esrj-105296	93	39	one	one	NUM
esrj-105296	93	40	spectral	spectral	ADJ
esrj-105296	93	41	dimension	dimension	NOUN
esrj-105296	93	42	,	,	PUNCT
esrj-105296	93	43	respectively	respectively	ADV
esrj-105296	93	44	.	.	PUNCT
esrj-105296	94	1	the	the	DET
esrj-105296	94	2	kernel	kernel	PROPN
esrj-105296	94	3	parameters	parameter	NOUN
esrj-105296	94	4	are	be	AUX
esrj-105296	94	5	represented	represent	VERB
esrj-105296	94	6	by	by	ADP
esrj-105296	94	7	k	k	PROPN
esrj-105296	94	8	,	,	PUNCT
esrj-105296	94	9	while	while	SCONJ
esrj-105296	94	10	the	the	DET
esrj-105296	94	11	indexes	index	NOUN
esrj-105296	94	12	of	of	ADP
esrj-105296	94	13	the	the	DET
esrj-105296	94	14	input	input	NOUN
esrj-105296	94	15	layer	layer	NOUN
esrj-105296	94	16	,	,	PUNCT
esrj-105296	94	17	output	output	NOUN
esrj-105296	94	18	layer	layer	NOUN
esrj-105296	94	19	,	,	PUNCT
esrj-105296	94	20	and	and	CCONJ
esrj-105296	94	21	feature	feature	NOUN
esrj-105296	94	22	maps	map	NOUN
esrj-105296	94	23	are	be	AUX
esrj-105296	94	24	denoted	denote	VERB
esrj-105296	94	25	by	by	ADP
esrj-105296	94	26	i	i	PROPN
esrj-105296	94	27	,	,	PUNCT
esrj-105296	94	28	j	j	PROPN
esrj-105296	94	29	,	,	PUNCT
esrj-105296	94	30	and	and	CCONJ
esrj-105296	94	31	p	p	X
esrj-105296	94	32	,	,	PUNCT
esrj-105296	94	33	respectively	respectively	ADV
esrj-105296	94	34	.	.	PUNCT
esrj-105296	95	1	p	p	X
esrj-105296	95	2	indicates	indicate	VERB
esrj-105296	95	3	the	the	DET
esrj-105296	95	4	number	number	NOUN
esrj-105296	95	5	of	of	ADP
esrj-105296	95	6	feature	feature	NOUN
esrj-105296	95	7	maps	map	NOUN
esrj-105296	95	8	.	.	PUNCT
esrj-105296	96	1	the	the	DET
esrj-105296	96	2	bias	bias	NOUN
esrj-105296	96	3	term	term	NOUN
esrj-105296	96	4	is	be	AUX
esrj-105296	96	5	denoted	denote	VERB
esrj-105296	96	6	by	by	ADP
esrj-105296	96	7	b.	b.	PROPN
esrj-105296	96	8	the	the	DET
esrj-105296	96	9	activation	activation	NOUN
esrj-105296	96	10	function	function	NOUN
esrj-105296	96	11	is	be	AUX
esrj-105296	96	12	represented	represent	VERB
esrj-105296	96	13	by	by	ADP
esrj-105296	96	14	f	f	PROPN
esrj-105296	96	15	,	,	PUNCT
esrj-105296	96	16	was	be	AUX
esrj-105296	96	17	selected	select	VERB
esrj-105296	96	18	as	as	ADP
esrj-105296	96	19	a	a	DET
esrj-105296	96	20	rectified	rectified	ADJ
esrj-105296	96	21	linear	linear	NOUN
esrj-105296	96	22	unit	unit	NOUN
esrj-105296	96	23	(	(	PUNCT
esrj-105296	96	24	relu	relu	NOUN
esrj-105296	96	25	)	)	PUNCT
esrj-105296	96	26	in	in	ADP
esrj-105296	96	27	this	this	DET
esrj-105296	96	28	study	study	NOUN
esrj-105296	96	29	due	due	ADP
esrj-105296	96	30	to	to	ADP
esrj-105296	96	31	its	its	PRON
esrj-105296	96	32	faster	fast	ADJ
esrj-105296	96	33	computing	computing	NOUN
esrj-105296	96	34	rate	rate	NOUN
esrj-105296	96	35	without	without	ADP
esrj-105296	96	36	a	a	DET
esrj-105296	96	37	gradient	gradient	ADJ
esrj-105296	96	38	diffusion	diffusion	NOUN
esrj-105296	96	39	problem	problem	NOUN
esrj-105296	96	40	.	.	PUNCT
esrj-105296	97	1	the	the	DET
esrj-105296	97	2	relu	relu	NOUN
esrj-105296	97	3	function	function	NOUN
esrj-105296	97	4	is	be	AUX
esrj-105296	97	5	defined	define	VERB
esrj-105296	97	6	as	as	ADP
esrj-105296	97	7	follows	follow	VERB
esrj-105296	97	8	(	(	PUNCT
esrj-105296	97	9	nair	nair	PROPN
esrj-105296	97	10	&	&	CCONJ
esrj-105296	97	11	hinton	hinton	PROPN
esrj-105296	97	12	,	,	PUNCT
esrj-105296	97	13	2010	2010	NUM
esrj-105296	97	14	;	;	PUNCT
esrj-105296	97	15	wang	wang	PROPN
esrj-105296	97	16	et	et	PROPN
esrj-105296	97	17	al	al	PROPN
esrj-105296	97	18	.	.	PROPN
esrj-105296	97	19	,	,	PUNCT
esrj-105296	97	20	2020	2020	NUM
esrj-105296	97	21	):	):	PUNCT
esrj-105296	98	1	f	f	PROPN
esrj-105296	98	2	(	(	PUNCT
esrj-105296	98	3	x	x	NOUN
esrj-105296	98	4	)	)	PUNCT
esrj-105296	98	5	=	=	SYM
esrj-105296	98	6	(	(	PUNCT
esrj-105296	98	7	0	0	NUM
esrj-105296	98	8	,	,	PUNCT
esrj-105296	98	9	x	x	NOUN
esrj-105296	98	10	)	)	PUNCT
esrj-105296	98	11	(	(	PUNCT
esrj-105296	98	12	4	4	NUM
esrj-105296	98	13	)	)	PUNCT
esrj-105296	98	14	in	in	ADP
esrj-105296	98	15	most	most	ADJ
esrj-105296	98	16	cnn	cnn	PROPN
esrj-105296	98	17	architectures	architecture	NOUN
esrj-105296	98	18	,	,	PUNCT
esrj-105296	98	19	a	a	DET
esrj-105296	98	20	pooling	pool	VERB
esrj-105296	98	21	layer	layer	NOUN
esrj-105296	98	22	follows	follow	VERB
esrj-105296	98	23	a	a	DET
esrj-105296	98	24	convolution	convolution	NOUN
esrj-105296	98	25	layer	layer	NOUN
esrj-105296	98	26	,	,	PUNCT
esrj-105296	98	27	reducing	reduce	VERB
esrj-105296	98	28	the	the	DET
esrj-105296	98	29	dimensionality	dimensionality	NOUN
esrj-105296	98	30	of	of	ADP
esrj-105296	98	31	feature	feature	NOUN
esrj-105296	98	32	maps	map	NOUN
esrj-105296	98	33	.	.	PUNCT
esrj-105296	99	1	two	two	NUM
esrj-105296	99	2	common	common	ADJ
esrj-105296	99	3	pooling	pooling	NOUN
esrj-105296	99	4	operations	operation	NOUN
esrj-105296	99	5	are	be	AUX
esrj-105296	99	6	commonly	commonly	ADV
esrj-105296	99	7	used	use	VERB
esrj-105296	99	8	,	,	PUNCT
esrj-105296	99	9	named	name	VERB
esrj-105296	99	10	max	max	PROPN
esrj-105296	99	11	pooling	pooling	NOUN
esrj-105296	99	12	and	and	CCONJ
esrj-105296	99	13	average	average	ADJ
esrj-105296	99	14	pooling	pooling	NOUN
esrj-105296	99	15	.	.	PUNCT
esrj-105296	100	1	following	follow	VERB
esrj-105296	100	2	the	the	DET
esrj-105296	100	3	pooling	pool	VERB
esrj-105296	100	4	layer	layer	NOUN
esrj-105296	100	5	,	,	PUNCT
esrj-105296	100	6	the	the	DET
esrj-105296	100	7	final	final	ADJ
esrj-105296	100	8	fundamental	fundamental	ADJ
esrj-105296	100	9	cnn	cnn	PROPN
esrj-105296	100	10	layer	layer	NOUN
esrj-105296	100	11	is	be	AUX
esrj-105296	100	12	the	the	DET
esrj-105296	100	13	fully	fully	ADV
esrj-105296	100	14	connected	connect	VERB
esrj-105296	100	15	layer	layer	NOUN
esrj-105296	100	16	,	,	PUNCT
esrj-105296	100	17	similar	similar	ADJ
esrj-105296	100	18	to	to	ADP
esrj-105296	100	19	backpropagation	backpropagation	NOUN
esrj-105296	100	20	in	in	ADP
esrj-105296	100	21	traditional	traditional	ADJ
esrj-105296	100	22	neural	neural	ADJ
esrj-105296	100	23	networks	network	NOUN
esrj-105296	100	24	.	.	PUNCT
esrj-105296	101	1	the	the	DET
esrj-105296	101	2	learned	learn	VERB
esrj-105296	101	3	features	feature	NOUN
esrj-105296	101	4	extracted	extract	VERB
esrj-105296	101	5	from	from	ADP
esrj-105296	101	6	the	the	DET
esrj-105296	101	7	input	input	NOUN
esrj-105296	101	8	image	image	NOUN
esrj-105296	101	9	by	by	ADP
esrj-105296	101	10	the	the	DET
esrj-105296	101	11	cnn	cnn	PROPN
esrj-105296	101	12	serve	serve	VERB
esrj-105296	101	13	as	as	ADP
esrj-105296	101	14	the	the	DET
esrj-105296	101	15	output	output	NOUN
esrj-105296	101	16	of	of	ADP
esrj-105296	101	17	the	the	DET
esrj-105296	101	18	fully	fully	ADV
esrj-105296	101	19	connected	connected	ADJ
esrj-105296	101	20	layer	layer	NOUN
esrj-105296	101	21	.	.	PUNCT
esrj-105296	102	1	connecting	connect	VERB
esrj-105296	102	2	this	this	DET
esrj-105296	102	3	output	output	NOUN
esrj-105296	102	4	to	to	ADP
esrj-105296	102	5	a	a	DET
esrj-105296	102	6	learning	learning	NOUN
esrj-105296	102	7	classifier	classifier	NOUN
esrj-105296	102	8	,	,	PUNCT
esrj-105296	102	9	such	such	ADJ
esrj-105296	102	10	as	as	ADP
esrj-105296	102	11	softmax	softmax	NOUN
esrj-105296	102	12	,	,	PUNCT
esrj-105296	102	13	the	the	DET
esrj-105296	102	14	classification	classification	NOUN
esrj-105296	102	15	process	process	NOUN
esrj-105296	102	16	is	be	AUX
esrj-105296	102	17	implemented	implement	VERB
esrj-105296	102	18	(	(	PUNCT
esrj-105296	102	19	krizhevsky	krizhevsky	NOUN
esrj-105296	102	20	et	et	PROPN
esrj-105296	102	21	al	al	PROPN
esrj-105296	102	22	.	.	PROPN
esrj-105296	102	23	,	,	PUNCT
esrj-105296	102	24	2012	2012	NUM
esrj-105296	102	25	)	)	PUNCT
esrj-105296	102	26	.	.	PUNCT
esrj-105296	103	1	in	in	ADP
esrj-105296	103	2	this	this	DET
esrj-105296	103	3	study	study	NOUN
esrj-105296	103	4	,	,	PUNCT
esrj-105296	103	5	the	the	DET
esrj-105296	103	6	hybridsn	hybridsn	PROPN
esrj-105296	103	7	cnn	cnn	PROPN
esrj-105296	103	8	model	model	PROPN
esrj-105296	103	9	,	,	PUNCT
esrj-105296	103	10	introduced	introduce	VERB
esrj-105296	103	11	by	by	ADP
esrj-105296	103	12	roy	roy	PROPN
esrj-105296	103	13	et	et	PROPN
esrj-105296	103	14	al	al	PROPN
esrj-105296	103	15	.	.	PROPN
esrj-105296	103	16	(	(	PUNCT
esrj-105296	103	17	2019	2019	NUM
esrj-105296	103	18	)	)	PUNCT
esrj-105296	103	19	,	,	PUNCT
esrj-105296	103	20	was	be	AUX
esrj-105296	103	21	employed	employ	VERB
esrj-105296	103	22	for	for	ADP
esrj-105296	103	23	classifying	classify	VERB
esrj-105296	103	24	hsi	hsi	PROPN
esrj-105296	103	25	images	image	NOUN
esrj-105296	103	26	.	.	PUNCT
esrj-105296	104	1	the	the	DET
esrj-105296	104	2	structure	structure	NOUN
esrj-105296	104	3	of	of	ADP
esrj-105296	104	4	the	the	DET
esrj-105296	104	5	hybridsn	hybridsn	PROPN
esrj-105296	104	6	cnn	cnn	PROPN
esrj-105296	104	7	model	model	NOUN
esrj-105296	104	8	utilized	utilize	VERB
esrj-105296	104	9	in	in	ADP
esrj-105296	104	10	this	this	DET
esrj-105296	104	11	study	study	NOUN
esrj-105296	104	12	is	be	AUX
esrj-105296	104	13	shown	show	VERB
esrj-105296	104	14	in	in	ADP
esrj-105296	104	15	figure	figure	NOUN
esrj-105296	104	16	1	1	NUM
esrj-105296	104	17	.	.	PUNCT
esrj-105296	104	18	upon	upon	SCONJ
esrj-105296	104	19	reviewing	review	VERB
esrj-105296	104	20	some	some	DET
esrj-105296	104	21	studies	study	NOUN
esrj-105296	104	22	in	in	ADP
esrj-105296	104	23	the	the	DET
esrj-105296	104	24	literature	literature	NOUN
esrj-105296	104	25	,	,	PUNCT
esrj-105296	104	26	it	it	PRON
esrj-105296	104	27	has	have	AUX
esrj-105296	104	28	been	be	AUX
esrj-105296	104	29	observed	observe	VERB
esrj-105296	104	30	that	that	SCONJ
esrj-105296	104	31	activation	activation	NOUN
esrj-105296	104	32	functions	function	NOUN
esrj-105296	104	33	and	and	CCONJ
esrj-105296	104	34	optimizers	optimizer	NOUN
esrj-105296	104	35	have	have	VERB
esrj-105296	104	36	an	an	DET
esrj-105296	104	37	impact	impact	NOUN
esrj-105296	104	38	on	on	ADP
esrj-105296	104	39	the	the	DET
esrj-105296	104	40	performance	performance	NOUN
esrj-105296	104	41	of	of	ADP
esrj-105296	104	42	cnn	cnn	PROPN
esrj-105296	104	43	models	model	NOUN
esrj-105296	104	44	.	.	PUNCT
esrj-105296	105	1	for	for	ADP
esrj-105296	105	2	example	example	NOUN
esrj-105296	105	3	,	,	PUNCT
esrj-105296	105	4	some	some	DET
esrj-105296	105	5	studies	study	NOUN
esrj-105296	105	6	observed	observe	VERB
esrj-105296	105	7	that	that	SCONJ
esrj-105296	105	8	the	the	DET
esrj-105296	105	9	adamax	adamax	PROPN
esrj-105296	105	10	optimizer	optimizer	NOUN
esrj-105296	105	11	improved	improve	VERB
esrj-105296	105	12	the	the	DET
esrj-105296	105	13	classification	classification	NOUN
esrj-105296	105	14	performance	performance	NOUN
esrj-105296	105	15	in	in	ADP
esrj-105296	105	16	the	the	DET
esrj-105296	105	17	cnn	cnn	PROPN
esrj-105296	105	18	model	model	NOUN
esrj-105296	105	19	(	(	PUNCT
esrj-105296	105	20	ariff	ariff	PROPN
esrj-105296	105	21	&	&	CCONJ
esrj-105296	105	22	ismail	ismail	PROPN
esrj-105296	105	23	,	,	PUNCT
esrj-105296	105	24	2023	2023	NUM
esrj-105296	105	25	;	;	PUNCT
esrj-105296	105	26	seyrek	seyrek	NOUN
esrj-105296	105	27	&	&	CCONJ
esrj-105296	105	28	uysal	uysal	PROPN
esrj-105296	105	29	,	,	PUNCT
esrj-105296	105	30	2024	2024	NUM
esrj-105296	105	31	;	;	PUNCT
esrj-105296	105	32	vani	vani	PROPN
esrj-105296	105	33	&	&	CCONJ
esrj-105296	105	34	rao	rao	PROPN
esrj-105296	105	35	,	,	PUNCT
esrj-105296	105	36	2019	2019	NUM
esrj-105296	105	37	)	)	PUNCT
esrj-105296	105	38	.	.	PUNCT
esrj-105296	106	1	furthermore	furthermore	ADV
esrj-105296	106	2	,	,	PUNCT
esrj-105296	106	3	there	there	PRON
esrj-105296	106	4	are	be	VERB
esrj-105296	106	5	research	research	NOUN
esrj-105296	106	6	findings	finding	NOUN
esrj-105296	106	7	indicating	indicate	VERB
esrj-105296	106	8	that	that	SCONJ
esrj-105296	106	9	mish	mish	ADJ
esrj-105296	106	10	activation	activation	NOUN
esrj-105296	106	11	function	function	NOUN
esrj-105296	106	12	(	(	PUNCT
esrj-105296	106	13	equation	equation	NOUN
esrj-105296	106	14	5	5	NUM
esrj-105296	106	15	)	)	PUNCT
esrj-105296	106	16	also	also	ADV
esrj-105296	106	17	enhances	enhance	VERB
esrj-105296	106	18	classification	classification	NOUN
esrj-105296	106	19	performance	performance	NOUN
esrj-105296	106	20	(	(	PUNCT
esrj-105296	106	21	misra	misra	ADJ
esrj-105296	106	22	,	,	PUNCT
esrj-105296	106	23	2019	2019	NUM
esrj-105296	106	24	;	;	PUNCT
esrj-105296	106	25	seyrek	seyrek	NOUN
esrj-105296	106	26	&	&	CCONJ
esrj-105296	106	27	uysal	uysal	PROPN
esrj-105296	106	28	,	,	PUNCT
esrj-105296	106	29	2024	2024	NUM
esrj-105296	106	30	;	;	PUNCT
esrj-105296	107	1	zhang	zhang	PROPN
esrj-105296	107	2	et	et	PROPN
esrj-105296	107	3	al	al	PROPN
esrj-105296	107	4	.	.	PROPN
esrj-105296	107	5	,	,	PUNCT
esrj-105296	107	6	2019	2019	NUM
esrj-105296	107	7	)	)	PUNCT
esrj-105296	107	8	.	.	PUNCT
esrj-105296	108	1	based	base	VERB
esrj-105296	108	2	on	on	ADP
esrj-105296	108	3	this	this	DET
esrj-105296	108	4	information	information	NOUN
esrj-105296	108	5	,	,	PUNCT
esrj-105296	108	6	the	the	DET
esrj-105296	108	7	hybridsn	hybridsn	NOUN
esrj-105296	108	8	cnn	cnn	PROPN
esrj-105296	108	9	architecture	architecture	NOUN
esrj-105296	108	10	was	be	AUX
esrj-105296	108	11	modified	modify	VERB
esrj-105296	108	12	to	to	PART
esrj-105296	108	13	utilize	utilize	VERB
esrj-105296	108	14	the	the	DET
esrj-105296	108	15	adamax	adamax	NOUN
esrj-105296	108	16	optimizer	optimizer	NOUN
esrj-105296	108	17	and	and	CCONJ
esrj-105296	108	18	mish	mish	ADJ
esrj-105296	108	19	activation	activation	NOUN
esrj-105296	108	20	function	function	NOUN
esrj-105296	108	21	.	.	PUNCT
esrj-105296	109	1	consequently	consequently	ADV
esrj-105296	109	2	,	,	PUNCT
esrj-105296	109	3	the	the	DET
esrj-105296	109	4	resulting	result	VERB
esrj-105296	109	5	cnn	cnn	NOUN
esrj-105296	109	6	architecture	architecture	NOUN
esrj-105296	109	7	is	be	AUX
esrj-105296	109	8	referred	refer	VERB
esrj-105296	109	9	to	to	ADP
esrj-105296	109	10	as	as	ADP
esrj-105296	109	11	the	the	DET
esrj-105296	109	12	modified	modify	VERB
esrj-105296	109	13	hybridsn	hybridsn	PROPN
esrj-105296	109	14	cnn	cnn	PROPN
esrj-105296	109	15	for	for	ADP
esrj-105296	109	16	the	the	DET
esrj-105296	109	17	remainder	remainder	NOUN
esrj-105296	109	18	of	of	ADP
esrj-105296	109	19	the	the	DET
esrj-105296	109	20	study	study	NOUN
esrj-105296	109	21	.	.	PUNCT
esrj-105296	110	1	mish(x	mish(x	X
esrj-105296	110	2	)	)	PUNCT
esrj-105296	110	3	=	=	PUNCT
esrj-105296	111	1	x	x	SYM
esrj-105296	111	2	×	×	PROPN
esrj-105296	111	3	tanh	tanh	PROPN
esrj-105296	111	4	tanh	tanh	PROPN
esrj-105296	111	5	(	(	PUNCT
esrj-105296	111	6	log	log	PROPN
esrj-105296	111	7	log	log	NOUN
esrj-105296	111	8	(	(	PUNCT
esrj-105296	111	9	1	1	NUM
esrj-105296	111	10	+	+	CCONJ
esrj-105296	111	11	ex	ex	X
esrj-105296	111	12	)	)	PUNCT
esrj-105296	111	13	)	)	PUNCT
esrj-105296	111	14	(	(	PUNCT
esrj-105296	111	15	5	5	X
esrj-105296	111	16	)	)	PUNCT
esrj-105296	111	17	the	the	DET
esrj-105296	111	18	model	model	NOUN
esrj-105296	111	19	comprises	comprise	VERB
esrj-105296	111	20	three	three	NUM
esrj-105296	111	21	3d	3d	NUM
esrj-105296	111	22	convolution	convolution	NOUN
esrj-105296	111	23	layers	layer	NOUN
esrj-105296	111	24	,	,	PUNCT
esrj-105296	111	25	enabling	enable	VERB
esrj-105296	111	26	simultaneous	simultaneous	ADJ
esrj-105296	111	27	extraction	extraction	NOUN
esrj-105296	111	28	of	of	ADP
esrj-105296	111	29	both	both	CCONJ
esrj-105296	111	30	spectral	spectral	ADJ
esrj-105296	111	31	and	and	CCONJ
esrj-105296	111	32	spatial	spatial	ADJ
esrj-105296	111	33	features	feature	NOUN
esrj-105296	111	34	.	.	PUNCT
esrj-105296	112	1	subsequently	subsequently	ADV
esrj-105296	112	2	,	,	PUNCT
esrj-105296	112	3	a	a	DET
esrj-105296	112	4	2d	2d	NUM
esrj-105296	112	5	convolution	convolution	NOUN
esrj-105296	112	6	layer	layer	NOUN
esrj-105296	112	7	performs	perform	VERB
esrj-105296	112	8	exclusively	exclusively	ADV
esrj-105296	112	9	spatial	spatial	ADJ
esrj-105296	112	10	feature	feature	NOUN
esrj-105296	112	11	extraction	extraction	NOUN
esrj-105296	112	12	.	.	PUNCT
esrj-105296	113	1	this	this	PRON
esrj-105296	113	2	ensures	ensure	VERB
esrj-105296	113	3	the	the	DET
esrj-105296	113	4	effective	effective	ADJ
esrj-105296	113	5	extraction	extraction	NOUN
esrj-105296	113	6	of	of	ADP
esrj-105296	113	7	both	both	CCONJ
esrj-105296	113	8	spectral	spectral	ADJ
esrj-105296	113	9	and	and	CCONJ
esrj-105296	113	10	spatial	spatial	ADJ
esrj-105296	113	11	features	feature	NOUN
esrj-105296	113	12	from	from	ADP
esrj-105296	113	13	a	a	DET
esrj-105296	113	14	hyperspectral	hyperspectral	ADJ
esrj-105296	113	15	image	image	NOUN
esrj-105296	113	16	.	.	PUNCT
esrj-105296	114	1	during	during	ADP
esrj-105296	114	2	the	the	DET
esrj-105296	114	3	data	data	NOUN
esrj-105296	114	4	preparation	preparation	NOUN
esrj-105296	114	5	stage	stage	NOUN
esrj-105296	114	6	,	,	PUNCT
esrj-105296	114	7	the	the	DET
esrj-105296	114	8	hsi	hsi	PROPN
esrj-105296	114	9	image	image	NOUN
esrj-105296	114	10	subjected	subject	VERB
esrj-105296	114	11	to	to	ADP
esrj-105296	114	12	pca	pca	PROPN
esrj-105296	114	13	was	be	AUX
esrj-105296	114	14	partitioned	partition	VERB
esrj-105296	114	15	into	into	ADP
esrj-105296	114	16	small	small	ADJ
esrj-105296	114	17	overlapping	overlap	VERB
esrj-105296	114	18	3d	3d	NUM
esrj-105296	114	19	patches	patch	NOUN
esrj-105296	114	20	,	,	PUNCT
esrj-105296	114	21	each	each	PRON
esrj-105296	114	22	associated	associate	VERB
esrj-105296	114	23	with	with	ADP
esrj-105296	114	24	a	a	DET
esrj-105296	114	25	ground	ground	NOUN
esrj-105296	114	26	truth	truth	NOUN
esrj-105296	114	27	label	label	NOUN
esrj-105296	114	28	corresponding	correspond	VERB
esrj-105296	114	29	to	to	ADP
esrj-105296	114	30	the	the	DET
esrj-105296	114	31	central	central	ADJ
esrj-105296	114	32	pixel	pixel	NOUN
esrj-105296	114	33	.	.	PUNCT
esrj-105296	115	1	let	let	VERB
esrj-105296	115	2	’s	’s	PRON
esrj-105296	115	3	assume	assume	VERB
esrj-105296	115	4	that	that	SCONJ
esrj-105296	115	5	p	p	PROPN
esrj-105296	115	6	∈	∈	PROPN
esrj-105296	115	7	 	 	SPACE
esrj-105296	115	8	r(s×s×b	r(s×s×b	ADJ
esrj-105296	115	9	)	)	PUNCT
esrj-105296	115	10	,	,	PUNCT
esrj-105296	115	11	where	where	SCONJ
esrj-105296	115	12	s	s	NOUN
esrj-105296	115	13	represents	represent	VERB
esrj-105296	115	14	the	the	DET
esrj-105296	115	15	size	size	NOUN
esrj-105296	115	16	of	of	ADP
esrj-105296	115	17	the	the	DET
esrj-105296	115	18	3d	3d	PROPN
esrj-105296	115	19	patches	patch	NOUN
esrj-105296	115	20	and	and	CCONJ
esrj-105296	115	21	b	b	NOUN
esrj-105296	115	22	denotes	denote	NOUN
esrj-105296	115	23	the	the	DET
esrj-105296	115	24	number	number	NOUN
esrj-105296	115	25	of	of	ADP
esrj-105296	115	26	spectral	spectral	ADJ
esrj-105296	115	27	bands	band	NOUN
esrj-105296	115	28	of	of	ADP
esrj-105296	115	29	the	the	DET
esrj-105296	115	30	pca	pca	NOUN
esrj-105296	115	31	-	-	PUNCT
esrj-105296	115	32	transformed	transform	VERB
esrj-105296	115	33	hsi	hsi	PROPN
esrj-105296	115	34	data	data	PROPN
esrj-105296	115	35	.	.	PUNCT
esrj-105296	116	1	the	the	DET
esrj-105296	116	2	total	total	ADJ
esrj-105296	116	3	number	number	NOUN
esrj-105296	116	4	of	of	ADP
esrj-105296	116	5	3d	3d	PROPN
esrj-105296	116	6	patches	patch	NOUN
esrj-105296	116	7	generated	generate	VERB
esrj-105296	116	8	is	be	AUX
esrj-105296	116	9	(	(	PUNCT
esrj-105296	116	10	m	m	PROPN
esrj-105296	116	11	s	s	PART
esrj-105296	116	12	+	+	ADJ
esrj-105296	116	13	1	1	NUM
esrj-105296	116	14	)	)	PUNCT
esrj-105296	116	15	×	×	NOUN
esrj-105296	116	16	(	(	PUNCT
esrj-105296	116	17	n	n	NOUN
esrj-105296	116	18	s	s	NOUN
esrj-105296	116	19	+	+	NOUN
esrj-105296	116	20	1	1	NUM
esrj-105296	116	21	)	)	PUNCT
esrj-105296	116	22	,	,	PUNCT
esrj-105296	116	23	where	where	SCONJ
esrj-105296	116	24	m	m	NOUN
esrj-105296	116	25	is	be	AUX
esrj-105296	116	26	the	the	DET
esrj-105296	116	27	width	width	NOUN
esrj-105296	116	28	and	and	CCONJ
esrj-105296	116	29	n	n	PRON
esrj-105296	116	30	is	be	AUX
esrj-105296	116	31	the	the	DET
esrj-105296	116	32	height	height	NOUN
esrj-105296	116	33	of	of	ADP
esrj-105296	116	34	hsi	hsi	PROPN
esrj-105296	116	35	data	data	PROPN
esrj-105296	116	36	.	.	PUNCT
esrj-105296	117	1	during	during	ADP
esrj-105296	117	2	the	the	DET
esrj-105296	117	3	data	data	NOUN
esrj-105296	117	4	preparation	preparation	NOUN
esrj-105296	117	5	for	for	ADP
esrj-105296	117	6	training	training	NOUN
esrj-105296	117	7	and	and	CCONJ
esrj-105296	117	8	testing	test	VERB
esrj-105296	117	9	the	the	DET
esrj-105296	117	10	cnn	cnn	PROPN
esrj-105296	117	11	model	model	NOUN
esrj-105296	117	12	,	,	PUNCT
esrj-105296	117	13	3d	3d	PROPN
esrj-105296	117	14	patches	patch	NOUN
esrj-105296	117	15	with	with	ADP
esrj-105296	117	16	any	any	DET
esrj-105296	117	17	ground	ground	NOUN
esrj-105296	117	18	truth	truth	NOUN
esrj-105296	117	19	data	datum	NOUN
esrj-105296	117	20	corresponding	correspond	VERB
esrj-105296	117	21	to	to	ADP
esrj-105296	117	22	the	the	DET
esrj-105296	117	23	central	central	ADJ
esrj-105296	117	24	pixel	pixel	NOUN
esrj-105296	117	25	are	be	AUX
esrj-105296	117	26	excluded	exclude	VERB
esrj-105296	117	27	.	.	PUNCT
esrj-105296	118	1	figure	figure	NOUN
esrj-105296	118	2	1	1	NUM
esrj-105296	118	3	.	.	PUNCT
esrj-105296	119	1	the	the	DET
esrj-105296	119	2	modified	modify	VERB
esrj-105296	119	3	hybridsn	hybridsn	NOUN
esrj-105296	119	4	architecture	architecture	NOUN
esrj-105296	119	5	164	164	NUM
esrj-105296	119	6	eren	eren	PROPN
esrj-105296	119	7	can	can	AUX
esrj-105296	119	8	seyrek	seyrek	VERB
esrj-105296	119	9	,	,	PUNCT
esrj-105296	119	10	murat	murat	PROPN
esrj-105296	119	11	uysal	uysal	ADJ
esrj-105296	119	12	table	table	NOUN
esrj-105296	119	13	1	1	NUM
esrj-105296	119	14	.	.	PUNCT
esrj-105296	120	1	summary	summary	NOUN
esrj-105296	120	2	of	of	ADP
esrj-105296	120	3	the	the	DET
esrj-105296	120	4	cnn	cnn	PROPN
esrj-105296	120	5	models	model	NOUN
esrj-105296	120	6	for	for	ADP
esrj-105296	120	7	the	the	DET
esrj-105296	120	8	hyrank	hyrank	PROPN
esrj-105296	120	9	loukia	loukia	NOUN
esrj-105296	120	10	,	,	PUNCT
esrj-105296	120	11	dfc	dfc	PROPN
esrj-105296	120	12	13	13	NUM
esrj-105296	120	13	,	,	PUNCT
esrj-105296	120	14	and	and	CCONJ
esrj-105296	120	15	salinas	salinas	PROPN
esrj-105296	120	16	scene	scene	NOUN
esrj-105296	120	17	datasets	dataset	NOUN
esrj-105296	120	18	.	.	PUNCT
esrj-105296	121	1	cnn	cnn	PROPN
esrj-105296	121	2	architecture	architecture	NOUN
esrj-105296	121	3	model	model	NOUN
esrj-105296	121	4	summary	summary	NOUN
esrj-105296	121	5	layer	layer	NOUN
esrj-105296	121	6	output	output	NOUN
esrj-105296	121	7	shape	shape	NOUN
esrj-105296	121	8	hyrank	hyrank	NOUN
esrj-105296	121	9	loukia	loukia	NOUN
esrj-105296	121	10	:	:	PUNCT
esrj-105296	121	11	#	#	NOUN
esrj-105296	121	12	of	of	ADP
esrj-105296	121	13	parameters	parameter	NOUN
esrj-105296	121	14	dfc	dfc	PROPN
esrj-105296	121	15	13	13	NUM
esrj-105296	121	16	:	:	PUNCT
esrj-105296	121	17	#	#	NOUN
esrj-105296	121	18	of	of	ADP
esrj-105296	121	19	parameters	parameter	NOUN
esrj-105296	121	20	salinas	salinas	PROPN
esrj-105296	121	21	scene	scene	NOUN
esrj-105296	121	22	:	:	PUNCT
esrj-105296	121	23	#	#	NOUN
esrj-105296	121	24	of	of	ADP
esrj-105296	121	25	parameters	parameter	NOUN
esrj-105296	122	1	input_1	input_1	ADV
esrj-105296	122	2	(	(	PUNCT
esrj-105296	122	3	input	input	NOUN
esrj-105296	122	4	layer	layer	NOUN
esrj-105296	122	5	)	)	PUNCT
esrj-105296	122	6	(	(	PUNCT
esrj-105296	122	7	13	13	NUM
esrj-105296	122	8	,	,	PUNCT
esrj-105296	122	9	13	13	NUM
esrj-105296	122	10	,	,	PUNCT
esrj-105296	122	11	15	15	NUM
esrj-105296	122	12	,	,	PUNCT
esrj-105296	122	13	1	1	NUM
esrj-105296	122	14	)	)	PUNCT
esrj-105296	122	15	0	0	NUM
esrj-105296	122	16	0	0	NUM
esrj-105296	122	17	0	0	NUM
esrj-105296	122	18	conv3d	conv3d	PROPN
esrj-105296	122	19	(	(	PUNCT
esrj-105296	122	20	conv3d	conv3d	PROPN
esrj-105296	122	21	)	)	PUNCT
esrj-105296	122	22	(	(	PUNCT
esrj-105296	122	23	11	11	NUM
esrj-105296	122	24	,	,	PUNCT
esrj-105296	122	25	11	11	NUM
esrj-105296	122	26	,	,	PUNCT
esrj-105296	122	27	9	9	NUM
esrj-105296	122	28	,	,	PUNCT
esrj-105296	122	29	8)	8)	NUM
esrj-105296	122	30	512	512	NUM
esrj-105296	122	31	512	512	NUM
esrj-105296	122	32	512	512	NUM
esrj-105296	122	33	conv3d_1	conv3d_1	NOUN
esrj-105296	122	34	(	(	PUNCT
esrj-105296	122	35	conv3d	conv3d	PROPN
esrj-105296	122	36	)	)	PUNCT
esrj-105296	122	37	(	(	PUNCT
esrj-105296	122	38	9	9	NUM
esrj-105296	122	39	,	,	PUNCT
esrj-105296	122	40	9	9	NUM
esrj-105296	122	41	,	,	PUNCT
esrj-105296	122	42	5	5	NUM
esrj-105296	122	43	,	,	PUNCT
esrj-105296	122	44	16	16	NUM
esrj-105296	122	45	)	)	PUNCT
esrj-105296	122	46	5776	5776	NUM
esrj-105296	122	47	5776	5776	NUM
esrj-105296	122	48	5776	5776	NUM
esrj-105296	122	49	conv3d_2	conv3d_2	X
esrj-105296	122	50	(	(	PUNCT
esrj-105296	122	51	conv3d	conv3d	PROPN
esrj-105296	122	52	)	)	PUNCT
esrj-105296	122	53	(	(	PUNCT
esrj-105296	122	54	7	7	NUM
esrj-105296	122	55	,	,	PUNCT
esrj-105296	122	56	7	7	NUM
esrj-105296	122	57	,	,	PUNCT
esrj-105296	122	58	3	3	NUM
esrj-105296	122	59	,	,	PUNCT
esrj-105296	122	60	32	32	NUM
esrj-105296	122	61	)	)	PUNCT
esrj-105296	122	62	13856	13856	NUM
esrj-105296	122	63	13856	13856	NUM
esrj-105296	122	64	13856	13856	NUM
esrj-105296	122	65	reshape	reshape	NOUN
esrj-105296	122	66	(	(	PUNCT
esrj-105296	122	67	reshape	reshape	NOUN
esrj-105296	122	68	)	)	PUNCT
esrj-105296	122	69	(	(	PUNCT
esrj-105296	122	70	7	7	NUM
esrj-105296	122	71	,	,	PUNCT
esrj-105296	122	72	7	7	NUM
esrj-105296	122	73	,	,	PUNCT
esrj-105296	122	74	96	96	NUM
esrj-105296	122	75	)	)	PUNCT
esrj-105296	122	76	0	0	NUM
esrj-105296	122	77	0	0	NUM
esrj-105296	122	78	0	0	NUM
esrj-105296	122	79	conv2d	conv2d	VERB
esrj-105296	122	80	(	(	PUNCT
esrj-105296	122	81	conv2d	conv2d	X
esrj-105296	122	82	)	)	PUNCT
esrj-105296	122	83	(	(	PUNCT
esrj-105296	122	84	5	5	NUM
esrj-105296	122	85	,	,	PUNCT
esrj-105296	122	86	5	5	NUM
esrj-105296	122	87	,	,	PUNCT
esrj-105296	122	88	64	64	NUM
esrj-105296	122	89	)	)	PUNCT
esrj-105296	122	90	55360	55360	NUM
esrj-105296	122	91	55360	55360	NUM
esrj-105296	122	92	55360	55360	NUM
esrj-105296	122	93	flatten	flatten	VERB
esrj-105296	122	94	(	(	PUNCT
esrj-105296	122	95	flatten	flatten	ADJ
esrj-105296	122	96	)	)	PUNCT
esrj-105296	122	97	(	(	PUNCT
esrj-105296	122	98	1600	1600	NUM
esrj-105296	122	99	)	)	PUNCT
esrj-105296	122	100	0	0	NUM
esrj-105296	122	101	0	0	NUM
esrj-105296	122	102	0	0	NUM
esrj-105296	122	103	dense	dense	ADJ
esrj-105296	122	104	(	(	PUNCT
esrj-105296	122	105	dense	dense	ADJ
esrj-105296	122	106	)	)	PUNCT
esrj-105296	122	107	(	(	PUNCT
esrj-105296	122	108	256	256	NUM
esrj-105296	122	109	)	)	PUNCT
esrj-105296	122	110	409856	409856	NUM
esrj-105296	122	111	409856	409856	NUM
esrj-105296	122	112	409856	409856	NUM
esrj-105296	122	113	dropout	dropout	NOUN
esrj-105296	122	114	(	(	PUNCT
esrj-105296	122	115	dropout	dropout	NOUN
esrj-105296	122	116	)	)	PUNCT
esrj-105296	122	117	(	(	PUNCT
esrj-105296	122	118	256	256	NUM
esrj-105296	122	119	)	)	PUNCT
esrj-105296	122	120	0	0	NUM
esrj-105296	122	121	0	0	NUM
esrj-105296	122	122	0	0	NUM
esrj-105296	123	1	dense_1	dense_1	NOUN
esrj-105296	123	2	(	(	PUNCT
esrj-105296	123	3	dense	dense	ADJ
esrj-105296	123	4	)	)	PUNCT
esrj-105296	123	5	(	(	PUNCT
esrj-105296	123	6	128	128	NUM
esrj-105296	123	7	)	)	PUNCT
esrj-105296	123	8	32896	32896	NUM
esrj-105296	123	9	32896	32896	NUM
esrj-105296	123	10	32896	32896	NUM
esrj-105296	124	1	dropout_1	dropout_1	PROPN
esrj-105296	124	2	(	(	PUNCT
esrj-105296	124	3	dropout	dropout	NOUN
esrj-105296	124	4	)	)	PUNCT
esrj-105296	124	5	(	(	PUNCT
esrj-105296	124	6	128	128	NUM
esrj-105296	124	7	)	)	PUNCT
esrj-105296	124	8	0	0	NUM
esrj-105296	124	9	0	0	NUM
esrj-105296	124	10	0	0	NUM
esrj-105296	124	11	dense_2	dense_2	NOUN
esrj-105296	124	12	(	(	PUNCT
esrj-105296	124	13	dense	dense	ADJ
esrj-105296	124	14	)	)	PUNCT
esrj-105296	124	15	hyrank	hyrank	PROPN
esrj-105296	124	16	loukia:(14	loukia:(14	PROPN
esrj-105296	124	17	)	)	PUNCT
esrj-105296	124	18	;	;	PUNCT
esrj-105296	124	19	dfc	dfc	PROPN
esrj-105296	124	20	13	13	NUM
esrj-105296	124	21	:	:	PUNCT
esrj-105296	124	22	(	(	PUNCT
esrj-105296	124	23	15	15	NUM
esrj-105296	124	24	)	)	PUNCT
esrj-105296	124	25	;	;	PUNCT
esrj-105296	124	26	salinas	salinas	PROPN
esrj-105296	124	27	scene	scene	PROPN
esrj-105296	124	28	:	:	PUNCT
esrj-105296	124	29	(	(	PUNCT
esrj-105296	124	30	16	16	NUM
esrj-105296	124	31	)	)	PUNCT
esrj-105296	124	32	1806	1806	NUM
esrj-105296	124	33	1935	1935	NUM
esrj-105296	124	34	2064	2064	NUM
esrj-105296	124	35	total	total	ADJ
esrj-105296	124	36	parameters	parameter	NOUN
esrj-105296	124	37	:	:	PUNCT
esrj-105296	124	38	520062	520062	NUM
esrj-105296	124	39	520191	520191	NUM
esrj-105296	124	40	520320	520320	NUM
esrj-105296	124	41	other	other	ADJ
esrj-105296	124	42	parameters	parameter	NOUN
esrj-105296	124	43	parameter	parameter	NOUN
esrj-105296	124	44	value	value	NOUN
esrj-105296	124	45	activation	activation	NOUN
esrj-105296	124	46	function	function	NOUN
esrj-105296	124	47	hybridsn	hybridsn	NOUN
esrj-105296	124	48	:	:	PUNCT
esrj-105296	124	49	relu	relu	NOUN
esrj-105296	124	50	;	;	PUNCT
esrj-105296	124	51	modified	modified	ADJ
esrj-105296	124	52	hybridsn	hybridsn	NOUN
esrj-105296	124	53	:	:	PUNCT
esrj-105296	124	54	mish	mish	PROPN
esrj-105296	124	55	dropout	dropout	PROPN
esrj-105296	124	56	rate	rate	NOUN
esrj-105296	124	57	%	%	NOUN
esrj-105296	124	58	40	40	NUM
esrj-105296	124	59	number	number	NOUN
esrj-105296	124	60	of	of	ADP
esrj-105296	124	61	epochs	epoch	NOUN
esrj-105296	124	62	100	100	NUM
esrj-105296	124	63	batch	batch	NOUN
esrj-105296	124	64	size	size	NOUN
esrj-105296	124	65	256	256	NUM
esrj-105296	124	66	optimizer	optimizer	NOUN
esrj-105296	124	67	hybridsn	hybridsn	NOUN
esrj-105296	124	68	:	:	PUNCT
esrj-105296	124	69	adam	adam	PROPN
esrj-105296	124	70	(	(	PUNCT
esrj-105296	124	71	β1	β1	PROPN
esrj-105296	124	72	:	:	PUNCT
esrj-105296	124	73	0.9	0.9	NUM
esrj-105296	124	74	,	,	PUNCT
esrj-105296	124	75	β2	β2	NOUN
esrj-105296	124	76	:	:	PUNCT
esrj-105296	124	77	0.999	0.999	NUM
esrj-105296	124	78	,	,	PUNCT
esrj-105296	124	79	ϵ:1e-08	ϵ:1e-08	NOUN
esrj-105296	124	80	,	,	PUNCT
esrj-105296	124	81	learning_rate	learning_rate	NUM
esrj-105296	124	82	:	:	PUNCT
esrj-105296	124	83	0.001	0.001	NUM
esrj-105296	124	84	)	)	PUNCT
esrj-105296	124	85	;	;	PUNCT
esrj-105296	124	86	modified	modify	VERB
esrj-105296	124	87	hybridsn	hybridsn	NOUN
esrj-105296	124	88	:	:	PUNCT
esrj-105296	124	89	adamax	adamax	ADV
esrj-105296	124	90	(	(	PUNCT
esrj-105296	124	91	β1	β1	NOUN
esrj-105296	124	92	:	:	PUNCT
esrj-105296	124	93	0.9	0.9	NUM
esrj-105296	124	94	,	,	PUNCT
esrj-105296	124	95	β2	β2	NOUN
esrj-105296	124	96	:	:	PUNCT
esrj-105296	124	97	0.999	0.999	NUM
esrj-105296	124	98	,	,	PUNCT
esrj-105296	124	99	ϵ:1e-08	ϵ:1e-08	NOUN
esrj-105296	124	100	,	,	PUNCT
esrj-105296	124	101	learning_rate	learning_rate	NUM
esrj-105296	124	102	:	:	PUNCT
esrj-105296	124	103	0.001	0.001	NUM
esrj-105296	124	104	)	)	PUNCT
esrj-105296	124	105	3	3	NUM
esrj-105296	124	106	.	.	PUNCT
esrj-105296	124	107	datasets	dataset	NOUN
esrj-105296	124	108	in	in	ADP
esrj-105296	124	109	order	order	NOUN
esrj-105296	124	110	to	to	PART
esrj-105296	124	111	evaluate	evaluate	VERB
esrj-105296	124	112	the	the	DET
esrj-105296	124	113	performance	performance	NOUN
esrj-105296	124	114	of	of	ADP
esrj-105296	124	115	the	the	DET
esrj-105296	124	116	classification	classification	NOUN
esrj-105296	124	117	algorithms	algorithm	NOUN
esrj-105296	124	118	,	,	PUNCT
esrj-105296	124	119	three	three	NUM
esrj-105296	124	120	publicly	publicly	ADV
esrj-105296	124	121	available	available	ADJ
esrj-105296	124	122	hsi	hsi	PROPN
esrj-105296	124	123	datasets	dataset	NOUN
esrj-105296	124	124	were	be	AUX
esrj-105296	124	125	employed	employ	VERB
esrj-105296	124	126	.	.	PUNCT
esrj-105296	125	1	the	the	DET
esrj-105296	125	2	first	first	ADJ
esrj-105296	125	3	dataset	dataset	NOUN
esrj-105296	125	4	,	,	PUNCT
esrj-105296	125	5	named	name	VERB
esrj-105296	125	6	hyrank	hyrank	PROPN
esrj-105296	125	7	,	,	PUNCT
esrj-105296	125	8	was	be	AUX
esrj-105296	125	9	developed	develop	VERB
esrj-105296	125	10	by	by	ADP
esrj-105296	125	11	the	the	DET
esrj-105296	125	12	international	international	ADJ
esrj-105296	125	13	society	society	NOUN
esrj-105296	125	14	for	for	ADP
esrj-105296	125	15	photogrammetry	photogrammetry	NOUN
esrj-105296	125	16	and	and	CCONJ
esrj-105296	125	17	remote	remote	ADJ
esrj-105296	125	18	sensing	sensing	NOUN
esrj-105296	125	19	(	(	PUNCT
esrj-105296	125	20	isprs	isprs	NOUN
esrj-105296	125	21	)	)	PUNCT
esrj-105296	125	22	,	,	PUNCT
esrj-105296	125	23	working	working	NOUN
esrj-105296	125	24	group	group	NOUN
esrj-105296	125	25	iii/4	iii/4	PROPN
esrj-105296	125	26	(	(	PUNCT
esrj-105296	125	27	karantzalos	karantzalo	NOUN
esrj-105296	125	28	et	et	PROPN
esrj-105296	125	29	al	al	PROPN
esrj-105296	125	30	.	.	PROPN
esrj-105296	125	31	,	,	PUNCT
esrj-105296	125	32	2018	2018	NUM
esrj-105296	125	33	)	)	PUNCT
esrj-105296	125	34	.	.	PUNCT
esrj-105296	126	1	it	it	PRON
esrj-105296	126	2	comprises	comprise	VERB
esrj-105296	126	3	five	five	NUM
esrj-105296	126	4	distinct	distinct	ADJ
esrj-105296	126	5	hsis	hsis	NOUN
esrj-105296	126	6	acquired	acquire	VERB
esrj-105296	126	7	from	from	ADP
esrj-105296	126	8	the	the	DET
esrj-105296	126	9	national	national	ADJ
esrj-105296	126	10	aeronautics	aeronautics	PROPN
esrj-105296	126	11	and	and	CCONJ
esrj-105296	126	12	space	space	NOUN
esrj-105296	126	13	administration	administration	NOUN
esrj-105296	126	14	’s	’s	PART
esrj-105296	126	15	(	(	PUNCT
esrj-105296	126	16	nasa	nasa	PROPN
esrj-105296	126	17	)	)	PUNCT
esrj-105296	126	18	eo-1	eo-1	PART
esrj-105296	127	1	hyperion	hyperion	PROPN
esrj-105296	127	2	sensor	sensor	NOUN
esrj-105296	127	3	.	.	PUNCT
esrj-105296	128	1	among	among	ADP
esrj-105296	128	2	these	these	PRON
esrj-105296	128	3	,	,	PUNCT
esrj-105296	128	4	only	only	ADV
esrj-105296	128	5	two	two	NUM
esrj-105296	128	6	hsis	hsis	NOUN
esrj-105296	128	7	,	,	PUNCT
esrj-105296	128	8	namely	namely	ADV
esrj-105296	128	9	dioni	dioni	NOUN
esrj-105296	128	10	and	and	CCONJ
esrj-105296	128	11	loukia	loukia	NOUN
esrj-105296	128	12	,	,	PUNCT
esrj-105296	128	13	are	be	AUX
esrj-105296	128	14	accompanied	accompany	VERB
esrj-105296	128	15	by	by	ADP
esrj-105296	128	16	ground	ground	NOUN
esrj-105296	128	17	-	-	PUNCT
esrj-105296	128	18	truth	truth	NOUN
esrj-105296	128	19	data	datum	NOUN
esrj-105296	128	20	.	.	PUNCT
esrj-105296	129	1	the	the	DET
esrj-105296	129	2	loukia	loukia	NOUN
esrj-105296	129	3	has	have	VERB
esrj-105296	129	4	30	30	NUM
esrj-105296	129	5	meters	meter	NOUN
esrj-105296	129	6	spatial	spatial	ADJ
esrj-105296	129	7	resolution	resolution	NOUN
esrj-105296	129	8	and	and	CCONJ
esrj-105296	129	9	a	a	DET
esrj-105296	129	10	hypercube	hypercube	ADJ
esrj-105296	129	11	size	size	NOUN
esrj-105296	129	12	of	of	ADP
esrj-105296	129	13	249×945×176	249×945×176	NUM
esrj-105296	129	14	.	.	PUNCT
esrj-105296	130	1	ground	ground	NOUN
esrj-105296	130	2	truth	truth	NOUN
esrj-105296	130	3	data	datum	NOUN
esrj-105296	130	4	for	for	ADP
esrj-105296	130	5	loukia	loukia	NOUN
esrj-105296	130	6	contains	contain	VERB
esrj-105296	130	7	14	14	NUM
esrj-105296	130	8	coordination	coordination	NOUN
esrj-105296	130	9	of	of	ADP
esrj-105296	130	10	information	information	NOUN
esrj-105296	130	11	on	on	ADP
esrj-105296	130	12	the	the	DET
esrj-105296	130	13	environment	environment	NOUN
esrj-105296	130	14	(	(	PUNCT
esrj-105296	130	15	corine	corine	NOUN
esrj-105296	130	16	)	)	PUNCT
esrj-105296	130	17	land	land	NOUN
esrj-105296	130	18	cover	cover	NOUN
esrj-105296	130	19	classes	class	NOUN
esrj-105296	130	20	,	,	PUNCT
esrj-105296	130	21	carefully	carefully	ADV
esrj-105296	130	22	annotated	annotate	VERB
esrj-105296	130	23	by	by	ADP
esrj-105296	130	24	interpretation	interpretation	NOUN
esrj-105296	130	25	experts	expert	NOUN
esrj-105296	130	26	(	(	PUNCT
esrj-105296	130	27	karantzalos	karantzalo	NOUN
esrj-105296	130	28	et	et	PROPN
esrj-105296	130	29	al	al	PROPN
esrj-105296	130	30	.	.	PROPN
esrj-105296	130	31	,	,	PUNCT
esrj-105296	130	32	2018	2018	NUM
esrj-105296	130	33	)	)	PUNCT
esrj-105296	130	34	.	.	PUNCT
esrj-105296	131	1	detailed	detailed	ADJ
esrj-105296	131	2	information	information	NOUN
esrj-105296	131	3	regarding	regard	VERB
esrj-105296	131	4	the	the	DET
esrj-105296	131	5	number	number	NOUN
esrj-105296	131	6	of	of	ADP
esrj-105296	131	7	training	training	NOUN
esrj-105296	131	8	and	and	CCONJ
esrj-105296	131	9	testing	testing	NOUN
esrj-105296	131	10	samples	sample	NOUN
esrj-105296	131	11	for	for	ADP
esrj-105296	131	12	this	this	DET
esrj-105296	131	13	data	datum	NOUN
esrj-105296	131	14	set	set	VERB
esrj-105296	131	15	is	be	AUX
esrj-105296	131	16	provided	provide	VERB
esrj-105296	131	17	in	in	ADP
esrj-105296	131	18	table	table	NOUN
esrj-105296	131	19	2	2	NUM
esrj-105296	131	20	.	.	PUNCT
esrj-105296	132	1	the	the	DET
esrj-105296	132	2	second	second	ADJ
esrj-105296	132	3	dataset	dataset	NOUN
esrj-105296	132	4	,	,	PUNCT
esrj-105296	132	5	named	name	VERB
esrj-105296	132	6	houston	houston	PROPN
esrj-105296	132	7	2013	2013	NUM
esrj-105296	132	8	,	,	PUNCT
esrj-105296	132	9	was	be	AUX
esrj-105296	132	10	acquired	acquire	VERB
esrj-105296	132	11	from	from	ADP
esrj-105296	132	12	the	the	DET
esrj-105296	132	13	university	university	PROPN
esrj-105296	132	14	of	of	ADP
esrj-105296	132	15	houston	houston	PROPN
esrj-105296	132	16	campus	campus	NOUN
esrj-105296	132	17	and	and	CCONJ
esrj-105296	132	18	the	the	DET
esrj-105296	132	19	surrounding	surround	VERB
esrj-105296	132	20	urban	urban	ADJ
esrj-105296	132	21	area	area	NOUN
esrj-105296	132	22	on	on	ADP
esrj-105296	132	23	june	june	PROPN
esrj-105296	132	24	23	23	NUM
esrj-105296	132	25	,	,	PUNCT
esrj-105296	132	26	2012	2012	NUM
esrj-105296	132	27	,	,	PUNCT
esrj-105296	132	28	by	by	ADP
esrj-105296	132	29	the	the	DET
esrj-105296	132	30	national	national	PROPN
esrj-105296	132	31	science	science	PROPN
esrj-105296	132	32	foundation	foundation	PROPN
esrj-105296	132	33	(	(	PUNCT
esrj-105296	132	34	nsf)-funded	nsf)-funded	ADJ
esrj-105296	132	35	center	center	NOUN
esrj-105296	132	36	for	for	ADP
esrj-105296	132	37	airborne	airborne	ADJ
esrj-105296	132	38	laser	laser	NOUN
esrj-105296	132	39	mapping	mapping	NOUN
esrj-105296	132	40	(	(	PUNCT
esrj-105296	132	41	ncalm	ncalm	PROPN
esrj-105296	132	42	)	)	PUNCT
esrj-105296	132	43	.	.	PUNCT
esrj-105296	133	1	the	the	DET
esrj-105296	133	2	dataset	dataset	NOUN
esrj-105296	133	3	has	have	VERB
esrj-105296	133	4	a	a	DET
esrj-105296	133	5	spatial	spatial	ADJ
esrj-105296	133	6	resolution	resolution	NOUN
esrj-105296	133	7	of	of	ADP
esrj-105296	133	8	2.5	2.5	NUM
esrj-105296	133	9	meters	meter	NOUN
esrj-105296	133	10	and	and	CCONJ
esrj-105296	133	11	comprises	comprise	VERB
esrj-105296	133	12	144	144	NUM
esrj-105296	133	13	spectral	spectral	ADJ
esrj-105296	133	14	bands	band	NOUN
esrj-105296	133	15	in	in	ADP
esrj-105296	133	16	the	the	DET
esrj-105296	133	17	range	range	NOUN
esrj-105296	133	18	between	between	ADP
esrj-105296	133	19	380	380	NUM
esrj-105296	133	20	and	and	CCONJ
esrj-105296	133	21	1050	1050	NUM
esrj-105296	133	22	nanometers	nanometer	NOUN
esrj-105296	133	23	.	.	PUNCT
esrj-105296	134	1	the	the	DET
esrj-105296	134	2	spatial	spatial	ADJ
esrj-105296	134	3	dimensions	dimension	NOUN
esrj-105296	134	4	of	of	ADP
esrj-105296	134	5	the	the	DET
esrj-105296	134	6	hsi	hsi	PROPN
esrj-105296	134	7	are	be	AUX
esrj-105296	134	8	349×1905×144	349×1905×144	NUM
esrj-105296	134	9	.	.	PUNCT
esrj-105296	135	1	fifteen	fifteen	NUM
esrj-105296	135	2	distinct	distinct	ADJ
esrj-105296	135	3	ground	ground	NOUN
esrj-105296	135	4	classes	class	NOUN
esrj-105296	135	5	were	be	AUX
esrj-105296	135	6	determined	determine	VERB
esrj-105296	135	7	by	by	ADP
esrj-105296	135	8	the	the	DET
esrj-105296	135	9	institute	institute	NOUN
esrj-105296	135	10	of	of	ADP
esrj-105296	135	11	electrical	electrical	ADJ
esrj-105296	135	12	and	and	CCONJ
esrj-105296	135	13	electronics	electronic	NOUN
esrj-105296	135	14	engineers	engineer	NOUN
esrj-105296	135	15	geoscience	geoscience	NOUN
esrj-105296	135	16	and	and	CCONJ
esrj-105296	135	17	remote	remote	ADJ
esrj-105296	135	18	sensing	sense	VERB
esrj-105296	135	19	society	society	NOUN
esrj-105296	135	20	(	(	PUNCT
esrj-105296	135	21	ieee	ieee	NOUN
esrj-105296	135	22	grss	grss	NOUN
esrj-105296	135	23	)	)	PUNCT
esrj-105296	135	24	image	image	NOUN
esrj-105296	135	25	analysis	analysis	NOUN
esrj-105296	135	26	and	and	CCONJ
esrj-105296	135	27	data	datum	NOUN
esrj-105296	135	28	fusion	fusion	NOUN
esrj-105296	135	29	technical	technical	PROPN
esrj-105296	135	30	committee	committee	NOUN
esrj-105296	135	31	for	for	ADP
esrj-105296	135	32	organizing	organize	VERB
esrj-105296	135	33	the	the	DET
esrj-105296	135	34	2013	2013	NUM
esrj-105296	135	35	data	datum	NOUN
esrj-105296	135	36	fusion	fusion	NOUN
esrj-105296	135	37	contest	contest	NOUN
esrj-105296	135	38	(	(	PUNCT
esrj-105296	135	39	dfc	dfc	PROPN
esrj-105296	135	40	)	)	PUNCT
esrj-105296	135	41	.	.	PUNCT
esrj-105296	136	1	the	the	DET
esrj-105296	136	2	original	original	ADJ
esrj-105296	136	3	dataset	dataset	NOUN
esrj-105296	136	4	included	include	VERB
esrj-105296	136	5	both	both	DET
esrj-105296	136	6	training	training	NOUN
esrj-105296	136	7	and	and	CCONJ
esrj-105296	136	8	validation	validation	NOUN
esrj-105296	136	9	data	datum	NOUN
esrj-105296	136	10	.	.	PUNCT
esrj-105296	137	1	in	in	ADP
esrj-105296	137	2	this	this	DET
esrj-105296	137	3	study	study	NOUN
esrj-105296	137	4	,	,	PUNCT
esrj-105296	137	5	training	training	NOUN
esrj-105296	137	6	and	and	CCONJ
esrj-105296	137	7	validation	validation	NOUN
esrj-105296	137	8	data	datum	NOUN
esrj-105296	137	9	were	be	AUX
esrj-105296	137	10	merged	merge	VERB
esrj-105296	137	11	and	and	CCONJ
esrj-105296	137	12	employed	employ	VERB
esrj-105296	137	13	as	as	ADP
esrj-105296	137	14	ground	ground	NOUN
esrj-105296	137	15	truth	truth	NOUN
esrj-105296	137	16	data	datum	NOUN
esrj-105296	137	17	.	.	PUNCT
esrj-105296	138	1	detailed	detailed	ADJ
esrj-105296	138	2	statistics	statistic	NOUN
esrj-105296	138	3	regarding	regard	VERB
esrj-105296	138	4	the	the	DET
esrj-105296	138	5	numbering	numbering	NOUN
esrj-105296	138	6	of	of	ADP
esrj-105296	138	7	train	train	NOUN
esrj-105296	138	8	and	and	CCONJ
esrj-105296	138	9	testing	testing	NOUN
esrj-105296	138	10	samples	sample	NOUN
esrj-105296	138	11	of	of	ADP
esrj-105296	138	12	the	the	DET
esrj-105296	138	13	dataset	dataset	NOUN
esrj-105296	138	14	are	be	AUX
esrj-105296	138	15	provided	provide	VERB
esrj-105296	138	16	in	in	ADP
esrj-105296	138	17	table	table	NOUN
esrj-105296	138	18	3	3	NUM
esrj-105296	138	19	.	.	PUNCT
esrj-105296	139	1	the	the	DET
esrj-105296	139	2	third	third	ADJ
esrj-105296	139	3	dataset	dataset	NOUN
esrj-105296	139	4	,	,	PUNCT
esrj-105296	139	5	referred	refer	VERB
esrj-105296	139	6	to	to	ADP
esrj-105296	139	7	as	as	ADP
esrj-105296	139	8	the	the	DET
esrj-105296	139	9	salinas	salinas	PROPN
esrj-105296	139	10	scene	scene	NOUN
esrj-105296	139	11	,	,	PUNCT
esrj-105296	139	12	was	be	AUX
esrj-105296	139	13	acquired	acquire	VERB
esrj-105296	139	14	on	on	ADP
esrj-105296	139	15	october	october	PROPN
esrj-105296	139	16	9	9	NUM
esrj-105296	139	17	,	,	PUNCT
esrj-105296	139	18	1998	1998	NUM
esrj-105296	139	19	,	,	PUNCT
esrj-105296	139	20	from	from	ADP
esrj-105296	139	21	salinas	salinas	PROPN
esrj-105296	139	22	valley	valley	PROPN
esrj-105296	139	23	,	,	PUNCT
esrj-105296	139	24	california	california	PROPN
esrj-105296	139	25	,	,	PUNCT
esrj-105296	139	26	united	united	PROPN
esrj-105296	139	27	states	states	PROPN
esrj-105296	139	28	of	of	ADP
esrj-105296	139	29	america	america	PROPN
esrj-105296	139	30	(	(	PUNCT
esrj-105296	139	31	usa	usa	PROPN
esrj-105296	139	32	)	)	PUNCT
esrj-105296	139	33	,	,	PUNCT
esrj-105296	139	34	utilizing	utilize	VERB
esrj-105296	139	35	nasa	nasa	PROPN
esrj-105296	139	36	’s	’s	PART
esrj-105296	139	37	airborne	airborne	ADJ
esrj-105296	139	38	airborne	airborne	ADJ
esrj-105296	139	39	visible	visible	ADJ
esrj-105296	139	40	/	/	SYM
esrj-105296	139	41	infrared	infrared	ADJ
esrj-105296	139	42	imaging	imaging	NOUN
esrj-105296	139	43	spectrometer	spectrometer	NOUN
esrj-105296	139	44	(	(	PUNCT
esrj-105296	139	45	aviris	aviris	PROPN
esrj-105296	139	46	)	)	PUNCT
esrj-105296	139	47	sensor	sensor	NOUN
esrj-105296	139	48	.	.	PUNCT
esrj-105296	140	1	the	the	DET
esrj-105296	140	2	dataset	dataset	NOUN
esrj-105296	140	3	has	have	VERB
esrj-105296	140	4	a	a	DET
esrj-105296	140	5	spatial	spatial	ADJ
esrj-105296	140	6	resolution	resolution	NOUN
esrj-105296	140	7	of	of	ADP
esrj-105296	140	8	3.7	3.7	NUM
esrj-105296	140	9	meters	meter	NOUN
esrj-105296	140	10	and	and	CCONJ
esrj-105296	140	11	comprises	comprise	VERB
esrj-105296	140	12	224	224	NUM
esrj-105296	140	13	spectral	spectral	ADJ
esrj-105296	140	14	bands	band	NOUN
esrj-105296	140	15	.	.	PUNCT
esrj-105296	141	1	notably	notably	ADV
esrj-105296	141	2	,	,	PUNCT
esrj-105296	141	3	the	the	DET
esrj-105296	141	4	original	original	ADJ
esrj-105296	141	5	dataset	dataset	VERB
esrj-105296	141	6	underwent	underwent	NOUN
esrj-105296	141	7	preprocessing	preprocessing	NOUN
esrj-105296	141	8	,	,	PUNCT
esrj-105296	141	9	during	during	ADP
esrj-105296	141	10	which	which	PRON
esrj-105296	141	11	water	water	NOUN
esrj-105296	141	12	vapor	vapor	NOUN
esrj-105296	141	13	absorption	absorption	NOUN
esrj-105296	141	14	bands	band	NOUN
esrj-105296	141	15	within	within	ADP
esrj-105296	141	16	the	the	DET
esrj-105296	141	17	ranges	range	NOUN
esrj-105296	141	18	of	of	ADP
esrj-105296	141	19	108	108	NUM
esrj-105296	141	20	-	-	SYM
esrj-105296	141	21	112	112	NUM
esrj-105296	141	22	and	and	CCONJ
esrj-105296	141	23	154	154	NUM
esrj-105296	141	24	-	-	SYM
esrj-105296	141	25	167	167	NUM
esrj-105296	141	26	were	be	AUX
esrj-105296	141	27	excluded	exclude	VERB
esrj-105296	141	28	.	.	PUNCT
esrj-105296	142	1	the	the	DET
esrj-105296	142	2	dataset	dataset	NOUN
esrj-105296	142	3	contains	contain	VERB
esrj-105296	142	4	16	16	NUM
esrj-105296	142	5	ground	ground	NOUN
esrj-105296	142	6	classes	class	NOUN
esrj-105296	142	7	in	in	ADP
esrj-105296	142	8	three	three	NUM
esrj-105296	142	9	main	main	ADJ
esrj-105296	142	10	groups	group	NOUN
esrj-105296	142	11	:	:	PUNCT
esrj-105296	142	12	vegetables	vegetable	NOUN
esrj-105296	142	13	,	,	PUNCT
esrj-105296	142	14	bare	bare	ADJ
esrj-105296	142	15	soils	soil	NOUN
esrj-105296	142	16	,	,	PUNCT
esrj-105296	142	17	and	and	CCONJ
esrj-105296	142	18	vineyard	vineyard	NOUN
esrj-105296	142	19	fields	field	NOUN
esrj-105296	142	20	.	.	PUNCT
esrj-105296	143	1	following	follow	VERB
esrj-105296	143	2	processing	processing	NOUN
esrj-105296	143	3	,	,	PUNCT
esrj-105296	143	4	the	the	DET
esrj-105296	143	5	dataset	dataset	NOUN
esrj-105296	143	6	’s	’s	PART
esrj-105296	143	7	dimensions	dimension	NOUN
esrj-105296	143	8	stand	stand	VERB
esrj-105296	143	9	at	at	ADP
esrj-105296	143	10	512×217×204	512×217×204	NUM
esrj-105296	143	11	.	.	PUNCT
esrj-105296	144	1	details	detail	NOUN
esrj-105296	144	2	regarding	regard	VERB
esrj-105296	144	3	the	the	DET
esrj-105296	144	4	number	number	NOUN
esrj-105296	144	5	of	of	ADP
esrj-105296	144	6	training	training	NOUN
esrj-105296	144	7	and	and	CCONJ
esrj-105296	144	8	testing	testing	NOUN
esrj-105296	144	9	samples	sample	NOUN
esrj-105296	144	10	for	for	ADP
esrj-105296	144	11	this	this	DET
esrj-105296	144	12	dataset	dataset	NOUN
esrj-105296	144	13	are	be	AUX
esrj-105296	144	14	provided	provide	VERB
esrj-105296	144	15	in	in	ADP
esrj-105296	144	16	table	table	NOUN
esrj-105296	144	17	4	4	NUM
esrj-105296	144	18	.	.	X
esrj-105296	145	1	165investigation	165investigation	NOUN
esrj-105296	145	2	of	of	ADP
esrj-105296	145	3	the	the	DET
esrj-105296	145	4	performances	performance	NOUN
esrj-105296	145	5	of	of	ADP
esrj-105296	145	6	support	support	NOUN
esrj-105296	145	7	vector	vector	NOUN
esrj-105296	145	8	machine	machine	NOUN
esrj-105296	145	9	,	,	PUNCT
esrj-105296	145	10	random	random	ADJ
esrj-105296	145	11	forest	forest	NOUN
esrj-105296	145	12	,	,	PUNCT
esrj-105296	145	13	and	and	CCONJ
esrj-105296	145	14	3d-2d	3d-2d	NUM
esrj-105296	145	15	convolutional	convolutional	ADJ
esrj-105296	145	16	neural	neural	ADJ
esrj-105296	145	17	network	network	NOUN
esrj-105296	145	18	for	for	ADP
esrj-105296	145	19	hyperspectral	hyperspectral	ADJ
esrj-105296	145	20	image	image	NOUN
esrj-105296	145	21	classification	classification	NOUN
esrj-105296	145	22	table	table	NOUN
esrj-105296	145	23	2	2	NUM
esrj-105296	145	24	.	.	X
esrj-105296	145	25	overview	overview	NOUN
esrj-105296	145	26	of	of	ADP
esrj-105296	145	27	training	training	NOUN
esrj-105296	145	28	and	and	CCONJ
esrj-105296	145	29	testing	testing	NOUN
esrj-105296	145	30	samples	sample	NOUN
esrj-105296	145	31	and	and	CCONJ
esrj-105296	145	32	corresponding	correspond	VERB
esrj-105296	145	33	training	training	NOUN
esrj-105296	145	34	ratios	ratio	NOUN
esrj-105296	145	35	utilized	utilize	VERB
esrj-105296	145	36	in	in	ADP
esrj-105296	145	37	the	the	DET
esrj-105296	145	38	hyrank	hyrank	NOUN
esrj-105296	145	39	loukia	loukia	NOUN
esrj-105296	145	40	dataset	dataset	NOUN
esrj-105296	145	41	.	.	PUNCT
esrj-105296	146	1	class	class	NOUN
esrj-105296	146	2	number	number	NOUN
esrj-105296	146	3	class	class	NOUN
esrj-105296	146	4	name	name	NOUN
esrj-105296	146	5	#	#	NOUN
esrj-105296	146	6	train	train	NOUN
esrj-105296	146	7	samples	sample	NOUN
esrj-105296	146	8	train	train	NOUN
esrj-105296	146	9	ratio	ratio	NOUN
esrj-105296	146	10	#	#	NOUN
esrj-105296	146	11	test	test	NOUN
esrj-105296	146	12	samples	sample	NOUN
esrj-105296	146	13	1	1	NUM
esrj-105296	146	14	dense	dense	ADJ
esrj-105296	146	15	urban	urban	ADJ
esrj-105296	146	16	fabric	fabric	NOUN
esrj-105296	146	17	29	29	NUM
esrj-105296	146	18	2.15	2.15	NUM
esrj-105296	146	19	%	%	NOUN
esrj-105296	146	20	259	259	NUM
esrj-105296	146	21	2	2	NUM
esrj-105296	146	22	mineral	mineral	NOUN
esrj-105296	146	23	extraction	extraction	NOUN
esrj-105296	146	24	sites	site	NOUN
esrj-105296	146	25	7	7	NUM
esrj-105296	146	26	0.52	0.52	NUM
esrj-105296	146	27	%	%	NOUN
esrj-105296	146	28	60	60	NUM
esrj-105296	146	29	3	3	NUM
esrj-105296	146	30	non	non	NOUN
esrj-105296	146	31	irrigated	irrigate	VERB
esrj-105296	146	32	arable	arable	ADJ
esrj-105296	146	33	land	land	NOUN
esrj-105296	146	34	54	54	NUM
esrj-105296	146	35	4.00	4.00	NUM
esrj-105296	146	36	%	%	NOUN
esrj-105296	146	37	488	488	NUM
esrj-105296	146	38	4	4	NUM
esrj-105296	146	39	fruit	fruit	NOUN
esrj-105296	146	40	trees	tree	NOUN
esrj-105296	146	41	8	8	NUM
esrj-105296	146	42	0.59	0.59	NUM
esrj-105296	146	43	%	%	NOUN
esrj-105296	146	44	71	71	NUM
esrj-105296	146	45	5	5	NUM
esrj-105296	146	46	olive	olive	ADJ
esrj-105296	146	47	groves	grove	NOUN
esrj-105296	146	48	140	140	NUM
esrj-105296	146	49	10.37	10.37	NUM
esrj-105296	146	50	%	%	NOUN
esrj-105296	146	51	1261	1261	NUM
esrj-105296	146	52	6	6	NUM
esrj-105296	146	53	broad	broad	ADV
esrj-105296	146	54	leaved	leave	VERB
esrj-105296	146	55	forest	forest	NOUN
esrj-105296	146	56	22	22	NUM
esrj-105296	146	57	1.63	1.63	NUM
esrj-105296	146	58	%	%	NOUN
esrj-105296	146	59	201	201	NUM
esrj-105296	146	60	7	7	NUM
esrj-105296	146	61	coniferous	coniferous	ADJ
esrj-105296	146	62	forest	forest	NOUN
esrj-105296	146	63	50	50	NUM
esrj-105296	146	64	3.70	3.70	NUM
esrj-105296	146	65	%	%	NOUN
esrj-105296	146	66	450	450	NUM
esrj-105296	146	67	8	8	NUM
esrj-105296	146	68	mixed	mixed	ADJ
esrj-105296	146	69	forest	forest	NOUN
esrj-105296	146	70	107	107	NUM
esrj-105296	146	71	7.93	7.93	NUM
esrj-105296	146	72	%	%	NOUN
esrj-105296	146	73	965	965	NUM
esrj-105296	146	74	9	9	NUM
esrj-105296	146	75	dense	dense	ADJ
esrj-105296	146	76	sclerophyllous	sclerophyllous	ADJ
esrj-105296	146	77	vegetation	vegetation	NOUN
esrj-105296	146	78	379	379	NUM
esrj-105296	146	79	28.07	28.07	NUM
esrj-105296	146	80	%	%	NOUN
esrj-105296	146	81	3414	3414	NUM
esrj-105296	146	82	10	10	NUM
esrj-105296	146	83	sparce	sparce	NOUN
esrj-105296	146	84	sclerophyllous	sclerophyllous	ADJ
esrj-105296	146	85	vegetation	vegetation	NOUN
esrj-105296	146	86	280	280	NUM
esrj-105296	146	87	20.74	20.74	NUM
esrj-105296	146	88	%	%	NOUN
esrj-105296	146	89	2523	2523	NUM
esrj-105296	146	90	11	11	NUM
esrj-105296	146	91	sparsely	sparsely	ADV
esrj-105296	146	92	vegetated	vegetate	VERB
esrj-105296	146	93	areas	area	NOUN
esrj-105296	146	94	41	41	NUM
esrj-105296	146	95	3.04	3.04	NUM
esrj-105296	146	96	%	%	NOUN
esrj-105296	146	97	363	363	NUM
esrj-105296	146	98	12	12	NUM
esrj-105296	146	99	rocks	rock	NOUN
esrj-105296	146	100	and	and	CCONJ
esrj-105296	146	101	sand	sand	VERB
esrj-105296	146	102	49	49	NUM
esrj-105296	146	103	3.63	3.63	NUM
esrj-105296	146	104	%	%	NOUN
esrj-105296	146	105	438	438	NUM
esrj-105296	146	106	13	13	NUM
esrj-105296	146	107	water	water	NOUN
esrj-105296	146	108	139	139	NUM
esrj-105296	146	109	10.30	10.30	NUM
esrj-105296	146	110	%	%	NOUN
esrj-105296	146	111	1254	1254	NUM
esrj-105296	146	112	14	14	NUM
esrj-105296	146	113	coastal	coastal	ADJ
esrj-105296	146	114	water	water	NOUN
esrj-105296	146	115	45	45	NUM
esrj-105296	146	116	3.33	3.33	NUM
esrj-105296	146	117	%	%	NOUN
esrj-105296	146	118	406	406	NUM
esrj-105296	146	119	total	total	NOUN
esrj-105296	146	120	1350	1350	NUM
esrj-105296	146	121	100.00	100.00	NUM
esrj-105296	146	122	%	%	NOUN
esrj-105296	146	123	12153	12153	NUM
esrj-105296	146	124	table	table	NOUN
esrj-105296	146	125	3	3	NUM
esrj-105296	146	126	.	.	X
esrj-105296	146	127	overview	overview	NOUN
esrj-105296	146	128	of	of	ADP
esrj-105296	146	129	training	training	NOUN
esrj-105296	146	130	and	and	CCONJ
esrj-105296	146	131	testing	testing	NOUN
esrj-105296	146	132	samples	sample	NOUN
esrj-105296	146	133	and	and	CCONJ
esrj-105296	146	134	corresponding	correspond	VERB
esrj-105296	146	135	training	training	NOUN
esrj-105296	146	136	ratios	ratio	NOUN
esrj-105296	146	137	utilized	utilize	VERB
esrj-105296	146	138	in	in	ADP
esrj-105296	146	139	the	the	DET
esrj-105296	146	140	houston	houston	PROPN
esrj-105296	146	141	2013	2013	NUM
esrj-105296	146	142	dataset	dataset	NOUN
esrj-105296	146	143	.	.	PUNCT
esrj-105296	147	1	class	class	NOUN
esrj-105296	147	2	number	number	NOUN
esrj-105296	147	3	class	class	NOUN
esrj-105296	147	4	name	name	NOUN
esrj-105296	147	5	#	#	NOUN
esrj-105296	147	6	train	train	NOUN
esrj-105296	147	7	samples	sample	NOUN
esrj-105296	147	8	train	train	NOUN
esrj-105296	147	9	ratio	ratio	NOUN
esrj-105296	147	10	#	#	NOUN
esrj-105296	147	11	test	test	NOUN
esrj-105296	147	12	samples	sample	NOUN
esrj-105296	147	13	1	1	NUM
esrj-105296	147	14	healthy	healthy	ADJ
esrj-105296	147	15	grass	grass	NOUN
esrj-105296	147	16	137	137	NUM
esrj-105296	147	17	7.93	7.93	NUM
esrj-105296	147	18	%	%	NOUN
esrj-105296	147	19	1237	1237	NUM
esrj-105296	147	20	2	2	NUM
esrj-105296	147	21	stressed	stress	VERB
esrj-105296	147	22	grass	grass	NOUN
esrj-105296	147	23	145	145	NUM
esrj-105296	147	24	8.40	8.40	NUM
esrj-105296	147	25	%	%	NOUN
esrj-105296	147	26	1309	1309	NUM
esrj-105296	147	27	3	3	NUM
esrj-105296	147	28	synthetic	synthetic	NOUN
esrj-105296	147	29	grass	grass	NOUN
esrj-105296	147	30	80	80	NUM
esrj-105296	147	31	4.63	4.63	NUM
esrj-105296	147	32	%	%	NOUN
esrj-105296	147	33	715	715	NUM
esrj-105296	147	34	4	4	NUM
esrj-105296	147	35	trees	tree	NOUN
esrj-105296	147	36	126	126	NUM
esrj-105296	147	37	7.30	7.30	NUM
esrj-105296	147	38	%	%	NOUN
esrj-105296	147	39	1138	1138	NUM
esrj-105296	147	40	5	5	NUM
esrj-105296	147	41	soil	soil	NOUN
esrj-105296	147	42	130	130	NUM
esrj-105296	147	43	7.53	7.53	NUM
esrj-105296	147	44	%	%	NOUN
esrj-105296	147	45	1168	1168	NUM
esrj-105296	147	46	6	6	NUM
esrj-105296	147	47	water	water	NOUN
esrj-105296	147	48	34	34	NUM
esrj-105296	147	49	1.97	1.97	NUM
esrj-105296	147	50	%	%	NOUN
esrj-105296	147	51	305	305	NUM
esrj-105296	147	52	7	7	NUM
esrj-105296	147	53	residential	residential	ADJ
esrj-105296	147	54	148	148	NUM
esrj-105296	147	55	8.57	8.57	NUM
esrj-105296	147	56	%	%	NOUN
esrj-105296	147	57	1328	1328	NUM
esrj-105296	147	58	8	8	NUM
esrj-105296	147	59	commercial	commercial	ADJ
esrj-105296	147	60	135	135	NUM
esrj-105296	147	61	7.82	7.82	NUM
esrj-105296	147	62	%	%	NOUN
esrj-105296	147	63	1219	1219	NUM
esrj-105296	147	64	9	9	NUM
esrj-105296	147	65	road	road	NOUN
esrj-105296	147	66	155	155	NUM
esrj-105296	147	67	8.98	8.98	NUM
esrj-105296	147	68	%	%	NOUN
esrj-105296	147	69	1399	1399	NUM
esrj-105296	147	70	10	10	NUM
esrj-105296	147	71	highway	highway	NOUN
esrj-105296	147	72	142	142	NUM
esrj-105296	147	73	8.22	8.22	NUM
esrj-105296	147	74	%	%	NOUN
esrj-105296	147	75	1282	1282	NUM
esrj-105296	147	76	11	11	NUM
esrj-105296	147	77	railway	railway	NOUN
esrj-105296	147	78	157	157	NUM
esrj-105296	147	79	9.09	9.09	NUM
esrj-105296	147	80	%	%	NOUN
esrj-105296	147	81	1409	1409	NUM
esrj-105296	147	82	12	12	NUM
esrj-105296	147	83	parking	parking	NOUN
esrj-105296	147	84	lot	lot	NOUN
esrj-105296	147	85	1	1	NUM
esrj-105296	147	86	143	143	NUM
esrj-105296	147	87	8.28	8.28	NUM
esrj-105296	147	88	%	%	NOUN
esrj-105296	147	89	1286	1286	NUM
esrj-105296	147	90	13	13	NUM
esrj-105296	147	91	parking	parking	NOUN
esrj-105296	147	92	lot	lot	NOUN
esrj-105296	147	93	2	2	NUM
esrj-105296	147	94	64	64	NUM
esrj-105296	147	95	3.71	3.71	NUM
esrj-105296	147	96	%	%	NOUN
esrj-105296	147	97	571	571	NUM
esrj-105296	147	98	14	14	NUM
esrj-105296	147	99	tennis	tennis	NOUN
esrj-105296	147	100	court	court	NOUN
esrj-105296	147	101	51	51	NUM
esrj-105296	147	102	2.95	2.95	NUM
esrj-105296	147	103	%	%	NOUN
esrj-105296	147	104	459	459	NUM
esrj-105296	147	105	15	15	NUM
esrj-105296	147	106	running	run	VERB
esrj-105296	147	107	track	track	NOUN
esrj-105296	147	108	80	80	NUM
esrj-105296	147	109	4.63	4.63	NUM
esrj-105296	147	110	%	%	NOUN
esrj-105296	147	111	718	718	NUM
esrj-105296	147	112	total	total	NOUN
esrj-105296	147	113	1727	1727	NUM
esrj-105296	147	114	100.00	100.00	NUM
esrj-105296	147	115	%	%	NOUN
esrj-105296	147	116	15543	15543	NUM
esrj-105296	147	117	table	table	NOUN
esrj-105296	147	118	4	4	NUM
esrj-105296	147	119	.	.	PUNCT
esrj-105296	147	120	overview	overview	NOUN
esrj-105296	147	121	of	of	ADP
esrj-105296	147	122	training	training	NOUN
esrj-105296	147	123	and	and	CCONJ
esrj-105296	147	124	testing	testing	NOUN
esrj-105296	147	125	samples	sample	NOUN
esrj-105296	147	126	and	and	CCONJ
esrj-105296	147	127	corresponding	correspond	VERB
esrj-105296	147	128	training	training	NOUN
esrj-105296	147	129	ratios	ratio	NOUN
esrj-105296	147	130	used	use	VERB
esrj-105296	147	131	in	in	ADP
esrj-105296	147	132	the	the	DET
esrj-105296	147	133	salinas	salinas	PROPN
esrj-105296	147	134	scene	scene	NOUN
esrj-105296	147	135	dataset	dataset	NOUN
esrj-105296	147	136	.	.	PUNCT
esrj-105296	148	1	class	class	NOUN
esrj-105296	148	2	number	number	NOUN
esrj-105296	148	3	class	class	NOUN
esrj-105296	148	4	name	name	NOUN
esrj-105296	148	5	#	#	NOUN
esrj-105296	148	6	train	train	NOUN
esrj-105296	148	7	samples	sample	NOUN
esrj-105296	148	8	train	train	NOUN
esrj-105296	148	9	ratio	ratio	NOUN
esrj-105296	148	10	#	#	NOUN
esrj-105296	148	11	test	test	NOUN
esrj-105296	148	12	samples	sample	NOUN
esrj-105296	148	13	1	1	NUM
esrj-105296	148	14	brocoli_green_weeds_1	brocoli_green_weeds_1	PROPN
esrj-105296	148	15	372	372	NUM
esrj-105296	148	16	3.71	3.71	NUM
esrj-105296	148	17	%	%	NOUN
esrj-105296	148	18	2009	2009	NUM
esrj-105296	148	19	2	2	NUM
esrj-105296	148	20	brocoli_green_weeds_2	brocoli_green_weeds_2	PROPN
esrj-105296	148	21	197	197	NUM
esrj-105296	148	22	6.87	6.87	NUM
esrj-105296	148	23	%	%	NOUN
esrj-105296	148	24	3726	3726	NUM
esrj-105296	148	25	3	3	NUM
esrj-105296	148	26	fallow	fallow	NOUN
esrj-105296	148	27	139	139	NUM
esrj-105296	148	28	3.64	3.64	NUM
esrj-105296	148	29	%	%	NOUN
esrj-105296	148	30	1976	1976	NUM
esrj-105296	148	31	4	4	NUM
esrj-105296	148	32	fallow_rough_plow	fallow_rough_plow	ADV
esrj-105296	148	33	268	268	NUM
esrj-105296	148	34	2.57	2.57	NUM
esrj-105296	148	35	%	%	NOUN
esrj-105296	148	36	1394	1394	NUM
esrj-105296	148	37	5	5	NUM
esrj-105296	148	38	fallow_smooth	fallow_smooth	NUM
esrj-105296	148	39	396	396	NUM
esrj-105296	148	40	4.95	4.95	NUM
esrj-105296	148	41	%	%	NOUN
esrj-105296	148	42	2678	2678	NUM
esrj-105296	148	43	6	6	NUM
esrj-105296	148	44	stubble	stubble	ADJ
esrj-105296	148	45	358	358	NUM
esrj-105296	148	46	7.32	7.32	NUM
esrj-105296	148	47	%	%	NOUN
esrj-105296	148	48	3959	3959	NUM
esrj-105296	148	49	7	7	NUM
esrj-105296	148	50	celery	celery	NOUN
esrj-105296	148	51	1127	1127	NUM
esrj-105296	148	52	6.61	6.61	NUM
esrj-105296	148	53	%	%	NOUN
esrj-105296	148	54	3579	3579	NUM
esrj-105296	148	55	8	8	NUM
esrj-105296	148	56	grapes_untrained	grapes_untrained	ADJ
esrj-105296	148	57	620	620	NUM
esrj-105296	148	58	20.82	20.82	NUM
esrj-105296	148	59	%	%	NOUN
esrj-105296	148	60	11271	11271	NUM
esrj-105296	148	61	9	9	NUM
esrj-105296	148	62	soil_vinyard_develop	soil_vinyard_develop	NOUN
esrj-105296	148	63	328	328	NUM
esrj-105296	148	64	11.46	11.46	NUM
esrj-105296	148	65	%	%	NOUN
esrj-105296	148	66	6203	6203	NUM
esrj-105296	148	67	10	10	NUM
esrj-105296	148	68	corn_senesced_green_weeds	corn_senesced_green_weed	NOUN
esrj-105296	148	69	107	107	NUM
esrj-105296	148	70	6.06	6.06	NUM
esrj-105296	148	71	%	%	NOUN
esrj-105296	148	72	3278	3278	NUM
esrj-105296	148	73	11	11	NUM
esrj-105296	148	74	lettuce_romaine_4wk	lettuce_romaine_4wk	PROPN
esrj-105296	148	75	193	193	NUM
esrj-105296	148	76	1.98	1.98	NUM
esrj-105296	148	77	%	%	NOUN
esrj-105296	148	78	1068	1068	NUM
esrj-105296	148	79	12	12	NUM
esrj-105296	148	80	lettuce_romaine_5wk	lettuce_romaine_5wk	NOUN
esrj-105296	148	81	91	91	NUM
esrj-105296	148	82	3.57	3.57	NUM
esrj-105296	148	83	%	%	NOUN
esrj-105296	148	84	1927	1927	NUM
esrj-105296	148	85	13	13	NUM
esrj-105296	148	86	lettuce_romaine_6wk	lettuce_romaine_6wk	NOUN
esrj-105296	148	87	107	107	NUM
esrj-105296	148	88	1.68	1.68	NUM
esrj-105296	148	89	%	%	NOUN
esrj-105296	148	90	916	916	NUM
esrj-105296	148	91	14	14	NUM
esrj-105296	148	92	lettuce_romaine_7wk	lettuce_romaine_7wk	NOUN
esrj-105296	148	93	727	727	NUM
esrj-105296	148	94	1.98	1.98	NUM
esrj-105296	148	95	%	%	NOUN
esrj-105296	148	96	1070	1070	NUM
esrj-105296	148	97	15	15	NUM
esrj-105296	148	98	vinyard_untrained	vinyard_untraine	VERB
esrj-105296	148	99	181	181	NUM
esrj-105296	148	100	13.43	13.43	NUM
esrj-105296	148	101	%	%	NOUN
esrj-105296	148	102	7268	7268	NUM
esrj-105296	148	103	16	16	NUM
esrj-105296	148	104	vinyard_vertical_trellis	vinyard_vertical_trellis	ADV
esrj-105296	148	105	372	372	NUM
esrj-105296	148	106	3.34	3.34	NUM
esrj-105296	148	107	%	%	NOUN
esrj-105296	148	108	1807	1807	NUM
esrj-105296	148	109	total	total	NOUN
esrj-105296	148	110	5211	5211	NUM
esrj-105296	148	111	3.71	3.71	NUM
esrj-105296	148	112	%	%	NOUN
esrj-105296	148	113	54129	54129	NUM
esrj-105296	148	114	166	166	NUM
esrj-105296	148	115	eren	eren	PROPN
esrj-105296	148	116	can	can	AUX
esrj-105296	148	117	seyrek	seyrek	VERB
esrj-105296	148	118	,	,	PUNCT
esrj-105296	148	119	murat	murat	PROPN
esrj-105296	148	120	uysal	uysal	ADJ
esrj-105296	148	121	figure	figure	NOUN
esrj-105296	148	122	2	2	NUM
esrj-105296	148	123	.	.	PUNCT
esrj-105296	148	124	true	true	ADJ
esrj-105296	148	125	color	color	NOUN
esrj-105296	148	126	image	image	NOUN
esrj-105296	148	127	compositions	composition	NOUN
esrj-105296	148	128	and	and	CCONJ
esrj-105296	148	129	corresponding	correspond	VERB
esrj-105296	148	130	ground	ground	NOUN
esrj-105296	148	131	truth	truth	NOUN
esrj-105296	148	132	data	datum	NOUN
esrj-105296	148	133	for	for	ADP
esrj-105296	148	134	the	the	DET
esrj-105296	148	135	hyrank	hyrank	PROPN
esrj-105296	148	136	loukia	loukia	NOUN
esrj-105296	148	137	,	,	PUNCT
esrj-105296	148	138	houston	houston	PROPN
esrj-105296	148	139	2013	2013	NUM
esrj-105296	148	140	,	,	PUNCT
esrj-105296	148	141	and	and	CCONJ
esrj-105296	148	142	salinas	salinas	PROPN
esrj-105296	148	143	scene	scene	NOUN
esrj-105296	148	144	datasets	dataset	NOUN
esrj-105296	148	145	.	.	PUNCT
esrj-105296	149	1	4	4	X
esrj-105296	149	2	.	.	X
esrj-105296	149	3	experimental	experimental	ADJ
esrj-105296	149	4	setup	setup	NOUN
esrj-105296	149	5	classification	classification	NOUN
esrj-105296	149	6	of	of	ADP
esrj-105296	149	7	hsis	hsis	NOUN
esrj-105296	149	8	was	be	AUX
esrj-105296	149	9	performed	perform	VERB
esrj-105296	149	10	using	use	VERB
esrj-105296	149	11	a	a	DET
esrj-105296	149	12	workstation	workstation	NOUN
esrj-105296	149	13	with	with	ADP
esrj-105296	149	14	intel	intel	PROPN
esrj-105296	149	15	®	®	NOUN
esrj-105296	149	16	xeon	xeon	ADJ
esrj-105296	149	17	™	™	ADJ
esrj-105296	149	18	e-2136	e-2136	NOUN
esrj-105296	149	19	processor	processor	NOUN
esrj-105296	149	20	and	and	CCONJ
esrj-105296	149	21	nvidia	nvidia	PROPN
esrj-105296	149	22	geforce	geforce	PROPN
esrj-105296	149	23	rtx-2070	rtx-2070	VERB
esrj-105296	149	24	super	super	ADJ
esrj-105296	149	25	graphic	graphic	ADJ
esrj-105296	149	26	processor	processor	NOUN
esrj-105296	149	27	.	.	PUNCT
esrj-105296	150	1	the	the	DET
esrj-105296	150	2	software	software	NOUN
esrj-105296	150	3	setup	setup	NOUN
esrj-105296	150	4	comprised	comprise	VERB
esrj-105296	150	5	windows	window	NOUN
esrj-105296	150	6	11	11	NUM
esrj-105296	150	7	x64	x64	NOUN
esrj-105296	150	8	as	as	ADP
esrj-105296	150	9	an	an	DET
esrj-105296	150	10	operating	operating	NOUN
esrj-105296	150	11	system	system	NOUN
esrj-105296	150	12	and	and	CCONJ
esrj-105296	150	13	python	python	NOUN
esrj-105296	150	14	3.7.9	3.7.9	NUM
esrj-105296	150	15	for	for	ADP
esrj-105296	150	16	programming	programming	NOUN
esrj-105296	150	17	tasks	task	NOUN
esrj-105296	150	18	.	.	PUNCT
esrj-105296	151	1	model	model	NOUN
esrj-105296	151	2	development	development	PROPN
esrj-105296	151	3	utilized	utilize	VERB
esrj-105296	151	4	keras	keras	PROPN
esrj-105296	151	5	2.3.1	2.3.1	PROPN
esrj-105296	151	6	(	(	PUNCT
esrj-105296	151	7	chollet	chollet	NOUN
esrj-105296	151	8	,	,	PUNCT
esrj-105296	151	9	2015	2015	NUM
esrj-105296	151	10	)	)	PUNCT
esrj-105296	151	11	,	,	PUNCT
esrj-105296	151	12	tensorflow	tensorflow	NOUN
esrj-105296	151	13	2.1.0	2.1.0	PROPN
esrj-105296	151	14	(	(	PUNCT
esrj-105296	151	15	abadi	abadi	PROPN
esrj-105296	151	16	et	et	PROPN
esrj-105296	151	17	al	al	PROPN
esrj-105296	151	18	.	.	PROPN
esrj-105296	151	19	,	,	PUNCT
esrj-105296	151	20	2016	2016	NUM
esrj-105296	151	21	)	)	PUNCT
esrj-105296	151	22	with	with	ADP
esrj-105296	151	23	cuda	cuda	NOUN
esrj-105296	151	24	support	support	NOUN
esrj-105296	151	25	,	,	PUNCT
esrj-105296	151	26	and	and	CCONJ
esrj-105296	151	27	scikit	scikit	NOUN
esrj-105296	151	28	-	-	PUNCT
esrj-105296	151	29	learn	learn	VERB
esrj-105296	151	30	0.23.2	0.23.2	NUM
esrj-105296	152	1	(	(	PUNCT
esrj-105296	152	2	pedregosa	pedregosa	PROPN
esrj-105296	152	3	et	et	PROPN
esrj-105296	152	4	al	al	PROPN
esrj-105296	152	5	.	.	PROPN
esrj-105296	152	6	,	,	PUNCT
esrj-105296	152	7	2011	2011	NUM
esrj-105296	152	8	)	)	PUNCT
esrj-105296	152	9	libraries	library	NOUN
esrj-105296	152	10	.	.	PUNCT
esrj-105296	153	1	5	5	X
esrj-105296	153	2	.	.	X
esrj-105296	153	3	performance	performance	NOUN
esrj-105296	153	4	evaluation	evaluation	NOUN
esrj-105296	153	5	to	to	PART
esrj-105296	153	6	assess	assess	VERB
esrj-105296	153	7	and	and	CCONJ
esrj-105296	153	8	compare	compare	VERB
esrj-105296	153	9	the	the	DET
esrj-105296	153	10	performance	performance	NOUN
esrj-105296	153	11	of	of	ADP
esrj-105296	153	12	the	the	DET
esrj-105296	153	13	classification	classification	NOUN
esrj-105296	153	14	models	model	NOUN
esrj-105296	153	15	,	,	PUNCT
esrj-105296	153	16	several	several	ADJ
esrj-105296	153	17	evaluation	evaluation	NOUN
esrj-105296	153	18	metrics	metric	NOUN
esrj-105296	153	19	were	be	AUX
esrj-105296	153	20	computed	compute	VERB
esrj-105296	153	21	from	from	ADP
esrj-105296	153	22	the	the	DET
esrj-105296	153	23	confusion	confusion	NOUN
esrj-105296	153	24	matrices	matrix	NOUN
esrj-105296	153	25	.	.	PUNCT
esrj-105296	154	1	these	these	DET
esrj-105296	154	2	metrics	metric	NOUN
esrj-105296	154	3	included	include	VERB
esrj-105296	154	4	oa	oa	PROPN
esrj-105296	154	5	,	,	PUNCT
esrj-105296	154	6	pa	pa	PROPN
esrj-105296	154	7	,	,	PUNCT
esrj-105296	154	8	ua	ua	PROPN
esrj-105296	154	9	,	,	PUNCT
esrj-105296	154	10	f	f	PROPN
esrj-105296	154	11	score	score	NOUN
esrj-105296	154	12	,	,	PUNCT
esrj-105296	154	13	and	and	CCONJ
esrj-105296	154	14	κ	κ	X
esrj-105296	154	15	.	.	PROPN
esrj-105296	155	1	oa	oa	INTJ
esrj-105296	155	2	,	,	PUNCT
esrj-105296	155	3	a	a	DET
esrj-105296	155	4	commonly	commonly	ADV
esrj-105296	155	5	used	use	VERB
esrj-105296	155	6	performance	performance	NOUN
esrj-105296	155	7	metric	metric	ADJ
esrj-105296	155	8	,	,	PUNCT
esrj-105296	155	9	represents	represent	VERB
esrj-105296	155	10	the	the	DET
esrj-105296	155	11	ratio	ratio	NOUN
esrj-105296	155	12	of	of	ADP
esrj-105296	155	13	correctly	correctly	ADV
esrj-105296	155	14	classified	classify	VERB
esrj-105296	155	15	pixels	pixel	NOUN
esrj-105296	155	16	to	to	ADP
esrj-105296	155	17	the	the	DET
esrj-105296	155	18	total	total	ADJ
esrj-105296	155	19	number	number	NOUN
esrj-105296	155	20	of	of	ADP
esrj-105296	155	21	pixels	pixel	NOUN
esrj-105296	155	22	.	.	PUNCT
esrj-105296	156	1	the	the	DET
esrj-105296	156	2	f	f	PROPN
esrj-105296	156	3	score	score	NOUN
esrj-105296	156	4	is	be	AUX
esrj-105296	156	5	calculated	calculate	VERB
esrj-105296	156	6	as	as	ADP
esrj-105296	156	7	the	the	DET
esrj-105296	156	8	harmonic	harmonic	ADJ
esrj-105296	156	9	mean	mean	NOUN
esrj-105296	156	10	of	of	ADP
esrj-105296	156	11	pa	pa	PROPN
esrj-105296	156	12	(	(	PUNCT
esrj-105296	156	13	precision	precision	NOUN
esrj-105296	156	14	)	)	PUNCT
esrj-105296	156	15	and	and	CCONJ
esrj-105296	156	16	ua	ua	PROPN
esrj-105296	156	17	(	(	PUNCT
esrj-105296	156	18	recall	recall	PROPN
esrj-105296	156	19	)	)	PUNCT
esrj-105296	156	20	for	for	ADP
esrj-105296	156	21	each	each	DET
esrj-105296	156	22	class	class	NOUN
esrj-105296	156	23	.	.	PUNCT
esrj-105296	157	1	the	the	DET
esrj-105296	157	2	κ	κ	NOUN
esrj-105296	157	3	is	be	AUX
esrj-105296	157	4	a	a	DET
esrj-105296	157	5	statistical	statistical	ADJ
esrj-105296	157	6	measure	measure	NOUN
esrj-105296	157	7	indicating	indicate	VERB
esrj-105296	157	8	the	the	DET
esrj-105296	157	9	agreements	agreement	NOUN
esrj-105296	157	10	between	between	ADP
esrj-105296	157	11	ground	ground	NOUN
esrj-105296	157	12	truth	truth	NOUN
esrj-105296	157	13	and	and	CCONJ
esrj-105296	157	14	classification	classification	NOUN
esrj-105296	157	15	maps	map	NOUN
esrj-105296	157	16	.	.	PUNCT
esrj-105296	158	1	these	these	DET
esrj-105296	158	2	calculated	calculate	VERB
esrj-105296	158	3	range	range	NOUN
esrj-105296	158	4	from	from	ADP
esrj-105296	158	5	0	0	NUM
esrj-105296	158	6	to	to	ADP
esrj-105296	158	7	1	1	NUM
esrj-105296	158	8	,	,	PUNCT
esrj-105296	158	9	with	with	ADP
esrj-105296	158	10	higher	high	ADJ
esrj-105296	158	11	values	value	NOUN
esrj-105296	158	12	indicating	indicate	VERB
esrj-105296	158	13	better	well	ADJ
esrj-105296	158	14	classification	classification	NOUN
esrj-105296	158	15	performance	performance	NOUN
esrj-105296	158	16	.	.	PUNCT
esrj-105296	159	1	additionally	additionally	ADV
esrj-105296	159	2	,	,	PUNCT
esrj-105296	159	3	mcnemar	mcnemar	PROPN
esrj-105296	159	4	’s	’s	PART
esrj-105296	159	5	test	test	NOUN
esrj-105296	159	6	was	be	AUX
esrj-105296	159	7	employed	employ	VERB
esrj-105296	159	8	to	to	PART
esrj-105296	159	9	examine	examine	VERB
esrj-105296	159	10	the	the	DET
esrj-105296	159	11	statistical	statistical	ADJ
esrj-105296	159	12	significance	significance	NOUN
esrj-105296	159	13	of	of	ADP
esrj-105296	159	14	prediction	prediction	NOUN
esrj-105296	159	15	differences	difference	NOUN
esrj-105296	159	16	between	between	ADP
esrj-105296	159	17	the	the	DET
esrj-105296	159	18	classification	classification	NOUN
esrj-105296	159	19	algorithms	algorithm	NOUN
esrj-105296	159	20	.	.	PUNCT
esrj-105296	160	1	mcnemar	mcnemar	PROPN
esrj-105296	160	2	’s	’s	PART
esrj-105296	160	3	test	test	NOUN
esrj-105296	160	4	is	be	AUX
esrj-105296	160	5	a	a	DET
esrj-105296	160	6	non	non	ADJ
esrj-105296	160	7	-	-	ADJ
esrj-105296	160	8	parametric	parametric	ADJ
esrj-105296	160	9	test	test	NOUN
esrj-105296	160	10	based	base	VERB
esrj-105296	160	11	on	on	ADP
esrj-105296	160	12	chi	chi	ADJ
esrj-105296	160	13	-	-	PUNCT
esrj-105296	160	14	square	square	ADJ
esrj-105296	160	15	distribution	distribution	NOUN
esrj-105296	160	16	and	and	CCONJ
esrj-105296	160	17	is	be	AUX
esrj-105296	160	18	utilized	utilize	VERB
esrj-105296	160	19	to	to	PART
esrj-105296	160	20	determine	determine	VERB
esrj-105296	160	21	dichotomous	dichotomous	ADJ
esrj-105296	160	22	dependent	dependent	ADJ
esrj-105296	160	23	variable	variable	NOUN
esrj-105296	160	24	between	between	ADP
esrj-105296	160	25	two	two	NUM
esrj-105296	160	26	related	related	ADJ
esrj-105296	160	27	groups	group	NOUN
esrj-105296	160	28	(	(	PUNCT
esrj-105296	160	29	i.e.	i.e.	X
esrj-105296	160	30	,	,	PUNCT
esrj-105296	160	31	pixel	pixel	PROPN
esrj-105296	160	32	classes	class	NOUN
esrj-105296	160	33	predicted	predict	VERB
esrj-105296	160	34	by	by	ADP
esrj-105296	160	35	two	two	NUM
esrj-105296	160	36	different	different	ADJ
esrj-105296	160	37	classification	classification	NOUN
esrj-105296	160	38	models	model	NOUN
esrj-105296	160	39	)	)	PUNCT
esrj-105296	160	40	.	.	PUNCT
esrj-105296	161	1	the	the	DET
esrj-105296	161	2	test	test	NOUN
esrj-105296	161	3	value	value	NOUN
esrj-105296	161	4	(	(	PUNCT
esrj-105296	161	5	x2	x2	PROPN
esrj-105296	161	6	)	)	PUNCT
esrj-105296	161	7	is	be	AUX
esrj-105296	161	8	calculated	calculate	VERB
esrj-105296	161	9	from	from	ADP
esrj-105296	161	10	a	a	DET
esrj-105296	161	11	2×2	2×2	NUM
esrj-105296	161	12	sized	sized	ADJ
esrj-105296	161	13	contingency	contingency	NOUN
esrj-105296	161	14	table	table	NOUN
esrj-105296	161	15	by	by	ADP
esrj-105296	161	16	following	follow	VERB
esrj-105296	161	17	equation	equation	NOUN
esrj-105296	161	18	(	(	PUNCT
esrj-105296	161	19	foody	foody	NOUN
esrj-105296	161	20	,	,	PUNCT
esrj-105296	161	21	2004	2004	NUM
esrj-105296	161	22	)	)	PUNCT
esrj-105296	161	23	.	.	PUNCT
esrj-105296	162	1	χ2	χ2	NOUN
esrj-105296	162	2	=	=	PUNCT
esrj-105296	163	1	12	12	NUM
esrj-105296	163	2	−	−	PROPN
esrj-105296	163	3	21|	21|	NUM
esrj-105296	163	4	|−1	|−1	NUM
esrj-105296	163	5	(	(	PUNCT
esrj-105296	163	6	)	)	PUNCT
esrj-105296	163	7	2	2	NUM
esrj-105296	163	8	12	12	NUM
esrj-105296	163	9	−	−	NUM
esrj-105296	163	10	21	21	NUM
esrj-105296	163	11	(	(	PUNCT
esrj-105296	163	12	)	)	PUNCT
esrj-105296	163	13	(	(	PUNCT
esrj-105296	163	14	6	6	NUM
esrj-105296	163	15	)	)	PUNCT
esrj-105296	163	16	where	where	SCONJ
esrj-105296	163	17	f12	f12	NOUN
esrj-105296	163	18	represents	represent	VERB
esrj-105296	163	19	the	the	DET
esrj-105296	163	20	number	number	NOUN
esrj-105296	163	21	of	of	ADP
esrj-105296	163	22	samples	sample	NOUN
esrj-105296	163	23	that	that	PRON
esrj-105296	163	24	are	be	AUX
esrj-105296	163	25	correctly	correctly	ADV
esrj-105296	163	26	classified	classify	VERB
esrj-105296	163	27	by	by	ADP
esrj-105296	163	28	the	the	DET
esrj-105296	163	29	first	first	ADJ
esrj-105296	163	30	model	model	NOUN
esrj-105296	163	31	but	but	CCONJ
esrj-105296	163	32	misclassified	misclassifie	VERB
esrj-105296	163	33	by	by	ADP
esrj-105296	163	34	the	the	DET
esrj-105296	163	35	second	second	ADJ
esrj-105296	163	36	model	model	NOUN
esrj-105296	163	37	,	,	PUNCT
esrj-105296	163	38	and	and	CCONJ
esrj-105296	163	39	f21	f21	NOUN
esrj-105296	163	40	represents	represent	VERB
esrj-105296	163	41	the	the	DET
esrj-105296	163	42	number	number	NOUN
esrj-105296	163	43	of	of	ADP
esrj-105296	163	44	the	the	DET
esrj-105296	163	45	samples	sample	NOUN
esrj-105296	163	46	misclassified	misclassifie	VERB
esrj-105296	163	47	by	by	ADP
esrj-105296	163	48	the	the	DET
esrj-105296	163	49	first	first	ADJ
esrj-105296	163	50	model	model	NOUN
esrj-105296	163	51	,	,	PUNCT
esrj-105296	163	52	and	and	CCONJ
esrj-105296	163	53	correctly	correctly	ADV
esrj-105296	163	54	classified	classify	VERB
esrj-105296	163	55	by	by	ADP
esrj-105296	163	56	the	the	DET
esrj-105296	163	57	second	second	ADJ
esrj-105296	163	58	model	model	NOUN
esrj-105296	163	59	.	.	PUNCT
esrj-105296	164	1	if	if	SCONJ
esrj-105296	164	2	the	the	DET
esrj-105296	164	3	derived	derive	VERB
esrj-105296	164	4	x2	x2	NOUN
esrj-105296	164	5	value	value	NOUN
esrj-105296	164	6	exceeds	exceed	VERB
esrj-105296	164	7	the	the	DET
esrj-105296	164	8	critical	critical	ADJ
esrj-105296	164	9	value	value	NOUN
esrj-105296	164	10	from	from	ADP
esrj-105296	164	11	x2	x2	PROPN
esrj-105296	164	12	table	table	NOUN
esrj-105296	164	13	(	(	PUNCT
esrj-105296	164	14	3.841	3.841	NUM
esrj-105296	164	15	)	)	PUNCT
esrj-105296	164	16	at	at	ADP
esrj-105296	164	17	a	a	DET
esrj-105296	164	18	95	95	NUM
esrj-105296	164	19	%	%	NOUN
esrj-105296	164	20	confidence	confidence	NOUN
esrj-105296	164	21	level	level	NOUN
esrj-105296	164	22	,	,	PUNCT
esrj-105296	164	23	the	the	DET
esrj-105296	164	24	null	null	ADJ
esrj-105296	164	25	hypothesis	hypothesis	NOUN
esrj-105296	164	26	is	be	AUX
esrj-105296	164	27	rejected	reject	VERB
esrj-105296	164	28	.	.	PUNCT
esrj-105296	165	1	the	the	DET
esrj-105296	165	2	rejection	rejection	NOUN
esrj-105296	165	3	indicates	indicate	VERB
esrj-105296	165	4	statistically	statistically	ADV
esrj-105296	165	5	significant	significant	ADJ
esrj-105296	165	6	difference	difference	NOUN
esrj-105296	165	7	in	in	ADP
esrj-105296	165	8	oa	oa	PRON
esrj-105296	165	9	between	between	ADP
esrj-105296	165	10	the	the	DET
esrj-105296	165	11	predictions	prediction	NOUN
esrj-105296	165	12	of	of	ADP
esrj-105296	165	13	the	the	DET
esrj-105296	165	14	two	two	NUM
esrj-105296	165	15	models	model	NOUN
esrj-105296	165	16	.	.	PUNCT
esrj-105296	166	1	167investigation	167investigation	NUM
esrj-105296	166	2	of	of	ADP
esrj-105296	166	3	the	the	DET
esrj-105296	166	4	performances	performance	NOUN
esrj-105296	166	5	of	of	ADP
esrj-105296	166	6	support	support	NOUN
esrj-105296	166	7	vector	vector	NOUN
esrj-105296	166	8	machine	machine	NOUN
esrj-105296	166	9	,	,	PUNCT
esrj-105296	166	10	random	random	ADJ
esrj-105296	166	11	forest	forest	NOUN
esrj-105296	166	12	,	,	PUNCT
esrj-105296	166	13	and	and	CCONJ
esrj-105296	166	14	3d-2d	3d-2d	NUM
esrj-105296	166	15	convolutional	convolutional	ADJ
esrj-105296	166	16	neural	neural	ADJ
esrj-105296	166	17	network	network	NOUN
esrj-105296	166	18	for	for	ADP
esrj-105296	166	19	hyperspectral	hyperspectral	ADJ
esrj-105296	166	20	image	image	NOUN
esrj-105296	166	21	classification	classification	NOUN
esrj-105296	166	22	6	6	NUM
esrj-105296	166	23	.	.	PUNCT
esrj-105296	166	24	results	result	NOUN
esrj-105296	166	25	initially	initially	ADV
esrj-105296	166	26	,	,	PUNCT
esrj-105296	166	27	pca	pca	NOUN
esrj-105296	166	28	transformation	transformation	NOUN
esrj-105296	166	29	was	be	AUX
esrj-105296	166	30	implemented	implement	VERB
esrj-105296	166	31	on	on	ADP
esrj-105296	166	32	the	the	DET
esrj-105296	166	33	hsis	hsis	NOUN
esrj-105296	166	34	,	,	PUNCT
esrj-105296	166	35	retaining	retain	VERB
esrj-105296	166	36	the	the	DET
esrj-105296	166	37	first	first	ADJ
esrj-105296	166	38	15	15	NUM
esrj-105296	166	39	principal	principal	ADJ
esrj-105296	166	40	components	component	NOUN
esrj-105296	166	41	across	across	ADP
esrj-105296	166	42	all	all	DET
esrj-105296	166	43	datasets	dataset	NOUN
esrj-105296	166	44	.	.	PUNCT
esrj-105296	167	1	subsequently	subsequently	ADV
esrj-105296	167	2	,	,	PUNCT
esrj-105296	167	3	10	10	NUM
esrj-105296	167	4	%	%	NOUN
esrj-105296	167	5	of	of	ADP
esrj-105296	167	6	the	the	DET
esrj-105296	167	7	ground	ground	NOUN
esrj-105296	167	8	truth	truth	NOUN
esrj-105296	167	9	data	datum	NOUN
esrj-105296	167	10	was	be	AUX
esrj-105296	167	11	split	split	VERB
esrj-105296	167	12	for	for	ADP
esrj-105296	167	13	training	train	VERB
esrj-105296	167	14	the	the	DET
esrj-105296	167	15	models	model	NOUN
esrj-105296	167	16	,	,	PUNCT
esrj-105296	167	17	with	with	ADP
esrj-105296	167	18	the	the	DET
esrj-105296	167	19	remaining	remain	VERB
esrj-105296	167	20	90	90	NUM
esrj-105296	167	21	%	%	NOUN
esrj-105296	167	22	reserved	reserve	VERB
esrj-105296	167	23	for	for	ADP
esrj-105296	167	24	testing	test	VERB
esrj-105296	167	25	the	the	DET
esrj-105296	167	26	models	model	NOUN
esrj-105296	167	27	.	.	PUNCT
esrj-105296	168	1	grid	grid	NOUN
esrj-105296	168	2	search	search	NOUN
esrj-105296	168	3	was	be	AUX
esrj-105296	168	4	performed	perform	VERB
esrj-105296	168	5	for	for	ADP
esrj-105296	168	6	svm	svm	ADJ
esrj-105296	168	7	and	and	CCONJ
esrj-105296	168	8	rf	rf	ADJ
esrj-105296	168	9	algorithms	algorithm	NOUN
esrj-105296	168	10	on	on	ADP
esrj-105296	168	11	each	each	DET
esrj-105296	168	12	hsi	hsi	PROPN
esrj-105296	168	13	dataset	dataset	VERB
esrj-105296	168	14	to	to	PART
esrj-105296	168	15	determine	determine	VERB
esrj-105296	168	16	the	the	DET
esrj-105296	168	17	optimal	optimal	ADJ
esrj-105296	168	18	user	user	NOUN
esrj-105296	168	19	-	-	PUNCT
esrj-105296	168	20	defined	define	VERB
esrj-105296	168	21	parameters	parameter	NOUN
esrj-105296	168	22	.	.	PUNCT
esrj-105296	169	1	the	the	DET
esrj-105296	169	2	search	search	NOUN
esrj-105296	169	3	ranges	range	VERB
esrj-105296	169	4	for	for	ADP
esrj-105296	169	5	each	each	DET
esrj-105296	169	6	parameter	parameter	NOUN
esrj-105296	169	7	,	,	PUNCT
esrj-105296	169	8	along	along	ADP
esrj-105296	169	9	with	with	ADP
esrj-105296	169	10	the	the	DET
esrj-105296	169	11	determined	determined	ADJ
esrj-105296	169	12	optimal	optimal	ADJ
esrj-105296	169	13	parameters	parameter	NOUN
esrj-105296	169	14	for	for	ADP
esrj-105296	169	15	each	each	DET
esrj-105296	169	16	dataset	dataset	NOUN
esrj-105296	169	17	,	,	PUNCT
esrj-105296	169	18	are	be	AUX
esrj-105296	169	19	detailed	detail	VERB
esrj-105296	169	20	in	in	ADP
esrj-105296	169	21	table	table	NOUN
esrj-105296	169	22	5	5	NUM
esrj-105296	169	23	.	.	PUNCT
esrj-105296	169	24	table	table	NOUN
esrj-105296	169	25	5	5	NUM
esrj-105296	169	26	.	.	PUNCT
esrj-105296	170	1	hyperparameter	hyperparameter	NOUN
esrj-105296	170	2	tuning	tune	VERB
esrj-105296	170	3	results	result	NOUN
esrj-105296	170	4	with	with	ADP
esrj-105296	170	5	grid	grid	NOUN
esrj-105296	170	6	search	search	NOUN
esrj-105296	170	7	method	method	NOUN
esrj-105296	170	8	.	.	PUNCT
esrj-105296	171	1	a	a	DET
esrj-105296	171	2	lg	lg	NOUN
esrj-105296	171	3	or	or	CCONJ
esrj-105296	171	4	ith	ith	NOUN
esrj-105296	171	5	m	m	PROPN
esrj-105296	171	6	parameter	parameter	PROPN
esrj-105296	171	7	search	search	NOUN
esrj-105296	171	8	range	range	NOUN
esrj-105296	171	9	hyrank	hyrank	NOUN
esrj-105296	171	10	loukia	loukia	NOUN
esrj-105296	171	11	dfc13	dfc13	PROPN
esrj-105296	171	12	salinas	salinas	PROPN
esrj-105296	171	13	scene	scene	PROPN
esrj-105296	172	1	sv	sv	INTJ
esrj-105296	172	2	m	m	PROPN
esrj-105296	172	3	c	c	NOUN
esrj-105296	172	4	0.001	0.001	NUM
esrj-105296	172	5	,	,	PUNCT
esrj-105296	172	6	0.01	0.01	NUM
esrj-105296	172	7	,	,	PUNCT
esrj-105296	172	8	0.1	0.1	NUM
esrj-105296	172	9	,	,	PUNCT
esrj-105296	172	10	1	1	NUM
esrj-105296	172	11	,	,	PUNCT
esrj-105296	172	12	10	10	NUM
esrj-105296	172	13	,	,	PUNCT
esrj-105296	172	14	100	100	NUM
esrj-105296	172	15	,	,	PUNCT
esrj-105296	172	16	1000	1000	NUM
esrj-105296	172	17	10	10	NUM
esrj-105296	172	18	100	100	NUM
esrj-105296	172	19	100	100	NUM
esrj-105296	172	20	γ	γ	NOUN
esrj-105296	172	21	1	1	NUM
esrj-105296	172	22	,	,	PUNCT
esrj-105296	172	23	0.1	0.1	NUM
esrj-105296	172	24	,	,	PUNCT
esrj-105296	172	25	0.01	0.01	NUM
esrj-105296	172	26	,	,	PUNCT
esrj-105296	172	27	0.001	0.001	NUM
esrj-105296	172	28	,	,	PUNCT
esrj-105296	172	29	0.0001	0.0001	NUM
esrj-105296	172	30	,	,	PUNCT
esrj-105296	172	31	0.00001	0.00001	NUM
esrj-105296	172	32	0.01	0.01	NUM
esrj-105296	172	33	0.1	0.1	NUM
esrj-105296	172	34	0.01	0.01	NUM
esrj-105296	173	1	r	r	NOUN
esrj-105296	173	2	f	f	NOUN
esrj-105296	173	3	mrty	mrty	NUM
esrj-105296	173	4	‘	'	PUNCT
esrj-105296	173	5	sqrt	sqrt	NOUN
esrj-105296	173	6	’	'	PUNCT
esrj-105296	173	7	,	,	PUNCT
esrj-105296	173	8	‘	'	PUNCT
esrj-105296	173	9	log2	log2	PROPN
esrj-105296	173	10	’	'	PUNCT
esrj-105296	173	11	log2	log2	PROPN
esrj-105296	173	12	sqrt	sqrt	NOUN
esrj-105296	173	13	sqrt	sqrt	NOUN
esrj-105296	173	14	ntree	ntree	PROPN
esrj-105296	173	15	100	100	NUM
esrj-105296	173	16	,	,	PUNCT
esrj-105296	173	17	250	250	NUM
esrj-105296	173	18	,	,	PUNCT
esrj-105296	173	19	500	500	NUM
esrj-105296	173	20	,	,	PUNCT
esrj-105296	173	21	750	750	NUM
esrj-105296	173	22	,	,	PUNCT
esrj-105296	173	23	1000	1000	NUM
esrj-105296	173	24	,	,	PUNCT
esrj-105296	173	25	1200	1200	NUM
esrj-105296	173	26	100	100	NUM
esrj-105296	173	27	750	750	NUM
esrj-105296	173	28	1200	1200	NUM
esrj-105296	173	29	the	the	DET
esrj-105296	173	30	evaluation	evaluation	NOUN
esrj-105296	173	31	of	of	ADP
esrj-105296	173	32	classification	classification	NOUN
esrj-105296	173	33	performance	performance	NOUN
esrj-105296	173	34	using	use	VERB
esrj-105296	173	35	the	the	DET
esrj-105296	173	36	hyrank	hyrank	NOUN
esrj-105296	173	37	loukia	loukia	NOUN
esrj-105296	173	38	dataset	dataset	NOUN
esrj-105296	173	39	is	be	AUX
esrj-105296	173	40	summarized	summarize	VERB
esrj-105296	173	41	in	in	ADP
esrj-105296	173	42	table	table	NOUN
esrj-105296	173	43	6	6	NUM
esrj-105296	173	44	.	.	PUNCT
esrj-105296	174	1	note	note	VERB
esrj-105296	174	2	that	that	SCONJ
esrj-105296	174	3	bold	bold	ADJ
esrj-105296	174	4	texts	text	NOUN
esrj-105296	174	5	highlight	highlight	VERB
esrj-105296	174	6	the	the	DET
esrj-105296	174	7	highest	high	ADJ
esrj-105296	174	8	superior	superior	ADJ
esrj-105296	174	9	values	value	NOUN
esrj-105296	174	10	within	within	ADP
esrj-105296	174	11	the	the	DET
esrj-105296	174	12	same	same	ADJ
esrj-105296	174	13	performance	performance	NOUN
esrj-105296	174	14	metric	metric	ADJ
esrj-105296	174	15	row	row	NOUN
esrj-105296	174	16	.	.	PUNCT
esrj-105296	175	1	as	as	SCONJ
esrj-105296	175	2	can	can	AUX
esrj-105296	175	3	be	be	AUX
esrj-105296	175	4	seen	see	VERB
esrj-105296	175	5	from	from	ADP
esrj-105296	175	6	the	the	DET
esrj-105296	175	7	table	table	NOUN
esrj-105296	175	8	,	,	PUNCT
esrj-105296	175	9	the	the	DET
esrj-105296	175	10	modified	modify	VERB
esrj-105296	175	11	hybridsn	hybridsn	PROPN
esrj-105296	175	12	cnn	cnn	PROPN
esrj-105296	175	13	algorithm	algorithm	PROPN
esrj-105296	175	14	achieves	achieve	VERB
esrj-105296	175	15	the	the	DET
esrj-105296	175	16	highest	high	ADJ
esrj-105296	175	17	oa	oa	NOUN
esrj-105296	175	18	and	and	CCONJ
esrj-105296	175	19	κ	κ	X
esrj-105296	175	20	coefficient	coefficient	NOUN
esrj-105296	175	21	with	with	ADP
esrj-105296	175	22	89.69	89.69	NUM
esrj-105296	175	23	%	%	NOUN
esrj-105296	175	24	and	and	CCONJ
esrj-105296	175	25	87.74	87.74	NUM
esrj-105296	175	26	%	%	NOUN
esrj-105296	175	27	,	,	PUNCT
esrj-105296	175	28	respectively	respectively	ADV
esrj-105296	175	29	.	.	PUNCT
esrj-105296	176	1	in	in	ADP
esrj-105296	176	2	contrast	contrast	NOUN
esrj-105296	176	3	,	,	PUNCT
esrj-105296	176	4	rf	rf	NOUN
esrj-105296	176	5	exhibits	exhibit	VERB
esrj-105296	176	6	the	the	DET
esrj-105296	176	7	lowest	low	ADJ
esrj-105296	176	8	oa	oa	NOUN
esrj-105296	176	9	and	and	CCONJ
esrj-105296	176	10	κ	κ	PRON
esrj-105296	176	11	coefficient	coefficient	NOUN
esrj-105296	176	12	with	with	ADP
esrj-105296	176	13	80.86	80.86	NUM
esrj-105296	176	14	%	%	NOUN
esrj-105296	176	15	and	and	CCONJ
esrj-105296	176	16	76.20	76.20	NUM
esrj-105296	176	17	%	%	NOUN
esrj-105296	176	18	,	,	PUNCT
esrj-105296	176	19	respectively	respectively	ADV
esrj-105296	176	20	,	,	PUNCT
esrj-105296	176	21	for	for	ADP
esrj-105296	176	22	the	the	DET
esrj-105296	176	23	hyrank	hyrank	NOUN
esrj-105296	176	24	loukia	loukia	NOUN
esrj-105296	176	25	dataset	dataset	NOUN
esrj-105296	176	26	.	.	PUNCT
esrj-105296	177	1	upon	upon	SCONJ
esrj-105296	177	2	examining	examine	VERB
esrj-105296	177	3	the	the	DET
esrj-105296	177	4	class	class	NOUN
esrj-105296	177	5	-	-	PUNCT
esrj-105296	177	6	based	base	VERB
esrj-105296	177	7	accuracy	accuracy	NOUN
esrj-105296	177	8	metrics	metric	NOUN
esrj-105296	177	9	,	,	PUNCT
esrj-105296	177	10	it	it	PRON
esrj-105296	177	11	is	be	AUX
esrj-105296	177	12	evident	evident	ADJ
esrj-105296	177	13	that	that	SCONJ
esrj-105296	177	14	the	the	DET
esrj-105296	177	15	both	both	DET
esrj-105296	177	16	cnn	cnn	PROPN
esrj-105296	177	17	algorithms	algorithm	NOUN
esrj-105296	177	18	demonstrates	demonstrate	VERB
esrj-105296	177	19	superior	superior	ADJ
esrj-105296	177	20	performance	performance	NOUN
esrj-105296	177	21	compared	compare	VERB
esrj-105296	177	22	to	to	ADP
esrj-105296	177	23	other	other	ADJ
esrj-105296	177	24	algorithms	algorithm	NOUN
esrj-105296	177	25	across	across	ADP
esrj-105296	177	26	most	most	ADJ
esrj-105296	177	27	classes	class	NOUN
esrj-105296	177	28	.	.	PUNCT
esrj-105296	178	1	of	of	ADP
esrj-105296	178	2	particular	particular	ADJ
esrj-105296	178	3	significance	significance	NOUN
esrj-105296	178	4	is	be	AUX
esrj-105296	178	5	its	its	PRON
esrj-105296	178	6	sufficiency	sufficiency	NOUN
esrj-105296	178	7	in	in	ADP
esrj-105296	178	8	classes	class	NOUN
esrj-105296	178	9	such	such	ADJ
esrj-105296	178	10	as	as	ADP
esrj-105296	178	11	dense	dense	ADJ
esrj-105296	178	12	urban	urban	ADJ
esrj-105296	178	13	fabric	fabric	NOUN
esrj-105296	178	14	,	,	PUNCT
esrj-105296	178	15	fruit	fruit	NOUN
esrj-105296	178	16	trees	tree	NOUN
esrj-105296	178	17	,	,	PUNCT
esrj-105296	178	18	broad	broad	ADJ
esrj-105296	178	19	leaved	leave	VERB
esrj-105296	178	20	forest	forest	NOUN
esrj-105296	178	21	,	,	PUNCT
esrj-105296	178	22	coniferous	coniferous	ADJ
esrj-105296	178	23	forest	forest	NOUN
esrj-105296	178	24	,	,	PUNCT
esrj-105296	178	25	and	and	CCONJ
esrj-105296	178	26	mixed	mixed	ADJ
esrj-105296	178	27	forest	forest	NOUN
esrj-105296	178	28	,	,	PUNCT
esrj-105296	178	29	where	where	SCONJ
esrj-105296	178	30	both	both	PRON
esrj-105296	178	31	svm	svm	VERB
esrj-105296	178	32	and	and	CCONJ
esrj-105296	178	33	rf	rf	VERB
esrj-105296	178	34	underperformed	underperform	VERB
esrj-105296	178	35	,	,	PUNCT
esrj-105296	178	36	exhibiting	exhibit	VERB
esrj-105296	178	37	lower	low	ADJ
esrj-105296	178	38	ua	ua	PROPN
esrj-105296	178	39	scores	score	NOUN
esrj-105296	178	40	.	.	PUNCT
esrj-105296	179	1	this	this	DET
esrj-105296	179	2	observation	observation	NOUN
esrj-105296	179	3	underscores	underscore	VERB
esrj-105296	179	4	the	the	DET
esrj-105296	179	5	significant	significant	ADJ
esrj-105296	179	6	contribution	contribution	NOUN
esrj-105296	179	7	of	of	ADP
esrj-105296	179	8	the	the	DET
esrj-105296	179	9	feature	feature	NOUN
esrj-105296	179	10	extraction	extraction	NOUN
esrj-105296	179	11	process	process	NOUN
esrj-105296	179	12	of	of	ADP
esrj-105296	179	13	cnn	cnn	PROPN
esrj-105296	179	14	to	to	ADP
esrj-105296	179	15	the	the	DET
esrj-105296	179	16	accuracy	accuracy	NOUN
esrj-105296	179	17	of	of	ADP
esrj-105296	179	18	classifying	classify	VERB
esrj-105296	179	19	low	low	ADJ
esrj-105296	179	20	-	-	PUNCT
esrj-105296	179	21	resolution	resolution	NOUN
esrj-105296	179	22	hsi	hsi	PROPN
esrj-105296	179	23	.	.	PUNCT
esrj-105296	180	1	when	when	SCONJ
esrj-105296	180	2	examining	examine	VERB
esrj-105296	180	3	class	class	NOUN
esrj-105296	180	4	-	-	PUNCT
esrj-105296	180	5	based	base	VERB
esrj-105296	180	6	accuracy	accuracy	NOUN
esrj-105296	180	7	metrics	metric	NOUN
esrj-105296	180	8	for	for	ADP
esrj-105296	180	9	cnn	cnn	PROPN
esrj-105296	180	10	models	model	NOUN
esrj-105296	180	11	,	,	PUNCT
esrj-105296	180	12	it	it	PRON
esrj-105296	180	13	was	be	AUX
esrj-105296	180	14	found	find	VERB
esrj-105296	180	15	that	that	SCONJ
esrj-105296	180	16	the	the	DET
esrj-105296	180	17	modified	modify	VERB
esrj-105296	180	18	hybridsn	hybridsn	PROPN
esrj-105296	180	19	cnn	cnn	PROPN
esrj-105296	180	20	model	model	NOUN
esrj-105296	180	21	slightly	slightly	ADV
esrj-105296	180	22	improved	improve	VERB
esrj-105296	180	23	the	the	DET
esrj-105296	180	24	classification	classification	NOUN
esrj-105296	180	25	accuracy	accuracy	NOUN
esrj-105296	180	26	for	for	ADP
esrj-105296	180	27	certain	certain	ADJ
esrj-105296	180	28	classes	class	NOUN
esrj-105296	180	29	(	(	PUNCT
esrj-105296	180	30	dense	dense	ADJ
esrj-105296	180	31	urban	urban	ADJ
esrj-105296	180	32	fabric	fabric	NOUN
esrj-105296	180	33	,	,	PUNCT
esrj-105296	180	34	fruit	fruit	NOUN
esrj-105296	180	35	trees	tree	NOUN
esrj-105296	180	36	,	,	PUNCT
esrj-105296	180	37	coniferous	coniferous	ADJ
esrj-105296	180	38	forest	forest	NOUN
esrj-105296	180	39	,	,	PUNCT
esrj-105296	180	40	dense	dense	ADJ
esrj-105296	180	41	sclerophyllous	sclerophyllous	ADJ
esrj-105296	180	42	vegetation	vegetation	NOUN
esrj-105296	180	43	,	,	PUNCT
esrj-105296	180	44	sparse	sparse	VERB
esrj-105296	180	45	sclerophyllous	sclerophyllous	ADJ
esrj-105296	180	46	vegetation	vegetation	NOUN
esrj-105296	180	47	,	,	PUNCT
esrj-105296	180	48	and	and	CCONJ
esrj-105296	180	49	sparsely	sparsely	ADV
esrj-105296	180	50	vegetated	vegetate	VERB
esrj-105296	180	51	areas	area	NOUN
esrj-105296	180	52	)	)	PUNCT
esrj-105296	180	53	compared	compare	VERB
esrj-105296	180	54	to	to	ADP
esrj-105296	180	55	the	the	DET
esrj-105296	180	56	original	original	ADJ
esrj-105296	180	57	hybridsn	hybridsn	PROPN
esrj-105296	180	58	cnn	cnn	PROPN
esrj-105296	180	59	model	model	NOUN
esrj-105296	180	60	.	.	PUNCT
esrj-105296	181	1	in	in	ADP
esrj-105296	181	2	terms	term	NOUN
esrj-105296	181	3	of	of	ADP
esrj-105296	181	4	training	training	NOUN
esrj-105296	181	5	processing	processing	NOUN
esrj-105296	181	6	times	time	NOUN
esrj-105296	181	7	,	,	PUNCT
esrj-105296	181	8	svm	svm	PROPN
esrj-105296	181	9	stands	stand	VERB
esrj-105296	181	10	out	out	ADP
esrj-105296	181	11	for	for	ADP
esrj-105296	181	12	its	its	PRON
esrj-105296	181	13	rapid	rapid	ADJ
esrj-105296	181	14	processing	processing	NOUN
esrj-105296	181	15	,	,	PUNCT
esrj-105296	181	16	whereas	whereas	SCONJ
esrj-105296	181	17	the	the	DET
esrj-105296	181	18	rf	rf	NOUN
esrj-105296	181	19	requires	require	VERB
esrj-105296	181	20	more	more	ADJ
esrj-105296	181	21	processing	processing	NOUN
esrj-105296	181	22	time	time	NOUN
esrj-105296	181	23	.	.	PUNCT
esrj-105296	182	1	in	in	ADP
esrj-105296	182	2	contrast	contrast	NOUN
esrj-105296	182	3	,	,	PUNCT
esrj-105296	182	4	rf	rf	PRON
esrj-105296	182	5	had	have	VERB
esrj-105296	182	6	the	the	DET
esrj-105296	182	7	shortest	short	ADJ
esrj-105296	182	8	classification	classification	NOUN
esrj-105296	182	9	time	time	NOUN
esrj-105296	182	10	for	for	ADP
esrj-105296	182	11	processing	process	VERB
esrj-105296	182	12	the	the	DET
esrj-105296	182	13	entire	entire	ADJ
esrj-105296	182	14	image	image	NOUN
esrj-105296	182	15	,	,	PUNCT
esrj-105296	182	16	whereas	whereas	SCONJ
esrj-105296	182	17	svm	svm	PROPN
esrj-105296	182	18	took	take	VERB
esrj-105296	182	19	the	the	DET
esrj-105296	182	20	longest	long	ADJ
esrj-105296	182	21	time	time	NOUN
esrj-105296	182	22	for	for	ADP
esrj-105296	182	23	classification	classification	NOUN
esrj-105296	182	24	.	.	PUNCT
esrj-105296	183	1	the	the	DET
esrj-105296	183	2	classification	classification	NOUN
esrj-105296	183	3	maps	map	NOUN
esrj-105296	183	4	of	of	ADP
esrj-105296	183	5	the	the	DET
esrj-105296	183	6	algorithms	algorithm	NOUN
esrj-105296	183	7	applied	apply	VERB
esrj-105296	183	8	to	to	ADP
esrj-105296	183	9	the	the	DET
esrj-105296	183	10	hyrank	hyrank	NOUN
esrj-105296	183	11	loukia	loukia	NOUN
esrj-105296	183	12	dataset	dataset	NOUN
esrj-105296	183	13	,	,	PUNCT
esrj-105296	183	14	alongside	alongside	ADP
esrj-105296	183	15	the	the	DET
esrj-105296	183	16	true	true	ADJ
esrj-105296	183	17	color	color	NOUN
esrj-105296	183	18	composition	composition	NOUN
esrj-105296	183	19	of	of	ADP
esrj-105296	183	20	the	the	DET
esrj-105296	183	21	scene	scene	NOUN
esrj-105296	183	22	,	,	PUNCT
esrj-105296	183	23	are	be	AUX
esrj-105296	183	24	given	give	VERB
esrj-105296	183	25	in	in	ADP
esrj-105296	183	26	figure	figure	NOUN
esrj-105296	183	27	3	3	NUM
esrj-105296	183	28	.	.	PUNCT
esrj-105296	183	29	visual	visual	ADJ
esrj-105296	183	30	interpretation	interpretation	NOUN
esrj-105296	183	31	of	of	ADP
esrj-105296	183	32	the	the	DET
esrj-105296	183	33	classification	classification	NOUN
esrj-105296	183	34	maps	map	NOUN
esrj-105296	183	35	is	be	AUX
esrj-105296	183	36	very	very	ADV
esrj-105296	183	37	challenging	challenging	ADJ
esrj-105296	183	38	owing	owe	VERB
esrj-105296	183	39	to	to	ADP
esrj-105296	183	40	the	the	DET
esrj-105296	183	41	discontinuous	discontinuous	ADJ
esrj-105296	183	42	land	land	NOUN
esrj-105296	183	43	patterns	pattern	NOUN
esrj-105296	183	44	and	and	CCONJ
esrj-105296	183	45	low	low	ADJ
esrj-105296	183	46	spatial	spatial	ADJ
esrj-105296	183	47	resolution	resolution	NOUN
esrj-105296	183	48	of	of	ADP
esrj-105296	183	49	the	the	DET
esrj-105296	183	50	scene	scene	NOUN
esrj-105296	183	51	.	.	PUNCT
esrj-105296	184	1	nonetheless	nonetheless	ADV
esrj-105296	184	2	,	,	PUNCT
esrj-105296	184	3	notable	notable	ADJ
esrj-105296	184	4	classification	classification	NOUN
esrj-105296	184	5	errors	error	NOUN
esrj-105296	184	6	are	be	AUX
esrj-105296	184	7	discernible	discernible	ADJ
esrj-105296	184	8	in	in	ADP
esrj-105296	184	9	certain	certain	ADJ
esrj-105296	184	10	pixels	pixel	NOUN
esrj-105296	184	11	.	.	PUNCT
esrj-105296	185	1	for	for	ADP
esrj-105296	185	2	instance	instance	NOUN
esrj-105296	185	3	,	,	PUNCT
esrj-105296	185	4	the	the	DET
esrj-105296	185	5	svm	svm	PROPN
esrj-105296	185	6	and	and	CCONJ
esrj-105296	185	7	both	both	DET
esrj-105296	185	8	cnn	cnn	PROPN
esrj-105296	185	9	algorithms	algorithm	VERB
esrj-105296	185	10	misclassifies	misclassifie	VERB
esrj-105296	185	11	some	some	DET
esrj-105296	185	12	pixels	pixel	NOUN
esrj-105296	185	13	located	locate	VERB
esrj-105296	185	14	within	within	ADP
esrj-105296	185	15	the	the	DET
esrj-105296	185	16	inner	inner	ADJ
esrj-105296	185	17	side	side	NOUN
esrj-105296	185	18	of	of	ADP
esrj-105296	185	19	the	the	DET
esrj-105296	185	20	sea	sea	NOUN
esrj-105296	185	21	(	(	PUNCT
esrj-105296	185	22	e.g.	e.g.	ADV
esrj-105296	185	23	,	,	PUNCT
esrj-105296	185	24	pixels	pixel	NOUN
esrj-105296	185	25	incorrectly	incorrectly	ADV
esrj-105296	185	26	classified	classify	VERB
esrj-105296	185	27	as	as	ADP
esrj-105296	185	28	land	land	NOUN
esrj-105296	185	29	within	within	ADP
esrj-105296	185	30	the	the	DET
esrj-105296	185	31	sea	sea	NOUN
esrj-105296	185	32	)	)	PUNCT
esrj-105296	185	33	.	.	PUNCT
esrj-105296	186	1	furthermore	furthermore	ADV
esrj-105296	186	2	,	,	PUNCT
esrj-105296	186	3	certain	certain	ADJ
esrj-105296	186	4	pixels	pixel	NOUN
esrj-105296	186	5	,	,	PUNCT
esrj-105296	186	6	which	which	PRON
esrj-105296	186	7	should	should	AUX
esrj-105296	186	8	have	have	AUX
esrj-105296	186	9	been	be	AUX
esrj-105296	186	10	classified	classify	VERB
esrj-105296	186	11	as	as	ADP
esrj-105296	186	12	coastal	coastal	ADJ
esrj-105296	186	13	water	water	NOUN
esrj-105296	186	14	,	,	PUNCT
esrj-105296	186	15	are	be	AUX
esrj-105296	186	16	misclassified	misclassifie	VERB
esrj-105296	186	17	as	as	ADP
esrj-105296	186	18	sparse	sparse	ADJ
esrj-105296	186	19	sclerophyllous	sclerophyllous	ADJ
esrj-105296	186	20	vegetation	vegetation	NOUN
esrj-105296	186	21	.	.	PUNCT
esrj-105296	187	1	additionally	additionally	ADV
esrj-105296	187	2	,	,	PUNCT
esrj-105296	187	3	the	the	DET
esrj-105296	187	4	cnn	cnn	PROPN
esrj-105296	187	5	algorithm	algorithm	PROPN
esrj-105296	187	6	eliminated	eliminate	VERB
esrj-105296	187	7	certain	certain	ADJ
esrj-105296	187	8	details	detail	NOUN
esrj-105296	187	9	,	,	PUNCT
esrj-105296	187	10	such	such	ADJ
esrj-105296	187	11	as	as	ADP
esrj-105296	187	12	roads	road	NOUN
esrj-105296	187	13	.	.	PUNCT
esrj-105296	188	1	despite	despite	SCONJ
esrj-105296	188	2	the	the	DET
esrj-105296	188	3	overall	overall	ADJ
esrj-105296	188	4	similarity	similarity	NOUN
esrj-105296	188	5	in	in	ADP
esrj-105296	188	6	classification	classification	NOUN
esrj-105296	188	7	maps	map	NOUN
esrj-105296	188	8	produced	produce	VERB
esrj-105296	188	9	by	by	ADP
esrj-105296	188	10	cnn	cnn	PROPN
esrj-105296	188	11	algorithms	algorithms	PROPN
esrj-105296	188	12	,	,	PUNCT
esrj-105296	188	13	the	the	DET
esrj-105296	188	14	modified	modify	VERB
esrj-105296	188	15	hybridsn	hybridsn	NOUN
esrj-105296	188	16	algorithm	algorithm	NOUN
esrj-105296	188	17	seems	seem	VERB
esrj-105296	188	18	to	to	PART
esrj-105296	188	19	provide	provide	VERB
esrj-105296	188	20	a	a	DET
esrj-105296	188	21	superior	superior	ADJ
esrj-105296	188	22	classification	classification	NOUN
esrj-105296	188	23	map	map	NOUN
esrj-105296	188	24	compared	compare	VERB
esrj-105296	188	25	to	to	ADP
esrj-105296	188	26	the	the	DET
esrj-105296	188	27	original	original	ADJ
esrj-105296	188	28	hybridsn	hybridsn	NOUN
esrj-105296	188	29	model	model	NOUN
esrj-105296	188	30	.	.	PUNCT
esrj-105296	189	1	this	this	DET
esrj-105296	189	2	conclusion	conclusion	NOUN
esrj-105296	189	3	is	be	AUX
esrj-105296	189	4	drawn	draw	VERB
esrj-105296	189	5	from	from	ADP
esrj-105296	189	6	the	the	DET
esrj-105296	189	7	classification	classification	NOUN
esrj-105296	189	8	of	of	ADP
esrj-105296	189	9	the	the	DET
esrj-105296	189	10	sea	sea	NOUN
esrj-105296	189	11	region	region	NOUN
esrj-105296	189	12	in	in	ADP
esrj-105296	189	13	the	the	DET
esrj-105296	189	14	lower	low	ADJ
esrj-105296	189	15	right	right	ADJ
esrj-105296	189	16	corner	corner	NOUN
esrj-105296	189	17	of	of	ADP
esrj-105296	189	18	the	the	DET
esrj-105296	189	19	scene	scene	NOUN
esrj-105296	189	20	and	and	CCONJ
esrj-105296	189	21	the	the	DET
esrj-105296	189	22	lower	low	ADJ
esrj-105296	189	23	incidence	incidence	NOUN
esrj-105296	189	24	of	of	ADP
esrj-105296	189	25	misclassified	misclassifie	VERB
esrj-105296	189	26	pixels	pixel	NOUN
esrj-105296	189	27	in	in	ADP
esrj-105296	189	28	the	the	DET
esrj-105296	189	29	sea	sea	NOUN
esrj-105296	189	30	.	.	PROPN
esrj-105296	189	31	table	table	NOUN
esrj-105296	189	32	6	6	NUM
esrj-105296	189	33	.	.	PUNCT
esrj-105296	190	1	the	the	DET
esrj-105296	190	2	performance	performance	NOUN
esrj-105296	190	3	analysis	analysis	NOUN
esrj-105296	190	4	of	of	ADP
esrj-105296	190	5	the	the	DET
esrj-105296	190	6	classification	classification	NOUN
esrj-105296	190	7	algorithms	algorithm	NOUN
esrj-105296	190	8	on	on	ADP
esrj-105296	190	9	hyrank	hyrank	PROPN
esrj-105296	190	10	loukia	loukia	PROPN
esrj-105296	190	11	hsi	hsi	PROPN
esrj-105296	190	12	dataset	dataset	PROPN
esrj-105296	190	13	.	.	PUNCT
esrj-105296	191	1	class	class	NOUN
esrj-105296	191	2	name	name	NOUN
esrj-105296	191	3	svm	svm	NOUN
esrj-105296	191	4	rf	rf	PART
esrj-105296	191	5	hybridsn	hybridsn	PROPN
esrj-105296	191	6	cnn	cnn	PROPN
esrj-105296	191	7	modified	modify	VERB
esrj-105296	191	8	hybridsn	hybridsn	PROPN
esrj-105296	192	1	cnn	cnn	PROPN
esrj-105296	192	2	pa	pa	PROPN
esrj-105296	192	3	ua	ua	PROPN
esrj-105296	192	4	f	f	PROPN
esrj-105296	192	5	score	score	NOUN
esrj-105296	192	6	pa	pa	PROPN
esrj-105296	192	7	ua	ua	PROPN
esrj-105296	192	8	f	f	PROPN
esrj-105296	192	9	score	score	NOUN
esrj-105296	192	10	pa	pa	PROPN
esrj-105296	192	11	ua	ua	PROPN
esrj-105296	192	12	f	f	PROPN
esrj-105296	192	13	score	score	NOUN
esrj-105296	192	14	pa	pa	PROPN
esrj-105296	192	15	ua	ua	PROPN
esrj-105296	192	16	f	f	PROPN
esrj-105296	192	17	score	score	VERB
esrj-105296	192	18	dense	dense	ADJ
esrj-105296	192	19	urban	urban	ADJ
esrj-105296	192	20	fabric	fabric	NOUN
esrj-105296	192	21	71.69	71.69	NUM
esrj-105296	192	22	75.29	75.29	NUM
esrj-105296	192	23	73.45	73.45	NUM
esrj-105296	192	24	84.80	84.80	NUM
esrj-105296	192	25	55.98	55.98	NUM
esrj-105296	192	26	67.44	67.44	NUM
esrj-105296	192	27	93.69	93.69	NUM
esrj-105296	192	28	80.31	80.31	NUM
esrj-105296	192	29	86.49	86.49	NUM
esrj-105296	192	30	91.60	91.60	NUM
esrj-105296	192	31	84.17	84.17	NUM
esrj-105296	192	32	87.73	87.73	NUM
esrj-105296	192	33	mineral	mineral	NOUN
esrj-105296	192	34	extraction	extraction	NOUN
esrj-105296	192	35	sites	site	NOUN
esrj-105296	192	36	96.15	96.15	NUM
esrj-105296	192	37	83.33	83.33	NUM
esrj-105296	192	38	89.29	89.29	NUM
esrj-105296	192	39	95.56	95.56	NUM
esrj-105296	192	40	71.67	71.67	NUM
esrj-105296	192	41	81.90	81.90	NUM
esrj-105296	192	42	100.00	100.00	NUM
esrj-105296	192	43	93.33	93.33	NUM
esrj-105296	192	44	96.55	96.55	NUM
esrj-105296	192	45	96.23	96.23	NUM
esrj-105296	192	46	85.00	85.00	NUM
esrj-105296	192	47	90.27	90.27	NUM
esrj-105296	192	48	non	non	NOUN
esrj-105296	192	49	irrigated	irrigate	VERB
esrj-105296	192	50	arable	arable	ADJ
esrj-105296	192	51	land	land	NOUN
esrj-105296	192	52	90.16	90.16	NUM
esrj-105296	192	53	80.74	80.74	NUM
esrj-105296	192	54	85.19	85.19	NUM
esrj-105296	192	55	88.52	88.52	NUM
esrj-105296	192	56	75.82	75.82	NUM
esrj-105296	192	57	81.68	81.68	NUM
esrj-105296	192	58	95.24	95.24	NUM
esrj-105296	192	59	86.07	86.07	NUM
esrj-105296	192	60	90.42	90.42	NUM
esrj-105296	192	61	95.43	95.43	NUM
esrj-105296	192	62	85.66	85.66	NUM
esrj-105296	192	63	90.28	90.28	NUM
esrj-105296	192	64	fruit	fruit	NOUN
esrj-105296	192	65	trees	tree	NOUN
esrj-105296	192	66	80.36	80.36	NUM
esrj-105296	192	67	63.38	63.38	NUM
esrj-105296	192	68	70.87	70.87	NUM
esrj-105296	192	69	100.00	100.00	NUM
esrj-105296	192	70	21.13	21.13	NUM
esrj-105296	192	71	34.88	34.88	NUM
esrj-105296	192	72	90.91	90.91	NUM
esrj-105296	192	73	56.34	56.34	NUM
esrj-105296	192	74	69.57	69.57	NUM
esrj-105296	192	75	68.42	68.42	NUM
esrj-105296	192	76	73.24	73.24	NUM
esrj-105296	192	77	70.75	70.75	NUM
esrj-105296	192	78	olive	olive	ADJ
esrj-105296	192	79	groves	grove	NOUN
esrj-105296	192	80	91.38	91.38	NUM
esrj-105296	192	81	91.67	91.67	NUM
esrj-105296	192	82	91.53	91.53	NUM
esrj-105296	192	83	85.25	85.25	NUM
esrj-105296	192	84	87.07	87.07	NUM
esrj-105296	192	85	86.15	86.15	NUM
esrj-105296	192	86	91.86	91.86	NUM
esrj-105296	192	87	94.85	94.85	NUM
esrj-105296	192	88	93.33	93.33	NUM
esrj-105296	192	89	90.02	90.02	NUM
esrj-105296	192	90	94.45	94.45	NUM
esrj-105296	192	91	92.18	92.18	NUM
esrj-105296	192	92	broad	broad	ADV
esrj-105296	192	93	leaved	leave	VERB
esrj-105296	192	94	forest	forest	NOUN
esrj-105296	192	95	88.61	88.61	NUM
esrj-105296	192	96	34.83	34.83	NUM
esrj-105296	192	97	50.00	50.00	NUM
esrj-105296	192	98	86.30	86.30	NUM
esrj-105296	192	99	31.34	31.34	NUM
esrj-105296	192	100	45.99	45.99	NUM
esrj-105296	192	101	90.97	90.97	NUM
esrj-105296	192	102	65.17	65.17	NUM
esrj-105296	192	103	75.94	75.94	NUM
esrj-105296	192	104	94.55	94.55	NUM
esrj-105296	192	105	51.74	51.74	NUM
esrj-105296	192	106	66.88	66.88	NUM
esrj-105296	192	107	coniferous	coniferous	ADJ
esrj-105296	192	108	forest	forest	NOUN
esrj-105296	192	109	87.50	87.50	NUM
esrj-105296	192	110	38.89	38.89	NUM
esrj-105296	192	111	53.85	53.85	NUM
esrj-105296	192	112	91.50	91.50	NUM
esrj-105296	192	113	50.22	50.22	NUM
esrj-105296	192	114	64.85	64.85	NUM
esrj-105296	192	115	89.52	89.52	NUM
esrj-105296	192	116	66.44	66.44	NUM
esrj-105296	192	117	76.28	76.28	NUM
esrj-105296	192	118	80.78	80.78	NUM
esrj-105296	192	119	73.78	73.78	NUM
esrj-105296	192	120	77.12	77.12	NUM
esrj-105296	192	121	mixed	mix	VERB
esrj-105296	192	122	forest	forest	NOUN
esrj-105296	192	123	62.25	62.25	NUM
esrj-105296	192	124	55.03	55.03	NUM
esrj-105296	192	125	58.42	58.42	NUM
esrj-105296	192	126	66.33	66.33	NUM
esrj-105296	192	127	53.68	53.68	NUM
esrj-105296	192	128	59.34	59.34	NUM
esrj-105296	192	129	84.18	84.18	NUM
esrj-105296	192	130	86.01	86.01	NUM
esrj-105296	192	131	85.08	85.08	NUM
esrj-105296	192	132	77.89	77.89	NUM
esrj-105296	192	133	88.70	88.70	NUM
esrj-105296	192	134	82.95	82.95	NUM
esrj-105296	192	135	dense	dense	ADJ
esrj-105296	192	136	sclerophyllous	sclerophyllous	ADJ
esrj-105296	192	137	vegetation	vegetation	NOUN
esrj-105296	192	138	72.41	72.41	NUM
esrj-105296	192	139	87.35	87.35	NUM
esrj-105296	192	140	79.18	79.18	NUM
esrj-105296	192	141	73.11	73.11	NUM
esrj-105296	192	142	87.20	87.20	NUM
esrj-105296	192	143	79.54	79.54	NUM
esrj-105296	192	144	85.03	85.03	NUM
esrj-105296	192	145	89.05	89.05	NUM
esrj-105296	192	146	86.99	86.99	NUM
esrj-105296	192	147	87.57	87.57	NUM
esrj-105296	192	148	90.42	90.42	NUM
esrj-105296	192	149	88.98	88.98	NUM
esrj-105296	192	150	sparce	sparce	PROPN
esrj-105296	192	151	sclerophyllous	sclerophyllous	ADJ
esrj-105296	192	152	vegetation	vegetation	NOUN
esrj-105296	192	153	82.32	82.32	NUM
esrj-105296	192	154	82.32	82.32	NUM
esrj-105296	192	155	82.32	82.32	NUM
esrj-105296	192	156	78.10	78.10	NUM
esrj-105296	192	157	85.26	85.26	NUM
esrj-105296	192	158	81.52	81.52	NUM
esrj-105296	192	159	85.34	85.34	NUM
esrj-105296	192	160	89.73	89.73	NUM
esrj-105296	192	161	87.48	87.48	NUM
esrj-105296	192	162	90.22	90.22	NUM
esrj-105296	192	163	87.79	87.79	NUM
esrj-105296	192	164	88.99	88.99	NUM
esrj-105296	192	165	sparsely	sparsely	ADV
esrj-105296	192	166	vegetated	vegetate	VERB
esrj-105296	192	167	areas	area	NOUN
esrj-105296	192	168	82.81	82.81	NUM
esrj-105296	192	169	58.40	58.40	NUM
esrj-105296	192	170	68.50	68.50	NUM
esrj-105296	192	171	83.70	83.70	NUM
esrj-105296	192	172	52.34	52.34	NUM
esrj-105296	192	173	64.41	64.41	NUM
esrj-105296	192	174	91.81	91.81	NUM
esrj-105296	192	175	86.50	86.50	NUM
esrj-105296	192	176	89.08	89.08	NUM
esrj-105296	192	177	90.93	90.93	NUM
esrj-105296	192	178	88.43	88.43	NUM
esrj-105296	192	179	89.66	89.66	NUM
esrj-105296	192	180	rocks	rock	NOUN
esrj-105296	192	181	and	and	CCONJ
esrj-105296	192	182	sand	sand	VERB
esrj-105296	192	183	97.12	97.12	NUM
esrj-105296	192	184	84.70	84.70	NUM
esrj-105296	192	185	90.49	90.49	NUM
esrj-105296	192	186	92.29	92.29	NUM
esrj-105296	192	187	84.70	84.70	NUM
esrj-105296	192	188	88.33	88.33	NUM
esrj-105296	192	189	99.75	99.75	NUM
esrj-105296	192	190	89.73	89.73	NUM
esrj-105296	192	191	94.47	94.47	NUM
esrj-105296	192	192	96.34	96.34	NUM
esrj-105296	192	193	90.18	90.18	NUM
esrj-105296	192	194	93.16	93.16	NUM
esrj-105296	192	195	water	water	NOUN
esrj-105296	192	196	100.00	100.00	NUM
esrj-105296	192	197	100.00	100.00	NUM
esrj-105296	192	198	100.00	100.00	NUM
esrj-105296	192	199	100.00	100.00	NUM
esrj-105296	192	200	100.00	100.00	NUM
esrj-105296	192	201	100.00	100.00	NUM
esrj-105296	192	202	100.00	100.00	NUM
esrj-105296	192	203	100.00	100.00	NUM
esrj-105296	192	204	100.00	100.00	NUM
esrj-105296	192	205	100.00	100.00	NUM
esrj-105296	192	206	100.00	100.00	NUM
esrj-105296	192	207	100.00	100.00	NUM
esrj-105296	192	208	coastal	coastal	ADJ
esrj-105296	192	209	water	water	NOUN
esrj-105296	192	210	100.00	100.00	NUM
esrj-105296	192	211	100.00	100.00	NUM
esrj-105296	192	212	100.00	100.00	NUM
esrj-105296	192	213	100.00	100.00	NUM
esrj-105296	192	214	100.00	100.00	NUM
esrj-105296	192	215	100.00	100.00	NUM
esrj-105296	192	216	100.00	100.00	NUM
esrj-105296	192	217	100.00	100.00	NUM
esrj-105296	192	218	100.00	100.00	NUM
esrj-105296	192	219	99.51	99.51	NUM
esrj-105296	192	220	100.00	100.00	NUM
esrj-105296	192	221	99.75	99.75	NUM
esrj-105296	192	222	oa	oa	ADP
esrj-105296	192	223	81.61	81.61	NUM
esrj-105296	192	224	80.86	80.86	NUM
esrj-105296	192	225	89.29	89.29	NUM
esrj-105296	192	226	89.69	89.69	NUM
esrj-105296	192	227	κ	κ	PROPN
esrj-105296	192	228	77.82	77.82	NUM
esrj-105296	192	229	76.85	76.85	NUM
esrj-105296	192	230	87.21	87.21	NUM
esrj-105296	192	231	87.74	87.74	NUM
esrj-105296	192	232	training	training	NOUN
esrj-105296	192	233	time	time	NOUN
esrj-105296	192	234	(	(	PUNCT
esrj-105296	192	235	s	s	NOUN
esrj-105296	192	236	)	)	PUNCT
esrj-105296	192	237	5.47	5.47	NUM
esrj-105296	192	238	16.21	16.21	NUM
esrj-105296	192	239	7.61	7.61	NUM
esrj-105296	192	240	8.42	8.42	NUM
esrj-105296	192	241	classification	classification	NOUN
esrj-105296	192	242	time	time	NOUN
esrj-105296	192	243	(	(	PUNCT
esrj-105296	192	244	s	s	NOUN
esrj-105296	192	245	)	)	PUNCT
esrj-105296	192	246	18.75	18.75	NUM
esrj-105296	192	247	7.16	7.16	NUM
esrj-105296	192	248	11.64	11.64	NUM
esrj-105296	192	249	12.27	12.27	NUM
esrj-105296	192	250	168	168	NUM
esrj-105296	192	251	eren	eren	PROPN
esrj-105296	192	252	can	can	AUX
esrj-105296	192	253	seyrek	seyrek	VERB
esrj-105296	192	254	,	,	PUNCT
esrj-105296	192	255	murat	murat	PROPN
esrj-105296	192	256	uysal	uysal	ADJ
esrj-105296	192	257	a	a	DET
esrj-105296	192	258	b	b	NOUN
esrj-105296	192	259	c	c	NOUN
esrj-105296	192	260	d	d	X
esrj-105296	192	261	e	e	X
esrj-105296	192	262	figure	figure	NOUN
esrj-105296	192	263	3	3	NUM
esrj-105296	192	264	.	.	PUNCT
esrj-105296	193	1	classification	classification	NOUN
esrj-105296	193	2	maps	map	NOUN
esrj-105296	193	3	for	for	ADP
esrj-105296	193	4	the	the	DET
esrj-105296	193	5	hyrank	hyrank	NOUN
esrj-105296	193	6	loukia	loukia	NOUN
esrj-105296	193	7	dataset	dataset	NOUN
esrj-105296	193	8	:	:	PUNCT
esrj-105296	193	9	(	(	PUNCT
esrj-105296	193	10	a	a	X
esrj-105296	193	11	)	)	PUNCT
esrj-105296	193	12	svm	svm	PROPN
esrj-105296	193	13	,	,	PUNCT
esrj-105296	193	14	(	(	PUNCT
esrj-105296	193	15	b	b	NOUN
esrj-105296	193	16	)	)	PUNCT
esrj-105296	193	17	rf	rf	NOUN
esrj-105296	193	18	,	,	PUNCT
esrj-105296	193	19	(	(	PUNCT
esrj-105296	193	20	c	c	X
esrj-105296	193	21	)	)	PUNCT
esrj-105296	193	22	hybridsn	hybridsn	PROPN
esrj-105296	193	23	cnn	cnn	PROPN
esrj-105296	193	24	,	,	PUNCT
esrj-105296	193	25	(	(	PUNCT
esrj-105296	193	26	d	d	X
esrj-105296	193	27	)	)	PUNCT
esrj-105296	193	28	modified	modify	VERB
esrj-105296	193	29	hybridsn	hybridsn	PROPN
esrj-105296	193	30	cnn	cnn	PROPN
esrj-105296	193	31	,	,	PUNCT
esrj-105296	193	32	and	and	CCONJ
esrj-105296	193	33	(	(	PUNCT
esrj-105296	193	34	e	e	NOUN
esrj-105296	193	35	)	)	PUNCT
esrj-105296	193	36	true	true	ADJ
esrj-105296	193	37	color	color	NOUN
esrj-105296	193	38	composition	composition	NOUN
esrj-105296	193	39	table	table	NOUN
esrj-105296	193	40	7	7	NUM
esrj-105296	193	41	.	.	PUNCT
esrj-105296	194	1	the	the	DET
esrj-105296	194	2	performance	performance	NOUN
esrj-105296	194	3	analysis	analysis	NOUN
esrj-105296	194	4	of	of	ADP
esrj-105296	194	5	the	the	DET
esrj-105296	194	6	classification	classification	NOUN
esrj-105296	194	7	algorithms	algorithms	NOUN
esrj-105296	194	8	on	on	ADP
esrj-105296	194	9	houston	houston	PROPN
esrj-105296	194	10	2013	2013	NUM
esrj-105296	194	11	hsi	hsi	PROPN
esrj-105296	194	12	dataset	dataset	PROPN
esrj-105296	194	13	.	.	PUNCT
esrj-105296	195	1	class	class	NOUN
esrj-105296	195	2	name	name	NOUN
esrj-105296	195	3	svm	svm	NOUN
esrj-105296	195	4	rf	rf	PART
esrj-105296	195	5	hybridsn	hybridsn	PROPN
esrj-105296	195	6	cnn	cnn	PROPN
esrj-105296	195	7	modified	modify	VERB
esrj-105296	195	8	hybridsn	hybridsn	PROPN
esrj-105296	196	1	cnn	cnn	PROPN
esrj-105296	196	2	pa	pa	PROPN
esrj-105296	196	3	ua	ua	PROPN
esrj-105296	196	4	f	f	PROPN
esrj-105296	196	5	score	score	NOUN
esrj-105296	196	6	pa	pa	PROPN
esrj-105296	196	7	ua	ua	PROPN
esrj-105296	196	8	f	f	PROPN
esrj-105296	196	9	score	score	NOUN
esrj-105296	196	10	pa	pa	PROPN
esrj-105296	196	11	ua	ua	PROPN
esrj-105296	196	12	f	f	PROPN
esrj-105296	196	13	score	score	NOUN
esrj-105296	196	14	pa	pa	PROPN
esrj-105296	196	15	ua	ua	PROPN
esrj-105296	196	16	f	f	PROPN
esrj-105296	196	17	score	score	VERB
esrj-105296	196	18	healthy	healthy	ADJ
esrj-105296	196	19	grass	grass	NOUN
esrj-105296	196	20	96.12	96.12	NUM
esrj-105296	196	21	98.06	98.06	NUM
esrj-105296	196	22	97.08	97.08	NUM
esrj-105296	196	23	97.95	97.95	NUM
esrj-105296	196	24	96.36	96.36	NUM
esrj-105296	196	25	97.15	97.15	NUM
esrj-105296	196	26	96.03	96.03	NUM
esrj-105296	196	27	99.76	99.76	NUM
esrj-105296	196	28	97.86	97.86	NUM
esrj-105296	196	29	98.48	98.48	NUM
esrj-105296	196	30	99.51	99.51	NUM
esrj-105296	196	31	98.99	98.99	NUM
esrj-105296	196	32	stressed	stress	VERB
esrj-105296	196	33	grass	grass	NOUN
esrj-105296	196	34	98.76	98.76	NUM
esrj-105296	196	35	97.02	97.02	NUM
esrj-105296	197	1	97.88	97.88	NUM
esrj-105296	197	2	95.97	95.97	NUM
esrj-105296	197	3	98.32	98.32	NUM
esrj-105296	197	4	97.13	97.13	NUM
esrj-105296	197	5	100.00	100.00	NUM
esrj-105296	197	6	98.09	98.09	NUM
esrj-105296	197	7	99.04	99.04	NUM
esrj-105296	197	8	100.00	100.00	NUM
esrj-105296	197	9	98.55	98.55	NUM
esrj-105296	197	10	99.27	99.27	NUM
esrj-105296	197	11	synthetic	synthetic	ADJ
esrj-105296	197	12	grass	grass	NOUN
esrj-105296	197	13	100.00	100.00	NUM
esrj-105296	197	14	99.58	99.58	NUM
esrj-105296	197	15	99.79	99.79	NUM
esrj-105296	197	16	100.00	100.00	NUM
esrj-105296	197	17	98.88	98.88	NUM
esrj-105296	197	18	99.44	99.44	NUM
esrj-105296	197	19	100.00	100.00	NUM
esrj-105296	197	20	97.90	97.90	NUM
esrj-105296	197	21	98.94	98.94	NUM
esrj-105296	197	22	99.72	99.72	NUM
esrj-105296	197	23	98.60	98.60	NUM
esrj-105296	197	24	99.16	99.16	NUM
esrj-105296	197	25	trees	tree	NOUN
esrj-105296	197	26	98.60	98.60	NUM
esrj-105296	197	27	98.68	98.68	NUM
esrj-105296	197	28	98.64	98.64	NUM
esrj-105296	197	29	98.76	98.76	NUM
esrj-105296	197	30	98.33	98.33	NUM
esrj-105296	197	31	98.55	98.55	NUM
esrj-105296	197	32	97.25	97.25	NUM
esrj-105296	197	33	99.38	99.38	NUM
esrj-105296	197	34	98.31	98.31	NUM
esrj-105296	197	35	98.36	98.36	NUM
esrj-105296	197	36	100.00	100.00	NUM
esrj-105296	197	37	99.17	99.17	NUM
esrj-105296	197	38	soil	soil	NOUN
esrj-105296	197	39	99.83	99.83	NUM
esrj-105296	197	40	99.23	99.23	NUM
esrj-105296	197	41	99.53	99.53	NUM
esrj-105296	197	42	98.22	98.22	NUM
esrj-105296	197	43	99.06	99.06	NUM
esrj-105296	197	44	98.64	98.64	NUM
esrj-105296	197	45	99.91	99.91	NUM
esrj-105296	197	46	100.00	100.00	NUM
esrj-105296	197	47	99.96	99.96	NUM
esrj-105296	197	48	98.73	98.73	NUM
esrj-105296	197	49	100.00	100.00	NUM
esrj-105296	197	50	99.36	99.36	NUM
esrj-105296	197	51	water	water	NOUN
esrj-105296	197	52	100.00	100.00	NUM
esrj-105296	197	53	97.38	97.38	NUM
esrj-105296	197	54	98.67	98.67	NUM
esrj-105296	197	55	100.00	100.00	NUM
esrj-105296	197	56	98.03	98.03	NUM
esrj-105296	197	57	99.01	99.01	NUM
esrj-105296	197	58	100.00	100.00	NUM
esrj-105296	197	59	94.10	94.10	NUM
esrj-105296	197	60	96.96	96.96	NUM
esrj-105296	197	61	100.00	100.00	NUM
esrj-105296	197	62	96.39	96.39	NUM
esrj-105296	197	63	98.16	98.16	NUM
esrj-105296	197	64	residential	residential	ADJ
esrj-105296	197	65	93.22	93.22	NUM
esrj-105296	197	66	94.20	94.20	NUM
esrj-105296	197	67	93.71	93.71	NUM
esrj-105296	197	68	92.31	92.31	NUM
esrj-105296	197	69	90.36	90.36	NUM
esrj-105296	197	70	91.32	91.32	NUM
esrj-105296	197	71	99.75	99.75	NUM
esrj-105296	197	72	90.44	90.44	NUM
esrj-105296	197	73	94.87	94.87	NUM
esrj-105296	197	74	97.82	97.82	NUM
esrj-105296	197	75	97.82	97.82	NUM
esrj-105296	197	76	97.82	97.82	NUM
esrj-105296	197	77	commercial	commercial	NOUN
esrj-105296	197	78	94.48	94.48	NUM
esrj-105296	197	79	91.22	91.22	NUM
esrj-105296	197	80	92.82	92.82	NUM
esrj-105296	197	81	95.46	95.46	NUM
esrj-105296	197	82	89.66	89.66	NUM
esrj-105296	197	83	92.47	92.47	NUM
esrj-105296	197	84	93.53	93.53	NUM
esrj-105296	197	85	95.98	95.98	NUM
esrj-105296	197	86	94.74	94.74	NUM
esrj-105296	197	87	98.81	98.81	NUM
esrj-105296	197	88	95.57	95.57	NUM
esrj-105296	197	89	97.16	97.16	NUM
esrj-105296	197	90	road	road	NOUN
esrj-105296	197	91	90.17	90.17	NUM
esrj-105296	197	92	93.14	93.14	NUM
esrj-105296	197	93	91.63	91.63	NUM
esrj-105296	197	94	83.71	83.71	NUM
esrj-105296	197	95	88.13	88.13	NUM
esrj-105296	197	96	85.86	85.86	NUM
esrj-105296	197	97	97.03	97.03	NUM
esrj-105296	197	98	98.07	98.07	NUM
esrj-105296	197	99	97.55	97.55	NUM
esrj-105296	197	100	99.03	99.03	NUM
esrj-105296	197	101	94.78	94.78	NUM
esrj-105296	197	102	96.86	96.86	NUM
esrj-105296	197	103	highway	highway	NOUN
esrj-105296	197	104	93.38	93.38	NUM
esrj-105296	197	105	92.36	92.36	NUM
esrj-105296	197	106	92.86	92.86	NUM
esrj-105296	197	107	89.76	89.76	NUM
esrj-105296	197	108	91.65	91.65	NUM
esrj-105296	197	109	90.70	90.70	NUM
esrj-105296	197	110	97.55	97.55	NUM
esrj-105296	197	111	99.38	99.38	NUM
esrj-105296	197	112	98.45	98.45	NUM
esrj-105296	197	113	96.32	96.32	NUM
esrj-105296	197	114	99.92	99.92	NUM
esrj-105296	197	115	98.09	98.09	NUM
esrj-105296	197	116	railway	railway	NOUN
esrj-105296	197	117	91.44	91.44	NUM
esrj-105296	197	118	93.26	93.26	NUM
esrj-105296	197	119	92.34	92.34	NUM
esrj-105296	197	120	85.24	85.24	NUM
esrj-105296	197	121	90.99	90.99	NUM
esrj-105296	197	122	88.02	88.02	NUM
esrj-105296	197	123	99.93	99.93	NUM
esrj-105296	197	124	97.16	97.16	NUM
esrj-105296	197	125	98.52	98.52	NUM
esrj-105296	197	126	98.09	98.09	NUM
esrj-105296	197	127	98.37	98.37	NUM
esrj-105296	197	128	98.23	98.23	NUM
esrj-105296	197	129	parking	parking	NOUN
esrj-105296	197	130	lot	lot	NOUN
esrj-105296	197	131	1	1	NUM
esrj-105296	197	132	91.74	91.74	NUM
esrj-105296	197	133	92.38	92.38	NUM
esrj-105296	197	134	92.06	92.06	NUM
esrj-105296	197	135	87.55	87.55	NUM
esrj-105296	197	136	89.11	89.11	NUM
esrj-105296	197	137	88.32	88.32	NUM
esrj-105296	197	138	99.07	99.07	NUM
esrj-105296	197	139	99.84	99.84	NUM
esrj-105296	197	140	99.46	99.46	NUM
esrj-105296	197	141	97.94	97.94	NUM
esrj-105296	197	142	99.77	99.77	NUM
esrj-105296	197	143	98.84	98.84	NUM
esrj-105296	197	144	parking	parking	NOUN
esrj-105296	197	145	lot	lot	NOUN
esrj-105296	197	146	2	2	NUM
esrj-105296	197	147	78.60	78.60	NUM
esrj-105296	197	148	72.68	72.68	NUM
esrj-105296	197	149	75.52	75.52	NUM
esrj-105296	197	150	88.24	88.24	NUM
esrj-105296	197	151	63.05	63.05	NUM
esrj-105296	197	152	73.54	73.54	NUM
esrj-105296	197	153	94.07	94.07	NUM
esrj-105296	197	154	97.20	97.20	NUM
esrj-105296	197	155	95.61	95.61	NUM
esrj-105296	197	156	98.34	98.34	NUM
esrj-105296	197	157	93.52	93.52	NUM
esrj-105296	197	158	95.87	95.87	NUM
esrj-105296	197	159	tennis	tennis	NOUN
esrj-105296	197	160	court	court	NOUN
esrj-105296	197	161	93.08	93.08	NUM
esrj-105296	197	162	99.56	99.56	NUM
esrj-105296	197	163	96.21	96.21	NUM
esrj-105296	197	164	91.04	91.04	NUM
esrj-105296	197	165	99.56	99.56	NUM
esrj-105296	197	166	95.11	95.11	NUM
esrj-105296	197	167	97.45	97.45	NUM
esrj-105296	197	168	100.00	100.00	NUM
esrj-105296	197	169	98.71	98.71	NUM
esrj-105296	197	170	97.45	97.45	NUM
esrj-105296	197	171	100.00	100.00	NUM
esrj-105296	197	172	98.71	98.71	NUM
esrj-105296	197	173	running	run	VERB
esrj-105296	197	174	track	track	NOUN
esrj-105296	197	175	100.00	100.00	NUM
esrj-105296	197	176	97.91	97.91	NUM
esrj-105296	197	177	98.94	98.94	NUM
esrj-105296	197	178	98.05	98.05	NUM
esrj-105296	197	179	98.05	98.05	NUM
esrj-105296	197	180	98.05	98.05	NUM
esrj-105296	197	181	95.35	95.35	NUM
esrj-105296	197	182	100.00	100.00	NUM
esrj-105296	197	183	97.62	97.62	NUM
esrj-105296	197	184	95.86	95.86	NUM
esrj-105296	197	185	100.00	100.00	NUM
esrj-105296	197	186	97.89	97.89	NUM
esrj-105296	197	187	oa	oa	ADP
esrj-105296	197	188	94.58	94.58	NUM
esrj-105296	197	189	92.72	92.72	NUM
esrj-105296	197	190	97.83	97.83	NUM
esrj-105296	197	191	98.29	98.29	NUM
esrj-105296	197	192	κ	κ	ADP
esrj-105296	197	193	94.14	94.14	NUM
esrj-105296	197	194	92.12	92.12	NUM
esrj-105296	197	195	97.66	97.66	NUM
esrj-105296	197	196	98.15	98.15	NUM
esrj-105296	197	197	training	training	NOUN
esrj-105296	197	198	time	time	NOUN
esrj-105296	197	199	(	(	PUNCT
esrj-105296	197	200	s	s	NOUN
esrj-105296	197	201	)	)	PUNCT
esrj-105296	197	202	7.12	7.12	NUM
esrj-105296	197	203	19.59	19.59	NUM
esrj-105296	197	204	8.77	8.77	NUM
esrj-105296	197	205	9.25	9.25	NUM
esrj-105296	197	206	classification	classification	NOUN
esrj-105296	197	207	time	time	NOUN
esrj-105296	197	208	(	(	PUNCT
esrj-105296	197	209	s	s	NOUN
esrj-105296	197	210	)	)	PUNCT
esrj-105296	197	211	43.76	43.76	NUM
esrj-105296	197	212	20.12	20.12	NUM
esrj-105296	197	213	33.87	33.87	NUM
esrj-105296	197	214	36.75	36.75	NUM
esrj-105296	197	215	169investigation	169investigation	PROPN
esrj-105296	197	216	of	of	ADP
esrj-105296	197	217	the	the	DET
esrj-105296	197	218	performances	performance	NOUN
esrj-105296	197	219	of	of	ADP
esrj-105296	197	220	support	support	NOUN
esrj-105296	197	221	vector	vector	NOUN
esrj-105296	197	222	machine	machine	NOUN
esrj-105296	197	223	,	,	PUNCT
esrj-105296	197	224	random	random	ADJ
esrj-105296	197	225	forest	forest	NOUN
esrj-105296	197	226	,	,	PUNCT
esrj-105296	197	227	and	and	CCONJ
esrj-105296	197	228	3d-2d	3d-2d	NUM
esrj-105296	197	229	convolutional	convolutional	ADJ
esrj-105296	197	230	neural	neural	ADJ
esrj-105296	197	231	network	network	NOUN
esrj-105296	197	232	for	for	ADP
esrj-105296	197	233	hyperspectral	hyperspectral	ADJ
esrj-105296	197	234	image	image	NOUN
esrj-105296	197	235	classification	classification	NOUN
esrj-105296	197	236	the	the	DET
esrj-105296	197	237	evaluation	evaluation	NOUN
esrj-105296	197	238	of	of	ADP
esrj-105296	197	239	classification	classification	NOUN
esrj-105296	197	240	performance	performance	NOUN
esrj-105296	197	241	using	use	VERB
esrj-105296	197	242	the	the	DET
esrj-105296	197	243	houston	houston	PROPN
esrj-105296	197	244	2013	2013	NUM
esrj-105296	197	245	dataset	dataset	NOUN
esrj-105296	197	246	is	be	AUX
esrj-105296	197	247	summarized	summarize	VERB
esrj-105296	197	248	in	in	ADP
esrj-105296	197	249	table	table	NOUN
esrj-105296	197	250	7	7	NUM
esrj-105296	197	251	.	.	PUNCT
esrj-105296	198	1	notably	notably	ADV
esrj-105296	198	2	,	,	PUNCT
esrj-105296	198	3	the	the	DET
esrj-105296	198	4	modified	modify	VERB
esrj-105296	198	5	hybridsn	hybridsn	PROPN
esrj-105296	198	6	cnn	cnn	PROPN
esrj-105296	198	7	algorithm	algorithm	PROPN
esrj-105296	198	8	achieved	achieve	VERB
esrj-105296	198	9	the	the	DET
esrj-105296	198	10	highest	high	ADJ
esrj-105296	198	11	oa	oa	NOUN
esrj-105296	198	12	at	at	ADP
esrj-105296	198	13	98.29	98.29	NUM
esrj-105296	198	14	%	%	NOUN
esrj-105296	198	15	,	,	PUNCT
esrj-105296	198	16	contrasting	contrast	VERB
esrj-105296	198	17	with	with	ADP
esrj-105296	198	18	the	the	DET
esrj-105296	198	19	rf	rf	ADJ
esrj-105296	198	20	algorithm	algorithm	NOUN
esrj-105296	198	21	’s	’s	PART
esrj-105296	198	22	lowest	low	ADJ
esrj-105296	198	23	oa	oa	NOUN
esrj-105296	198	24	of	of	ADP
esrj-105296	198	25	92.72	92.72	NUM
esrj-105296	198	26	%	%	NOUN
esrj-105296	198	27	.	.	PUNCT
esrj-105296	199	1	when	when	SCONJ
esrj-105296	199	2	analyzing	analyze	VERB
esrj-105296	199	3	class	class	NOUN
esrj-105296	199	4	-	-	PUNCT
esrj-105296	199	5	based	base	VERB
esrj-105296	199	6	accuracy	accuracy	NOUN
esrj-105296	199	7	metrics	metric	NOUN
esrj-105296	199	8	,	,	PUNCT
esrj-105296	199	9	it	it	PRON
esrj-105296	199	10	is	be	AUX
esrj-105296	199	11	observed	observe	VERB
esrj-105296	199	12	that	that	SCONJ
esrj-105296	199	13	both	both	DET
esrj-105296	199	14	cnn	cnn	PROPN
esrj-105296	199	15	models	model	NOUN
esrj-105296	199	16	generally	generally	ADV
esrj-105296	199	17	exhibit	exhibit	VERB
esrj-105296	199	18	higher	high	ADJ
esrj-105296	199	19	accuracy	accuracy	NOUN
esrj-105296	199	20	values	value	NOUN
esrj-105296	199	21	and	and	CCONJ
esrj-105296	199	22	outperform	outperform	VERB
esrj-105296	199	23	other	other	ADJ
esrj-105296	199	24	algorithms	algorithm	NOUN
esrj-105296	199	25	,	,	PUNCT
esrj-105296	199	26	particularly	particularly	ADV
esrj-105296	199	27	in	in	ADP
esrj-105296	199	28	the	the	DET
esrj-105296	199	29	parking	parking	NOUN
esrj-105296	199	30	lot	lot	NOUN
esrj-105296	199	31	2	2	NUM
esrj-105296	199	32	class	class	NOUN
esrj-105296	199	33	.	.	PUNCT
esrj-105296	200	1	a	a	DET
esrj-105296	200	2	comparison	comparison	NOUN
esrj-105296	200	3	of	of	ADP
esrj-105296	200	4	cnn	cnn	PROPN
esrj-105296	200	5	algorithms	algorithms	PROPN
esrj-105296	200	6	reveals	reveal	VERB
esrj-105296	200	7	that	that	SCONJ
esrj-105296	200	8	their	their	PRON
esrj-105296	200	9	class	class	NOUN
esrj-105296	200	10	accuracies	accuracy	NOUN
esrj-105296	200	11	are	be	AUX
esrj-105296	200	12	similar	similar	ADJ
esrj-105296	200	13	.	.	PUNCT
esrj-105296	201	1	the	the	DET
esrj-105296	201	2	classification	classification	NOUN
esrj-105296	201	3	maps	map	NOUN
esrj-105296	201	4	of	of	ADP
esrj-105296	201	5	the	the	DET
esrj-105296	201	6	houston	houston	PROPN
esrj-105296	201	7	2013	2013	NUM
esrj-105296	201	8	data	datum	NOUN
esrj-105296	201	9	are	be	AUX
esrj-105296	201	10	given	give	VERB
esrj-105296	201	11	in	in	ADP
esrj-105296	201	12	figure	figure	NOUN
esrj-105296	201	13	4	4	NUM
esrj-105296	201	14	.	.	PUNCT
esrj-105296	201	15	overall	overall	ADV
esrj-105296	201	16	,	,	PUNCT
esrj-105296	201	17	the	the	DET
esrj-105296	201	18	algorithms	algorithm	NOUN
esrj-105296	201	19	extracted	extract	VERB
esrj-105296	201	20	the	the	DET
esrj-105296	201	21	shapes	shape	NOUN
esrj-105296	201	22	of	of	ADP
esrj-105296	201	23	human	human	NOUN
esrj-105296	201	24	-	-	PUNCT
esrj-105296	201	25	made	make	VERB
esrj-105296	201	26	objects	object	NOUN
esrj-105296	201	27	successfully	successfully	ADV
esrj-105296	201	28	,	,	PUNCT
esrj-105296	201	29	taking	take	VERB
esrj-105296	201	30	the	the	DET
esrj-105296	201	31	advantage	advantage	NOUN
esrj-105296	201	32	of	of	ADP
esrj-105296	201	33	the	the	DET
esrj-105296	201	34	superior	superior	ADJ
esrj-105296	201	35	spatial	spatial	ADJ
esrj-105296	201	36	and	and	CCONJ
esrj-105296	201	37	spectral	spectral	ADJ
esrj-105296	201	38	resolution	resolution	NOUN
esrj-105296	201	39	of	of	ADP
esrj-105296	201	40	the	the	DET
esrj-105296	201	41	dataset	dataset	NOUN
esrj-105296	201	42	.	.	PUNCT
esrj-105296	202	1	however	however	ADV
esrj-105296	202	2	,	,	PUNCT
esrj-105296	202	3	a	a	DET
esrj-105296	202	4	salt	salt	NOUN
esrj-105296	202	5	-	-	PUNCT
esrj-105296	202	6	and	and	CCONJ
esrj-105296	202	7	-	-	PUNCT
esrj-105296	202	8	pepper	pepper	NOUN
esrj-105296	202	9	effect	effect	NOUN
esrj-105296	202	10	was	be	AUX
esrj-105296	202	11	noticeable	noticeable	ADJ
esrj-105296	202	12	in	in	ADP
esrj-105296	202	13	classification	classification	NOUN
esrj-105296	202	14	maps	map	NOUN
esrj-105296	202	15	of	of	ADP
esrj-105296	202	16	the	the	DET
esrj-105296	202	17	svm	svm	PROPN
esrj-105296	202	18	and	and	CCONJ
esrj-105296	202	19	the	the	DET
esrj-105296	202	20	rf	rf	NOUN
esrj-105296	202	21	,	,	PUNCT
esrj-105296	202	22	stemming	stem	VERB
esrj-105296	202	23	from	from	ADP
esrj-105296	202	24	pixel	pixel	ADJ
esrj-105296	202	25	-	-	ADJ
esrj-105296	202	26	wise	wise	ADJ
esrj-105296	202	27	classification	classification	NOUN
esrj-105296	202	28	.	.	PUNCT
esrj-105296	203	1	misclassified	misclassifie	VERB
esrj-105296	203	2	pixels	pixel	NOUN
esrj-105296	203	3	were	be	AUX
esrj-105296	203	4	observed	observe	VERB
esrj-105296	203	5	,	,	PUNCT
esrj-105296	203	6	particularly	particularly	ADV
esrj-105296	203	7	by	by	ADP
esrj-105296	203	8	svm	svm	PROPN
esrj-105296	203	9	and	and	CCONJ
esrj-105296	203	10	rf	rf	VERB
esrj-105296	203	11	,	,	PUNCT
esrj-105296	203	12	in	in	ADP
esrj-105296	203	13	areas	area	NOUN
esrj-105296	203	14	such	such	ADJ
esrj-105296	203	15	as	as	ADP
esrj-105296	203	16	stadium	stadium	ADJ
esrj-105296	203	17	corners	corner	NOUN
esrj-105296	203	18	and	and	CCONJ
esrj-105296	203	19	regions	region	NOUN
esrj-105296	203	20	affected	affect	VERB
esrj-105296	203	21	by	by	ADP
esrj-105296	203	22	cloud	cloud	NOUN
esrj-105296	203	23	shadows	shadow	NOUN
esrj-105296	203	24	,	,	PUNCT
esrj-105296	203	25	resulting	result	VERB
esrj-105296	203	26	in	in	ADP
esrj-105296	203	27	significant	significant	ADJ
esrj-105296	203	28	pixel	pixel	NOUN
esrj-105296	203	29	misclassification	misclassification	NOUN
esrj-105296	203	30	.	.	PUNCT
esrj-105296	204	1	notably	notably	ADV
esrj-105296	204	2	,	,	PUNCT
esrj-105296	204	3	the	the	DET
esrj-105296	204	4	shaded	shaded	ADJ
esrj-105296	204	5	area	area	NOUN
esrj-105296	204	6	has	have	AUX
esrj-105296	204	7	proven	prove	VERB
esrj-105296	204	8	difficult	difficult	ADJ
esrj-105296	204	9	for	for	ADP
esrj-105296	204	10	all	all	DET
esrj-105296	204	11	algorithms	algorithm	NOUN
esrj-105296	204	12	,	,	PUNCT
esrj-105296	204	13	likely	likely	ADJ
esrj-105296	204	14	due	due	ADP
esrj-105296	204	15	to	to	ADP
esrj-105296	204	16	insufficient	insufficient	ADJ
esrj-105296	204	17	training	training	NOUN
esrj-105296	204	18	data	datum	NOUN
esrj-105296	204	19	for	for	ADP
esrj-105296	204	20	this	this	DET
esrj-105296	204	21	region	region	NOUN
esrj-105296	204	22	.	.	PUNCT
esrj-105296	205	1	this	this	PRON
esrj-105296	205	2	indicates	indicate	VERB
esrj-105296	205	3	that	that	SCONJ
esrj-105296	205	4	the	the	DET
esrj-105296	205	5	lighting	lighting	NOUN
esrj-105296	205	6	conditions	condition	NOUN
esrj-105296	205	7	of	of	ADP
esrj-105296	205	8	the	the	DET
esrj-105296	205	9	area	area	NOUN
esrj-105296	205	10	being	be	AUX
esrj-105296	205	11	classified	classify	VERB
esrj-105296	205	12	have	have	VERB
esrj-105296	205	13	a	a	DET
esrj-105296	205	14	direct	direct	ADJ
esrj-105296	205	15	impact	impact	NOUN
esrj-105296	205	16	on	on	ADP
esrj-105296	205	17	classification	classification	NOUN
esrj-105296	205	18	performance	performance	NOUN
esrj-105296	205	19	.	.	PUNCT
esrj-105296	206	1	moreover	moreover	ADV
esrj-105296	206	2	,	,	PUNCT
esrj-105296	206	3	it	it	PRON
esrj-105296	206	4	is	be	AUX
esrj-105296	206	5	evident	evident	ADJ
esrj-105296	206	6	that	that	SCONJ
esrj-105296	206	7	the	the	DET
esrj-105296	206	8	modified	modify	VERB
esrj-105296	206	9	hybridsn	hybridsn	PROPN
esrj-105296	206	10	cnn	cnn	PROPN
esrj-105296	206	11	algorithm	algorithm	PROPN
esrj-105296	206	12	performs	perform	VERB
esrj-105296	206	13	better	well	ADV
esrj-105296	206	14	at	at	ADP
esrj-105296	206	15	building	build	VERB
esrj-105296	206	16	classification	classification	NOUN
esrj-105296	206	17	in	in	ADP
esrj-105296	206	18	the	the	DET
esrj-105296	206	19	shaded	shaded	ADJ
esrj-105296	206	20	area	area	NOUN
esrj-105296	206	21	than	than	ADP
esrj-105296	206	22	the	the	DET
esrj-105296	206	23	hybridsn	hybridsn	PROPN
esrj-105296	206	24	cnn	cnn	PROPN
esrj-105296	206	25	algorithm	algorithm	PROPN
esrj-105296	206	26	.	.	PUNCT
esrj-105296	207	1	table	table	NOUN
esrj-105296	207	2	8	8	NUM
esrj-105296	207	3	represents	represent	VERB
esrj-105296	207	4	the	the	DET
esrj-105296	207	5	performance	performance	NOUN
esrj-105296	207	6	analysis	analysis	NOUN
esrj-105296	207	7	results	result	NOUN
esrj-105296	207	8	for	for	ADP
esrj-105296	207	9	the	the	DET
esrj-105296	207	10	salinas	salinas	PROPN
esrj-105296	207	11	scene	scene	PROPN
esrj-105296	207	12	dataset	dataset	PROPN
esrj-105296	207	13	.	.	PUNCT
esrj-105296	208	1	based	base	VERB
esrj-105296	208	2	on	on	ADP
esrj-105296	208	3	the	the	DET
esrj-105296	208	4	calculated	calculated	ADJ
esrj-105296	208	5	oa	oa	X
esrj-105296	208	6	and	and	CCONJ
esrj-105296	208	7	κ	κ	NOUN
esrj-105296	208	8	values	value	NOUN
esrj-105296	208	9	,	,	PUNCT
esrj-105296	208	10	the	the	DET
esrj-105296	208	11	modified	modify	VERB
esrj-105296	208	12	hybridsn	hybridsn	PROPN
esrj-105296	208	13	cnn	cnn	PROPN
esrj-105296	208	14	algorithm	algorithm	PROPN
esrj-105296	208	15	outperformed	outperform	VERB
esrj-105296	208	16	svm	svm	PROPN
esrj-105296	208	17	and	and	CCONJ
esrj-105296	208	18	rf	rf	NOUN
esrj-105296	208	19	,	,	PUNCT
esrj-105296	208	20	achieving	achieve	VERB
esrj-105296	208	21	an	an	DET
esrj-105296	208	22	oa	oa	NOUN
esrj-105296	208	23	of	of	ADP
esrj-105296	208	24	99.85	99.85	NUM
esrj-105296	208	25	%	%	NOUN
esrj-105296	208	26	and	and	CCONJ
esrj-105296	208	27	a	a	DET
esrj-105296	208	28	κ	κ	NOUN
esrj-105296	208	29	value	value	NOUN
esrj-105296	208	30	of	of	ADP
esrj-105296	208	31	99.83	99.83	NUM
esrj-105296	208	32	.	.	PUNCT
esrj-105296	209	1	overall	overall	ADV
esrj-105296	209	2	,	,	PUNCT
esrj-105296	209	3	the	the	DET
esrj-105296	209	4	modified	modify	VERB
esrj-105296	209	5	hybridsn	hybridsn	PROPN
esrj-105296	209	6	cnn	cnn	PROPN
esrj-105296	209	7	algorithm	algorithm	PROPN
esrj-105296	209	8	exhibits	exhibit	VERB
esrj-105296	209	9	slightly	slightly	ADV
esrj-105296	209	10	higher	high	ADJ
esrj-105296	209	11	class	class	NOUN
esrj-105296	209	12	-	-	PUNCT
esrj-105296	209	13	based	base	VERB
esrj-105296	209	14	performance	performance	NOUN
esrj-105296	209	15	metrics	metric	NOUN
esrj-105296	209	16	compared	compare	VERB
esrj-105296	209	17	to	to	ADP
esrj-105296	209	18	other	other	ADJ
esrj-105296	209	19	algorithms	algorithm	NOUN
esrj-105296	209	20	.	.	PUNCT
esrj-105296	210	1	notably	notably	ADV
esrj-105296	210	2	,	,	PUNCT
esrj-105296	210	3	in	in	ADP
esrj-105296	210	4	classes	class	NOUN
esrj-105296	210	5	such	such	ADJ
esrj-105296	210	6	as	as	ADP
esrj-105296	210	7	grapes_untrained	grapes_untrained	ADJ
esrj-105296	210	8	and	and	CCONJ
esrj-105296	210	9	vinyard_untrained	vinyard_untraine	VERB
esrj-105296	210	10	,	,	PUNCT
esrj-105296	210	11	cnn	cnn	PROPN
esrj-105296	210	12	outperformed	outperform	VERB
esrj-105296	210	13	other	other	ADJ
esrj-105296	210	14	algorithms	algorithm	NOUN
esrj-105296	210	15	.	.	PUNCT
esrj-105296	211	1	upon	upon	SCONJ
esrj-105296	211	2	reviewing	review	VERB
esrj-105296	211	3	processing	processing	NOUN
esrj-105296	211	4	times	time	NOUN
esrj-105296	211	5	,	,	PUNCT
esrj-105296	211	6	cnn	cnn	PROPN
esrj-105296	211	7	stands	stand	VERB
esrj-105296	211	8	out	out	ADP
esrj-105296	211	9	for	for	ADP
esrj-105296	211	10	its	its	PRON
esrj-105296	211	11	shortest	short	ADJ
esrj-105296	211	12	processing	processing	NOUN
esrj-105296	211	13	time	time	NOUN
esrj-105296	211	14	,	,	PUNCT
esrj-105296	211	15	in	in	ADP
esrj-105296	211	16	terms	term	NOUN
esrj-105296	211	17	of	of	ADP
esrj-105296	211	18	both	both	DET
esrj-105296	211	19	training	training	NOUN
esrj-105296	211	20	and	and	CCONJ
esrj-105296	211	21	classifying	classify	VERB
esrj-105296	211	22	the	the	DET
esrj-105296	211	23	whole	whole	ADJ
esrj-105296	211	24	scene	scene	NOUN
esrj-105296	211	25	.	.	PUNCT
esrj-105296	212	1	a	a	DET
esrj-105296	212	2	b	b	X
esrj-105296	212	3	c	c	NOUN
esrj-105296	212	4	d	d	X
esrj-105296	212	5	e	e	X
esrj-105296	212	6	figure	figure	NOUN
esrj-105296	212	7	4	4	NUM
esrj-105296	212	8	.	.	PUNCT
esrj-105296	212	9	classification	classification	NOUN
esrj-105296	212	10	maps	map	NOUN
esrj-105296	212	11	for	for	ADP
esrj-105296	212	12	the	the	DET
esrj-105296	212	13	houston	houston	PROPN
esrj-105296	212	14	2013	2013	NUM
esrj-105296	212	15	dataset	dataset	NOUN
esrj-105296	212	16	:	:	PUNCT
esrj-105296	212	17	(	(	PUNCT
esrj-105296	212	18	a	a	X
esrj-105296	212	19	)	)	PUNCT
esrj-105296	212	20	svm	svm	PROPN
esrj-105296	212	21	,	,	PUNCT
esrj-105296	212	22	(	(	PUNCT
esrj-105296	212	23	b	b	NOUN
esrj-105296	212	24	)	)	PUNCT
esrj-105296	212	25	rf	rf	NOUN
esrj-105296	212	26	,	,	PUNCT
esrj-105296	212	27	(	(	PUNCT
esrj-105296	212	28	c	c	X
esrj-105296	212	29	)	)	PUNCT
esrj-105296	212	30	hybridsn	hybridsn	PROPN
esrj-105296	212	31	cnn	cnn	PROPN
esrj-105296	212	32	,	,	PUNCT
esrj-105296	212	33	(	(	PUNCT
esrj-105296	212	34	d	d	X
esrj-105296	212	35	)	)	PUNCT
esrj-105296	212	36	modified	modify	VERB
esrj-105296	212	37	hybridsn	hybridsn	PROPN
esrj-105296	212	38	cnn	cnn	PROPN
esrj-105296	212	39	,	,	PUNCT
esrj-105296	212	40	and	and	CCONJ
esrj-105296	212	41	(	(	PUNCT
esrj-105296	212	42	e	e	NOUN
esrj-105296	212	43	)	)	PUNCT
esrj-105296	212	44	true	true	ADJ
esrj-105296	212	45	color	color	NOUN
esrj-105296	212	46	composition	composition	NOUN
esrj-105296	212	47	table	table	NOUN
esrj-105296	212	48	8	8	NUM
esrj-105296	212	49	.	.	PUNCT
esrj-105296	213	1	the	the	DET
esrj-105296	213	2	performance	performance	NOUN
esrj-105296	213	3	analysis	analysis	NOUN
esrj-105296	213	4	of	of	ADP
esrj-105296	213	5	the	the	DET
esrj-105296	213	6	classification	classification	NOUN
esrj-105296	213	7	algorithms	algorithm	NOUN
esrj-105296	213	8	on	on	ADP
esrj-105296	213	9	salinas	salinas	PROPN
esrj-105296	213	10	scene	scene	PROPN
esrj-105296	213	11	hsi	hsi	PROPN
esrj-105296	213	12	dataset	dataset	PROPN
esrj-105296	213	13	.	.	PUNCT
esrj-105296	214	1	class	class	NOUN
esrj-105296	214	2	name	name	NOUN
esrj-105296	214	3	svm	svm	NOUN
esrj-105296	214	4	rf	rf	PART
esrj-105296	214	5	hybridsn	hybridsn	PROPN
esrj-105296	214	6	cnn	cnn	PROPN
esrj-105296	214	7	modified	modify	VERB
esrj-105296	214	8	hybridsn	hybridsn	PROPN
esrj-105296	215	1	cnn	cnn	PROPN
esrj-105296	215	2	pa	pa	PROPN
esrj-105296	215	3	ua	ua	PROPN
esrj-105296	215	4	f	f	PROPN
esrj-105296	215	5	score	score	NOUN
esrj-105296	215	6	pa	pa	PROPN
esrj-105296	215	7	ua	ua	PROPN
esrj-105296	215	8	f	f	PROPN
esrj-105296	215	9	score	score	NOUN
esrj-105296	215	10	pa	pa	PROPN
esrj-105296	215	11	ua	ua	PROPN
esrj-105296	215	12	f	f	PROPN
esrj-105296	215	13	score	score	NOUN
esrj-105296	215	14	pa	pa	PROPN
esrj-105296	215	15	ua	ua	PROPN
esrj-105296	215	16	f	f	PROPN
esrj-105296	215	17	score	score	VERB
esrj-105296	215	18	brocoli_green_weeds_1	brocoli_green_weeds_1	PROPN
esrj-105296	215	19	100.00	100.00	NUM
esrj-105296	215	20	100.00	100.00	NUM
esrj-105296	215	21	100.00	100.00	NUM
esrj-105296	215	22	100.00	100.00	NUM
esrj-105296	215	23	99.83	99.83	NUM
esrj-105296	215	24	99.92	99.92	NUM
esrj-105296	215	25	100.00	100.00	NUM
esrj-105296	215	26	100.00	100.00	NUM
esrj-105296	215	27	100.00	100.00	NUM
esrj-105296	215	28	100.00	100.00	NUM
esrj-105296	215	29	100.00	100.00	NUM
esrj-105296	215	30	100.00	100.00	NUM
esrj-105296	215	31	brocoli_green_weeds_2	brocoli_green_weeds_2	PROPN
esrj-105296	215	32	100.00	100.00	NUM
esrj-105296	215	33	99.94	99.94	NUM
esrj-105296	215	34	99.97	99.97	NUM
esrj-105296	215	35	99.79	99.79	NUM
esrj-105296	215	36	99.88	99.88	NUM
esrj-105296	215	37	99.84	99.84	NUM
esrj-105296	215	38	100.00	100.00	NUM
esrj-105296	215	39	100.00	100.00	NUM
esrj-105296	215	40	100.00	100.00	NUM
esrj-105296	215	41	100.00	100.00	NUM
esrj-105296	215	42	100.00	100.00	NUM
esrj-105296	215	43	100.00	100.00	NUM
esrj-105296	215	44	fallow	fallow	NOUN
esrj-105296	215	45	98.88	98.88	NUM
esrj-105296	215	46	99.49	99.49	NUM
esrj-105296	215	47	99.19	99.19	NUM
esrj-105296	215	48	96.67	96.67	NUM
esrj-105296	215	49	99.66	99.66	NUM
esrj-105296	215	50	98.15	98.15	NUM
esrj-105296	215	51	100.00	100.00	NUM
esrj-105296	215	52	100.00	100.00	NUM
esrj-105296	215	53	100.00	100.00	NUM
esrj-105296	215	54	99.94	99.94	NUM
esrj-105296	215	55	100.00	100.00	NUM
esrj-105296	215	56	99.97	99.97	NUM
esrj-105296	215	57	fallow_rough_plow	fallow_rough_plow	ADP
esrj-105296	215	58	98.89	98.89	NUM
esrj-105296	215	59	99.68	99.68	NUM
esrj-105296	215	60	99.29	99.29	NUM
esrj-105296	215	61	98.89	98.89	NUM
esrj-105296	215	62	99.68	99.68	NUM
esrj-105296	215	63	99.29	99.29	NUM
esrj-105296	215	64	99.35	99.35	NUM
esrj-105296	215	65	97.93	97.93	NUM
esrj-105296	215	66	98.64	98.64	NUM
esrj-105296	215	67	99.44	99.44	NUM
esrj-105296	215	68	99.76	99.76	NUM
esrj-105296	215	69	99.60	99.60	NUM
esrj-105296	215	70	(	(	PUNCT
esrj-105296	215	71	continued	continue	VERB
esrj-105296	215	72	)	)	PUNCT
esrj-105296	215	73	170	170	NUM
esrj-105296	215	74	eren	eren	PROPN
esrj-105296	215	75	can	can	AUX
esrj-105296	215	76	seyrek	seyrek	VERB
esrj-105296	215	77	,	,	PUNCT
esrj-105296	215	78	murat	murat	PROPN
esrj-105296	215	79	uysal	uysal	ADJ
esrj-105296	215	80	class	class	NOUN
esrj-105296	215	81	name	name	NOUN
esrj-105296	215	82	svm	svm	NOUN
esrj-105296	215	83	rf	rf	PRON
esrj-105296	215	84	hybridsn	hybridsn	PROPN
esrj-105296	215	85	cnn	cnn	PROPN
esrj-105296	215	86	modified	modify	VERB
esrj-105296	215	87	hybridsn	hybridsn	PROPN
esrj-105296	216	1	cnn	cnn	PROPN
esrj-105296	216	2	pa	pa	PROPN
esrj-105296	216	3	ua	ua	PROPN
esrj-105296	216	4	f	f	PROPN
esrj-105296	216	5	score	score	NOUN
esrj-105296	216	6	pa	pa	PROPN
esrj-105296	216	7	ua	ua	PROPN
esrj-105296	216	8	f	f	PROPN
esrj-105296	216	9	score	score	NOUN
esrj-105296	216	10	pa	pa	PROPN
esrj-105296	216	11	ua	ua	PROPN
esrj-105296	216	12	f	f	PROPN
esrj-105296	216	13	score	score	NOUN
esrj-105296	216	14	pa	pa	PROPN
esrj-105296	216	15	ua	ua	PROPN
esrj-105296	216	16	f	f	PROPN
esrj-105296	216	17	score	score	VERB
esrj-105296	216	18	fallow_smooth	fallow_smooth	ADV
esrj-105296	216	19	98.67	98.67	NUM
esrj-105296	216	20	98.88	98.88	NUM
esrj-105296	216	21	98.78	98.78	NUM
esrj-105296	216	22	99.62	99.62	NUM
esrj-105296	216	23	98.55	98.55	NUM
esrj-105296	216	24	99.08	99.08	NUM
esrj-105296	216	25	98.89	98.89	NUM
esrj-105296	216	26	100.00	100.00	NUM
esrj-105296	216	27	99.44	99.44	NUM
esrj-105296	216	28	99.88	99.88	NUM
esrj-105296	216	29	99.71	99.71	NUM
esrj-105296	216	30	99.79	99.79	NUM
esrj-105296	216	31	stubble	stubble	ADJ
esrj-105296	216	32	100.00	100.00	NUM
esrj-105296	216	33	99.92	99.92	NUM
esrj-105296	216	34	99.96	99.96	NUM
esrj-105296	216	35	100.00	100.00	NUM
esrj-105296	216	36	99.78	99.78	NUM
esrj-105296	216	37	99.89	99.89	NUM
esrj-105296	216	38	100.00	100.00	NUM
esrj-105296	216	39	100.00	100.00	NUM
esrj-105296	216	40	100.00	100.00	NUM
esrj-105296	216	41	100.00	100.00	NUM
esrj-105296	216	42	100.00	100.00	NUM
esrj-105296	216	43	100.00	100.00	NUM
esrj-105296	216	44	celery	celery	NOUN
esrj-105296	216	45	99.78	99.78	NUM
esrj-105296	216	46	99.97	99.97	NUM
esrj-105296	216	47	99.88	99.88	NUM
esrj-105296	216	48	100.00	100.00	NUM
esrj-105296	216	49	99.50	99.50	NUM
esrj-105296	216	50	99.75	99.75	NUM
esrj-105296	216	51	100.00	100.00	NUM
esrj-105296	216	52	99.84	99.84	NUM
esrj-105296	216	53	99.92	99.92	NUM
esrj-105296	216	54	100.00	100.00	NUM
esrj-105296	216	55	100.00	100.00	NUM
esrj-105296	216	56	100.00	100.00	NUM
esrj-105296	216	57	grapes_untrained	grapes_untraine	VERB
esrj-105296	216	58	83.02	83.02	NUM
esrj-105296	216	59	91.41	91.41	NUM
esrj-105296	216	60	87.01	87.01	NUM
esrj-105296	216	61	81.03	81.03	NUM
esrj-105296	216	62	91.01	91.01	NUM
esrj-105296	216	63	85.73	85.73	NUM
esrj-105296	216	64	99.88	99.88	NUM
esrj-105296	216	65	98.37	98.37	NUM
esrj-105296	216	66	99.12	99.12	NUM
esrj-105296	216	67	99.89	99.89	NUM
esrj-105296	216	68	99.52	99.52	NUM
esrj-105296	216	69	99.70	99.70	NUM
esrj-105296	216	70	soil_vinyard_develop	soil_vinyard_develop	NOUN
esrj-105296	216	71	99.34	99.34	NUM
esrj-105296	216	72	99.96	99.96	NUM
esrj-105296	216	73	99.65	99.65	NUM
esrj-105296	216	74	99.13	99.13	NUM
esrj-105296	216	75	99.87	99.87	NUM
esrj-105296	216	76	99.50	99.50	NUM
esrj-105296	216	77	100.00	100.00	NUM
esrj-105296	216	78	100.00	100.00	NUM
esrj-105296	216	79	100.00	100.00	NUM
esrj-105296	216	80	99.98	99.98	NUM
esrj-105296	216	81	100.00	100.00	NUM
esrj-105296	216	82	99.99	99.99	NUM
esrj-105296	216	83	corn_senesced_green_weeds	corn_senesced_green_weed	VERB
esrj-105296	216	84	98.41	98.41	NUM
esrj-105296	216	85	96.54	96.54	NUM
esrj-105296	216	86	97.47	97.47	NUM
esrj-105296	216	87	97.23	97.23	NUM
esrj-105296	216	88	95.22	95.22	NUM
esrj-105296	216	89	96.22	96.22	NUM
esrj-105296	216	90	99.63	99.63	NUM
esrj-105296	216	91	99.53	99.53	NUM
esrj-105296	216	92	99.58	99.58	NUM
esrj-105296	216	93	100.00	100.00	NUM
esrj-105296	216	94	99.86	99.86	NUM
esrj-105296	216	95	99.93	99.93	NUM
esrj-105296	216	96	lettuce_romaine_4wk	lettuce_romaine_4wk	PROPN
esrj-105296	216	97	96.94	96.94	NUM
esrj-105296	216	98	98.75	98.75	NUM
esrj-105296	216	99	97.84	97.84	NUM
esrj-105296	216	100	94.88	94.88	NUM
esrj-105296	216	101	96.46	96.46	NUM
esrj-105296	216	102	95.67	95.67	NUM
esrj-105296	216	103	98.26	98.26	NUM
esrj-105296	216	104	100.00	100.00	NUM
esrj-105296	216	105	99.12	99.12	NUM
esrj-105296	216	106	99.69	99.69	NUM
esrj-105296	216	107	100.00	100.00	NUM
esrj-105296	216	108	99.84	99.84	NUM
esrj-105296	216	109	lettuce_romaine_5wk	lettuce_romaine_5wk	NOUN
esrj-105296	216	110	99.48	99.48	NUM
esrj-105296	216	111	99.77	99.77	NUM
esrj-105296	216	112	99.63	99.63	NUM
esrj-105296	216	113	98.30	98.30	NUM
esrj-105296	216	114	100.00	100.00	NUM
esrj-105296	216	115	99.14	99.14	NUM
esrj-105296	216	116	100.00	100.00	NUM
esrj-105296	216	117	99.83	99.83	NUM
esrj-105296	216	118	99.91	99.91	NUM
esrj-105296	216	119	100.00	100.00	NUM
esrj-105296	216	120	100.00	100.00	NUM
esrj-105296	216	121	100.00	100.00	NUM
esrj-105296	216	122	lettuce_romaine_6wk	lettuce_romaine_6wk	NOUN
esrj-105296	216	123	98.67	98.67	NUM
esrj-105296	216	124	98.91	98.91	NUM
esrj-105296	216	125	98.79	98.79	NUM
esrj-105296	216	126	98.44	98.44	NUM
esrj-105296	216	127	99.15	99.15	NUM
esrj-105296	216	128	98.79	98.79	NUM
esrj-105296	216	129	100.00	100.00	NUM
esrj-105296	216	130	99.88	99.88	NUM
esrj-105296	216	131	99.94	99.94	NUM
esrj-105296	216	132	100.00	100.00	NUM
esrj-105296	216	133	100.00	100.00	NUM
esrj-105296	216	134	100.00	100.00	NUM
esrj-105296	216	135	lettuce_romaine_7wk	lettuce_romaine_7wk	NOUN
esrj-105296	216	136	96.90	96.90	NUM
esrj-105296	216	137	97.30	97.30	NUM
esrj-105296	216	138	97.10	97.10	NUM
esrj-105296	216	139	98.62	98.62	NUM
esrj-105296	216	140	96.26	96.26	NUM
esrj-105296	216	141	97.43	97.43	NUM
esrj-105296	216	142	99.90	99.90	NUM
esrj-105296	216	143	100.00	100.00	NUM
esrj-105296	216	144	99.95	99.95	NUM
esrj-105296	216	145	100.00	100.00	NUM
esrj-105296	216	146	100.00	100.00	NUM
esrj-105296	216	147	100.00	100.00	NUM
esrj-105296	216	148	vinyard_untrained	vinyard_untraine	VERB
esrj-105296	216	149	84.71	84.71	NUM
esrj-105296	216	150	70.89	70.89	NUM
esrj-105296	216	151	77.19	77.19	NUM
esrj-105296	216	152	83.29	83.29	NUM
esrj-105296	216	153	67.07	67.07	NUM
esrj-105296	216	154	74.31	74.31	NUM
esrj-105296	216	155	97.70	97.70	NUM
esrj-105296	216	156	99.82	99.82	NUM
esrj-105296	216	157	98.74	98.74	NUM
esrj-105296	216	158	99.27	99.27	NUM
esrj-105296	216	159	99.83	99.83	NUM
esrj-105296	216	160	99.55	99.55	NUM
esrj-105296	216	161	vinyard_vertical_trellis	vinyard_vertical_trellis	X
esrj-105296	217	1	99.33	99.33	NUM
esrj-105296	217	2	99.69	99.69	NUM
esrj-105296	217	3	99.51	99.51	NUM
esrj-105296	217	4	99.08	99.08	NUM
esrj-105296	217	5	99.08	99.08	NUM
esrj-105296	217	6	99.08	99.08	NUM
esrj-105296	217	7	99.75	99.75	NUM
esrj-105296	217	8	99.51	99.51	NUM
esrj-105296	217	9	99.63	99.63	NUM
esrj-105296	217	10	100.00	100.00	NUM
esrj-105296	217	11	100.00	100.00	NUM
esrj-105296	217	12	100.00	100.00	NUM
esrj-105296	217	13	oa	oa	ADP
esrj-105296	217	14	93.88	93.88	NUM
esrj-105296	217	15	93.06	93.06	NUM
esrj-105296	217	16	99.52	99.52	NUM
esrj-105296	217	17	99.85	99.85	NUM
esrj-105296	217	18	κ	κ	PROPN
esrj-105296	217	19	93.18	93.18	NUM
esrj-105296	217	20	92.25	92.25	NUM
esrj-105296	217	21	99.47	99.47	NUM
esrj-105296	217	22	99.83	99.83	NUM
esrj-105296	217	23	training	training	NOUN
esrj-105296	217	24	time	time	NOUN
esrj-105296	217	25	(	(	PUNCT
esrj-105296	217	26	s	s	NOUN
esrj-105296	217	27	)	)	PUNCT
esrj-105296	217	28	61.45	61.45	NUM
esrj-105296	217	29	56.56	56.56	NUM
esrj-105296	217	30	26.84	26.84	NUM
esrj-105296	217	31	25.38	25.38	NUM
esrj-105296	217	32	classification	classification	NOUN
esrj-105296	217	33	time	time	NOUN
esrj-105296	217	34	(	(	PUNCT
esrj-105296	217	35	s	s	NOUN
esrj-105296	217	36	)	)	PUNCT
esrj-105296	217	37	16.07	16.07	NUM
esrj-105296	217	38	10.27	10.27	NUM
esrj-105296	217	39	6.76	6.76	NUM
esrj-105296	217	40	6.85	6.85	NUM
esrj-105296	217	41	figure	figure	NOUN
esrj-105296	217	42	5	5	NUM
esrj-105296	217	43	illustrates	illustrate	VERB
esrj-105296	217	44	the	the	DET
esrj-105296	217	45	classification	classification	NOUN
esrj-105296	217	46	maps	map	NOUN
esrj-105296	217	47	generated	generate	VERB
esrj-105296	217	48	for	for	ADP
esrj-105296	217	49	the	the	DET
esrj-105296	217	50	salinas	salinas	PROPN
esrj-105296	217	51	scene	scene	NOUN
esrj-105296	217	52	dataset	dataset	VERB
esrj-105296	217	53	.	.	PUNCT
esrj-105296	218	1	despite	despite	SCONJ
esrj-105296	218	2	algorithms	algorithm	NOUN
esrj-105296	218	3	achieving	achieve	VERB
esrj-105296	218	4	high	high	ADJ
esrj-105296	218	5	oa	oa	NOUN
esrj-105296	218	6	,	,	PUNCT
esrj-105296	218	7	instances	instance	NOUN
esrj-105296	218	8	of	of	ADP
esrj-105296	218	9	misclassifications	misclassification	NOUN
esrj-105296	218	10	were	be	AUX
esrj-105296	218	11	observed	observe	VERB
esrj-105296	218	12	,	,	PUNCT
esrj-105296	218	13	particularly	particularly	ADV
esrj-105296	218	14	in	in	ADP
esrj-105296	218	15	the	the	DET
esrj-105296	218	16	grapes_untrained	grapes_untrained	ADJ
esrj-105296	218	17	and	and	CCONJ
esrj-105296	218	18	vinyard_untrained	vinyard_untraine	VERB
esrj-105296	218	19	classes	class	NOUN
esrj-105296	218	20	.	.	PUNCT
esrj-105296	219	1	notably	notably	ADV
esrj-105296	219	2	,	,	PUNCT
esrj-105296	219	3	the	the	DET
esrj-105296	219	4	classification	classification	NOUN
esrj-105296	219	5	map	map	NOUN
esrj-105296	219	6	of	of	ADP
esrj-105296	219	7	both	both	DET
esrj-105296	219	8	cnn	cnn	PROPN
esrj-105296	219	9	algorithms	algorithm	NOUN
esrj-105296	219	10	contains	contain	VERB
esrj-105296	219	11	less	less	ADJ
esrj-105296	219	12	salt	salt	NOUN
esrj-105296	219	13	-	-	PUNCT
esrj-105296	219	14	and	and	CCONJ
esrj-105296	219	15	-	-	PUNCT
esrj-105296	219	16	pepper	pepper	NOUN
esrj-105296	219	17	effects	effect	NOUN
esrj-105296	219	18	compared	compare	VERB
esrj-105296	219	19	to	to	ADP
esrj-105296	219	20	other	other	ADJ
esrj-105296	219	21	classification	classification	NOUN
esrj-105296	219	22	maps	map	NOUN
esrj-105296	219	23	.	.	PUNCT
esrj-105296	220	1	analysis	analysis	NOUN
esrj-105296	220	2	based	base	VERB
esrj-105296	220	3	on	on	ADP
esrj-105296	220	4	ground	ground	NOUN
esrj-105296	220	5	truth	truth	NOUN
esrj-105296	220	6	photos	photo	NOUN
esrj-105296	220	7	by	by	ADP
esrj-105296	220	8	gualtieri	gualtieri	PROPN
esrj-105296	220	9	et	et	PROPN
esrj-105296	220	10	al	al	PROPN
esrj-105296	220	11	.	.	PROPN
esrj-105296	221	1	(	(	PUNCT
esrj-105296	221	2	1999	1999	NUM
esrj-105296	221	3	)	)	PUNCT
esrj-105296	221	4	suggests	suggest	VERB
esrj-105296	221	5	that	that	SCONJ
esrj-105296	221	6	confusion	confusion	NOUN
esrj-105296	221	7	,	,	PUNCT
esrj-105296	221	8	particularly	particularly	ADV
esrj-105296	221	9	in	in	ADP
esrj-105296	221	10	mixed	mixed	ADJ
esrj-105296	221	11	classes	class	NOUN
esrj-105296	221	12	,	,	PUNCT
esrj-105296	221	13	is	be	AUX
esrj-105296	221	14	expected	expect	VERB
esrj-105296	221	15	due	due	ADP
esrj-105296	221	16	to	to	ADP
esrj-105296	221	17	similar	similar	ADJ
esrj-105296	221	18	texture	texture	NOUN
esrj-105296	221	19	and	and	CCONJ
esrj-105296	221	20	color	color	NOUN
esrj-105296	221	21	characteristics	characteristic	NOUN
esrj-105296	221	22	.	.	PUNCT
esrj-105296	222	1	a	a	DET
esrj-105296	222	2	statistical	statistical	ADJ
esrj-105296	222	3	analysis	analysis	NOUN
esrj-105296	222	4	comparing	compare	VERB
esrj-105296	222	5	the	the	DET
esrj-105296	222	6	classification	classification	NOUN
esrj-105296	222	7	algorithms	algorithm	NOUN
esrj-105296	222	8	using	use	VERB
esrj-105296	222	9	mcnemar	mcnemar	PROPN
esrj-105296	222	10	’s	’s	PART
esrj-105296	222	11	test	test	NOUN
esrj-105296	222	12	was	be	AUX
esrj-105296	222	13	performed	perform	VERB
esrj-105296	222	14	.	.	PUNCT
esrj-105296	223	1	the	the	DET
esrj-105296	223	2	test	test	NOUN
esrj-105296	223	3	results	result	NOUN
esrj-105296	223	4	are	be	AUX
esrj-105296	223	5	presented	present	VERB
esrj-105296	223	6	in	in	ADP
esrj-105296	223	7	table	table	NOUN
esrj-105296	223	8	9	9	NUM
esrj-105296	223	9	.	.	PUNCT
esrj-105296	224	1	note	note	VERB
esrj-105296	224	2	that	that	SCONJ
esrj-105296	224	3	the	the	DET
esrj-105296	224	4	χ2	χ2	PROPN
esrj-105296	224	5	table	table	NOUN
esrj-105296	224	6	value	value	NOUN
esrj-105296	224	7	is	be	AUX
esrj-105296	224	8	3.841	3.841	NUM
esrj-105296	224	9	at	at	ADP
esrj-105296	224	10	95	95	NUM
esrj-105296	224	11	%	%	NOUN
esrj-105296	224	12	level	level	NOUN
esrj-105296	224	13	of	of	ADP
esrj-105296	224	14	confidence	confidence	NOUN
esrj-105296	224	15	.	.	PUNCT
esrj-105296	225	1	upon	upon	SCONJ
esrj-105296	225	2	analysis	analysis	NOUN
esrj-105296	225	3	of	of	ADP
esrj-105296	225	4	the	the	DET
esrj-105296	225	5	results	result	NOUN
esrj-105296	225	6	,	,	PUNCT
esrj-105296	225	7	it	it	PRON
esrj-105296	225	8	is	be	AUX
esrj-105296	225	9	evident	evident	ADJ
esrj-105296	225	10	that	that	SCONJ
esrj-105296	225	11	there	there	PRON
esrj-105296	225	12	are	be	VERB
esrj-105296	225	13	statistically	statistically	ADV
esrj-105296	225	14	significant	significant	ADJ
esrj-105296	225	15	variances	variance	NOUN
esrj-105296	225	16	among	among	ADP
esrj-105296	225	17	all	all	DET
esrj-105296	225	18	classification	classification	NOUN
esrj-105296	225	19	accuracies	accuracy	NOUN
esrj-105296	225	20	,	,	PUNCT
esrj-105296	225	21	except	except	SCONJ
esrj-105296	225	22	for	for	ADP
esrj-105296	225	23	the	the	DET
esrj-105296	225	24	comparison	comparison	NOUN
esrj-105296	225	25	between	between	ADP
esrj-105296	225	26	hybridsn	hybridsn	PROPN
esrj-105296	225	27	cnn	cnn	PROPN
esrj-105296	225	28	and	and	CCONJ
esrj-105296	225	29	modified	modify	VERB
esrj-105296	225	30	hybridsn	hybridsn	PROPN
esrj-105296	225	31	cnn	cnn	PROPN
esrj-105296	225	32	on	on	ADP
esrj-105296	225	33	the	the	DET
esrj-105296	225	34	loukia	loukia	NOUN
esrj-105296	225	35	dataset	dataset	VERB
esrj-105296	225	36	from	from	ADP
esrj-105296	225	37	hyrank	hyrank	NOUN
esrj-105296	225	38	.	.	PUNCT
esrj-105296	226	1	these	these	DET
esrj-105296	226	2	findings	finding	NOUN
esrj-105296	226	3	suggest	suggest	VERB
esrj-105296	226	4	that	that	SCONJ
esrj-105296	226	5	employing	employ	VERB
esrj-105296	226	6	the	the	DET
esrj-105296	226	7	cnn	cnn	PROPN
esrj-105296	226	8	algorithm	algorithm	NOUN
esrj-105296	226	9	for	for	ADP
esrj-105296	226	10	hsi	hsi	PROPN
esrj-105296	226	11	classification	classification	NOUN
esrj-105296	226	12	generally	generally	ADV
esrj-105296	226	13	yields	yield	VERB
esrj-105296	226	14	higher	high	ADJ
esrj-105296	226	15	accuracy	accuracy	NOUN
esrj-105296	226	16	.	.	PUNCT
esrj-105296	227	1	however	however	ADV
esrj-105296	227	2	,	,	PUNCT
esrj-105296	227	3	it	it	PRON
esrj-105296	227	4	’s	’	VERB
esrj-105296	227	5	noteworthy	noteworthy	ADJ
esrj-105296	227	6	that	that	SCONJ
esrj-105296	227	7	the	the	DET
esrj-105296	227	8	choice	choice	NOUN
esrj-105296	227	9	of	of	ADP
esrj-105296	227	10	activation	activation	NOUN
esrj-105296	227	11	function	function	NOUN
esrj-105296	227	12	and	and	CCONJ
esrj-105296	227	13	optimizer	optimizer	NOUN
esrj-105296	227	14	within	within	ADP
esrj-105296	227	15	the	the	DET
esrj-105296	227	16	cnn	cnn	PROPN
esrj-105296	227	17	algorithm	algorithm	NOUN
esrj-105296	227	18	significantly	significantly	ADV
esrj-105296	227	19	impacts	impact	VERB
esrj-105296	227	20	model	model	NOUN
esrj-105296	227	21	performance	performance	NOUN
esrj-105296	227	22	.	.	PUNCT
esrj-105296	228	1	table	table	NOUN
esrj-105296	228	2	9	9	NUM
esrj-105296	228	3	.	.	PUNCT
esrj-105296	229	1	mcnemar	mcnemar	PROPN
esrj-105296	229	2	’s	’s	PART
esrj-105296	229	3	test	test	NOUN
esrj-105296	229	4	results	result	NOUN
esrj-105296	229	5	for	for	ADP
esrj-105296	229	6	hyrank	hyrank	NOUN
esrj-105296	229	7	loukia	loukia	NOUN
esrj-105296	229	8	,	,	PUNCT
esrj-105296	229	9	houston	houston	PROPN
esrj-105296	229	10	2013	2013	NUM
esrj-105296	229	11	,	,	PUNCT
esrj-105296	229	12	and	and	CCONJ
esrj-105296	229	13	salinas	salinas	PROPN
esrj-105296	229	14	scene	scene	NOUN
esrj-105296	229	15	datasets	dataset	NOUN
esrj-105296	229	16	.	.	PUNCT
esrj-105296	230	1	model	model	NOUN
esrj-105296	230	2	1	1	NUM
esrj-105296	230	3	model	model	NOUN
esrj-105296	230	4	2	2	NUM
esrj-105296	230	5	hyrank	hyrank	NOUN
esrj-105296	230	6	loukia	loukia	NOUN
esrj-105296	230	7	houston	houston	PROPN
esrj-105296	230	8	2013	2013	NUM
esrj-105296	230	9	salinas	salinas	PROPN
esrj-105296	230	10	scene	scene	NOUN
esrj-105296	230	11	χ2	χ2	PROPN
esrj-105296	230	12	si	si	PROPN
esrj-105296	230	13	gn	gn	PROPN
esrj-105296	230	14	ifi	ifi	PROPN
esrj-105296	230	15	ca	can	AUX
esrj-105296	230	16	nt	not	PART
esrj-105296	230	17	?	?	PUNCT
esrj-105296	231	1	χ2	χ2	PROPN
esrj-105296	231	2	si	si	PROPN
esrj-105296	231	3	gn	gn	PROPN
esrj-105296	231	4	ifi	ifi	PROPN
esrj-105296	231	5	ca	can	AUX
esrj-105296	231	6	nt	not	PART
esrj-105296	231	7	?	?	PUNCT
esrj-105296	232	1	χ2	χ2	PROPN
esrj-105296	232	2	si	si	PROPN
esrj-105296	232	3	gn	gn	PROPN
esrj-105296	232	4	ifi	ifi	PROPN
esrj-105296	232	5	ca	can	AUX
esrj-105296	232	6	nt	not	PART
esrj-105296	232	7	?	?	PUNCT
esrj-105296	233	1	svm	svm	ADJ
esrj-105296	233	2	–	–	PUNCT
esrj-105296	233	3	rf	rf	NOUN
esrj-105296	233	4	7.604	7.604	NUM
esrj-105296	234	1	yes	yes	NOUN
esrj-105296	234	2	94.494	94.494	NUM
esrj-105296	235	1	yes	yes	NOUN
esrj-105296	235	2	81.999	81.999	NUM
esrj-105296	235	3	yes	yes	INTJ
esrj-105296	235	4	svm	svm	ADJ
esrj-105296	235	5	–	–	PUNCT
esrj-105296	235	6	hybridsn	hybridsn	PROPN
esrj-105296	235	7	cnn	cnn	PROPN
esrj-105296	235	8	406.961	406.961	NUM
esrj-105296	236	1	yes	yes	INTJ
esrj-105296	236	2	284.944	284.944	NUM
esrj-105296	237	1	yes	yes	INTJ
esrj-105296	238	1	2463.601	2463.601	NUM
esrj-105296	238	2	yes	yes	INTJ
esrj-105296	238	3	svm	svm	ADJ
esrj-105296	238	4	–	–	PUNCT
esrj-105296	238	5	modified	modify	VERB
esrj-105296	238	6	hybridsn	hybridsn	NOUN
esrj-105296	238	7	cnn	cnn	PROPN
esrj-105296	238	8	442.350	442.350	NUM
esrj-105296	239	1	yes	yes	INTJ
esrj-105296	239	2	410.614	410.614	NUM
esrj-105296	240	1	yes	yes	INTJ
esrj-105296	241	1	2815.944	2815.944	NUM
esrj-105296	242	1	yes	yes	INTJ
esrj-105296	242	2	rf	rf	NUM
esrj-105296	242	3	–	–	PUNCT
esrj-105296	242	4	hybridsn	hybridsn	PROPN
esrj-105296	242	5	cnn	cnn	PROPN
esrj-105296	242	6	478.365	478.365	NUM
esrj-105296	243	1	yes	yes	INTJ
esrj-105296	244	1	573.005	573.005	NUM
esrj-105296	245	1	yes	yes	INTJ
esrj-105296	246	1	2902.725	2902.725	NUM
esrj-105296	247	1	yes	yes	INTJ
esrj-105296	247	2	rf	rf	NUM
esrj-105296	247	3	–	–	PUNCT
esrj-105296	247	4	modified	modify	VERB
esrj-105296	247	5	hybridsn	hybridsn	NOUN
esrj-105296	247	6	cnn	cnn	PROPN
esrj-105296	247	7	513.757	513.757	NUM
esrj-105296	247	8	yes	yes	NOUN
esrj-105296	247	9	694.404	694.404	NUM
esrj-105296	248	1	yes	yes	INTJ
esrj-105296	249	1	3212.820	3212.820	ADV
esrj-105296	249	2	yes	yes	INTJ
esrj-105296	249	3	hybridsn	hybridsn	PROPN
esrj-105296	249	4	cnn	cnn	PROPN
esrj-105296	249	5	–	–	PUNCT
esrj-105296	249	6	modified	modify	VERB
esrj-105296	249	7	hybridsn	hybridsn	NOUN
esrj-105296	249	8	cnn	cnn	PROPN
esrj-105296	249	9	2.413	2.413	NUM
esrj-105296	249	10	no	no	PROPN
esrj-105296	249	11	16.420	16.420	NUM
esrj-105296	250	1	yes	yes	INTJ
esrj-105296	251	1	98.462	98.462	NUM
esrj-105296	252	1	yes	yes	INTJ
esrj-105296	252	2	7	7	NUM
esrj-105296	252	3	.	.	X
esrj-105296	252	4	discussion	discussion	NOUN
esrj-105296	252	5	this	this	DET
esrj-105296	252	6	study	study	NOUN
esrj-105296	252	7	compares	compare	VERB
esrj-105296	252	8	the	the	DET
esrj-105296	252	9	performance	performance	NOUN
esrj-105296	252	10	of	of	ADP
esrj-105296	252	11	four	four	NUM
esrj-105296	252	12	supervised	supervised	ADJ
esrj-105296	252	13	ml	ml	NOUN
esrj-105296	252	14	classification	classification	NOUN
esrj-105296	252	15	algorithms	algorithm	NOUN
esrj-105296	252	16	for	for	ADP
esrj-105296	252	17	hsi	hsi	PROPN
esrj-105296	252	18	classification	classification	NOUN
esrj-105296	252	19	.	.	PUNCT
esrj-105296	253	1	three	three	NUM
esrj-105296	253	2	benchmark	benchmark	ADJ
esrj-105296	253	3	datasets	dataset	NOUN
esrj-105296	253	4	,	,	PUNCT
esrj-105296	253	5	namely	namely	ADV
esrj-105296	253	6	hyrank	hyrank	ADJ
esrj-105296	253	7	loukia	loukia	NOUN
esrj-105296	253	8	,	,	PUNCT
esrj-105296	253	9	houston	houston	PROPN
esrj-105296	253	10	2013	2013	NUM
esrj-105296	253	11	,	,	PUNCT
esrj-105296	253	12	and	and	CCONJ
esrj-105296	253	13	salinas	salinas	PROPN
esrj-105296	253	14	scene	scene	NOUN
esrj-105296	253	15	,	,	PUNCT
esrj-105296	253	16	were	be	AUX
esrj-105296	253	17	utilized	utilize	VERB
esrj-105296	253	18	,	,	PUNCT
esrj-105296	253	19	each	each	PRON
esrj-105296	253	20	possessing	possess	VERB
esrj-105296	253	21	varying	vary	VERB
esrj-105296	253	22	levels	level	NOUN
esrj-105296	253	23	of	of	ADP
esrj-105296	253	24	spectral	spectral	ADJ
esrj-105296	253	25	and	and	CCONJ
esrj-105296	253	26	spatial	spatial	ADJ
esrj-105296	253	27	resolution	resolution	NOUN
esrj-105296	253	28	.	.	PUNCT
esrj-105296	254	1	notably	notably	ADV
esrj-105296	254	2	,	,	PUNCT
esrj-105296	254	3	hyrank	hyrank	NOUN
esrj-105296	254	4	loukia	loukia	NOUN
esrj-105296	254	5	and	and	CCONJ
esrj-105296	254	6	houston	houston	PROPN
esrj-105296	254	7	2013	2013	NUM
esrj-105296	254	8	datasets	dataset	NOUN
esrj-105296	254	9	are	be	AUX
esrj-105296	254	10	infrequently	infrequently	ADV
esrj-105296	254	11	utilized	utilize	VERB
esrj-105296	254	12	benchmarks	benchmark	NOUN
esrj-105296	254	13	in	in	ADP
esrj-105296	254	14	the	the	DET
esrj-105296	254	15	literature	literature	NOUN
esrj-105296	254	16	.	.	PUNCT
esrj-105296	255	1	in	in	ADP
esrj-105296	255	2	the	the	DET
esrj-105296	255	3	data	data	NOUN
esrj-105296	255	4	preparation	preparation	NOUN
esrj-105296	255	5	stage	stage	NOUN
esrj-105296	255	6	,	,	PUNCT
esrj-105296	255	7	pca	pca	PROPN
esrj-105296	255	8	was	be	AUX
esrj-105296	255	9	employed	employ	VERB
esrj-105296	255	10	to	to	PART
esrj-105296	255	11	reduce	reduce	VERB
esrj-105296	255	12	the	the	DET
esrj-105296	255	13	spectral	spectral	ADJ
esrj-105296	255	14	dimensionality	dimensionality	NOUN
esrj-105296	255	15	of	of	ADP
esrj-105296	255	16	the	the	DET
esrj-105296	255	17	hsi	hsi	PROPN
esrj-105296	255	18	datasets	dataset	NOUN
esrj-105296	255	19	,	,	PUNCT
esrj-105296	255	20	retaining	retain	VERB
esrj-105296	255	21	the	the	DET
esrj-105296	255	22	first	first	ADJ
esrj-105296	255	23	15	15	NUM
esrj-105296	255	24	principal	principal	ADJ
esrj-105296	255	25	components	component	NOUN
esrj-105296	255	26	for	for	ADP
esrj-105296	255	27	each	each	DET
esrj-105296	255	28	dataset	dataset	NOUN
esrj-105296	255	29	.	.	PUNCT
esrj-105296	256	1	the	the	DET
esrj-105296	256	2	classification	classification	NOUN
esrj-105296	256	3	algorithms	algorithm	NOUN
esrj-105296	256	4	were	be	AUX
esrj-105296	256	5	evaluated	evaluate	VERB
esrj-105296	256	6	using	use	VERB
esrj-105296	256	7	common	common	ADJ
esrj-105296	256	8	performance	performance	NOUN
esrj-105296	256	9	evaluation	evaluation	NOUN
esrj-105296	256	10	metrics	metric	NOUN
esrj-105296	256	11	.	.	PUNCT
esrj-105296	257	1	upon	upon	SCONJ
esrj-105296	257	2	reviewing	review	VERB
esrj-105296	257	3	the	the	DET
esrj-105296	257	4	classification	classification	NOUN
esrj-105296	257	5	results	result	NOUN
esrj-105296	257	6	of	of	ADP
esrj-105296	257	7	the	the	DET
esrj-105296	257	8	datasets	dataset	NOUN
esrj-105296	257	9	,	,	PUNCT
esrj-105296	257	10	it	it	PRON
esrj-105296	257	11	is	be	AUX
esrj-105296	257	12	evident	evident	ADJ
esrj-105296	257	13	that	that	SCONJ
esrj-105296	257	14	the	the	DET
esrj-105296	257	15	classification	classification	NOUN
esrj-105296	257	16	performance	performance	NOUN
esrj-105296	257	17	for	for	ADP
esrj-105296	257	18	the	the	DET
esrj-105296	257	19	hyrank	hyrank	PROPN
esrj-105296	257	20	loukia	loukia	NOUN
esrj-105296	257	21	dataset	dataset	NOUN
esrj-105296	257	22	was	be	AUX
esrj-105296	257	23	comparatively	comparatively	ADV
esrj-105296	257	24	lower	low	ADJ
esrj-105296	257	25	than	than	ADP
esrj-105296	257	26	that	that	PRON
esrj-105296	257	27	of	of	ADP
esrj-105296	257	28	the	the	DET
esrj-105296	257	29	other	other	ADJ
esrj-105296	257	30	datasets	dataset	NOUN
esrj-105296	257	31	.	.	PUNCT
esrj-105296	258	1	this	this	DET
esrj-105296	258	2	discrepancy	discrepancy	NOUN
esrj-105296	258	3	could	could	AUX
esrj-105296	258	4	potentially	potentially	ADV
esrj-105296	258	5	be	be	AUX
esrj-105296	258	6	attributed	attribute	VERB
esrj-105296	258	7	to	to	ADP
esrj-105296	258	8	factors	factor	NOUN
esrj-105296	258	9	such	such	ADJ
esrj-105296	258	10	as	as	ADP
esrj-105296	258	11	an	an	DET
esrj-105296	258	12	insufficient	insufficient	ADJ
esrj-105296	258	13	number	number	NOUN
esrj-105296	258	14	of	of	ADP
esrj-105296	258	15	collected	collect	VERB
esrj-105296	258	16	ground	ground	NOUN
esrj-105296	258	17	truth	truth	NOUN
esrj-105296	258	18	data	datum	NOUN
esrj-105296	258	19	and	and	CCONJ
esrj-105296	258	20	the	the	DET
esrj-105296	258	21	presence	presence	NOUN
esrj-105296	258	22	of	of	ADP
esrj-105296	258	23	mixed	mixed	ADJ
esrj-105296	258	24	pixels	pixel	NOUN
esrj-105296	258	25	,	,	PUNCT
esrj-105296	258	26	resulting	result	VERB
esrj-105296	258	27	in	in	ADP
esrj-105296	258	28	similar	similar	ADJ
esrj-105296	258	29	spectral	spectral	ADJ
esrj-105296	258	30	characteristics	characteristic	NOUN
esrj-105296	258	31	between	between	ADP
esrj-105296	258	32	classes	class	NOUN
esrj-105296	258	33	.	.	PUNCT
esrj-105296	259	1	additionally	additionally	ADV
esrj-105296	259	2	,	,	PUNCT
esrj-105296	259	3	the	the	DET
esrj-105296	259	4	lower	low	ADJ
esrj-105296	259	5	accuracy	accuracy	NOUN
esrj-105296	259	6	observed	observe	VERB
esrj-105296	259	7	in	in	ADP
esrj-105296	259	8	vegetation	vegetation	NOUN
esrj-105296	259	9	classes	class	NOUN
esrj-105296	259	10	may	may	AUX
esrj-105296	259	11	be	be	AUX
esrj-105296	259	12	attributed	attribute	VERB
esrj-105296	259	13	to	to	ADP
esrj-105296	259	14	the	the	DET
esrj-105296	259	15	low	low	ADJ
esrj-105296	259	16	density	density	NOUN
esrj-105296	259	17	of	of	ADP
esrj-105296	259	18	vegetation	vegetation	NOUN
esrj-105296	259	19	and	and	CCONJ
esrj-105296	259	20	the	the	DET
esrj-105296	259	21	relatively	relatively	ADV
esrj-105296	259	22	low	low	ADJ
esrj-105296	259	23	spatial	spatial	ADJ
esrj-105296	259	24	resolution	resolution	NOUN
esrj-105296	259	25	of	of	ADP
esrj-105296	259	26	the	the	DET
esrj-105296	259	27	hyperion	hyperion	NOUN
esrj-105296	259	28	sensor	sensor	NOUN
esrj-105296	259	29	.	.	PUNCT
esrj-105296	260	1	notably	notably	ADV
esrj-105296	260	2	,	,	PUNCT
esrj-105296	260	3	the	the	DET
esrj-105296	260	4	uas	uas	PROPN
esrj-105296	260	5	for	for	ADP
esrj-105296	260	6	these	these	DET
esrj-105296	260	7	classes	class	NOUN
esrj-105296	260	8	were	be	AUX
esrj-105296	260	9	notably	notably	ADV
esrj-105296	260	10	low	low	ADJ
esrj-105296	260	11	in	in	ADP
esrj-105296	260	12	the	the	DET
esrj-105296	260	13	classification	classification	NOUN
esrj-105296	260	14	results	result	NOUN
esrj-105296	260	15	obtained	obtain	VERB
esrj-105296	260	16	using	use	VERB
esrj-105296	260	17	svm	svm	PROPN
esrj-105296	260	18	and	and	CCONJ
esrj-105296	260	19	rf	rf	ADJ
esrj-105296	260	20	algorithms	algorithm	NOUN
esrj-105296	260	21	.	.	PUNCT
esrj-105296	261	1	similarly	similarly	ADV
esrj-105296	261	2	,	,	PUNCT
esrj-105296	261	3	christovam	christovam	PROPN
esrj-105296	261	4	et	et	PROPN
esrj-105296	261	5	al	al	PROPN
esrj-105296	261	6	.	.	PROPN
esrj-105296	261	7	(	(	PUNCT
esrj-105296	261	8	2019	2019	NUM
esrj-105296	261	9	)	)	PUNCT
esrj-105296	261	10	and	and	CCONJ
esrj-105296	261	11	si	si	INTJ
esrj-105296	261	12	et	et	PROPN
esrj-105296	261	13	al	al	PROPN
esrj-105296	261	14	.	.	PROPN
esrj-105296	262	1	(	(	PUNCT
esrj-105296	262	2	2021	2021	NUM
esrj-105296	262	3	)	)	PUNCT
esrj-105296	262	4	reported	report	VERB
esrj-105296	262	5	low	low	ADJ
esrj-105296	262	6	accuracy	accuracy	NOUN
esrj-105296	262	7	values	value	NOUN
esrj-105296	262	8	were	be	AUX
esrj-105296	262	9	calculated	calculate	VERB
esrj-105296	262	10	for	for	ADP
esrj-105296	262	11	the	the	DET
esrj-105296	262	12	same	same	ADJ
esrj-105296	262	13	classes	class	NOUN
esrj-105296	262	14	in	in	ADP
esrj-105296	262	15	their	their	PRON
esrj-105296	262	16	studies	study	NOUN
esrj-105296	262	17	.	.	PUNCT
esrj-105296	263	1	in	in	ADP
esrj-105296	263	2	the	the	DET
esrj-105296	263	3	case	case	NOUN
esrj-105296	263	4	of	of	ADP
esrj-105296	263	5	the	the	DET
esrj-105296	263	6	houston	houston	PROPN
esrj-105296	263	7	2013	2013	NUM
esrj-105296	263	8	dataset	dataset	NOUN
esrj-105296	263	9	,	,	PUNCT
esrj-105296	263	10	a	a	DET
esrj-105296	263	11	significant	significant	ADJ
esrj-105296	263	12	number	number	NOUN
esrj-105296	263	13	of	of	ADP
esrj-105296	263	14	pixels	pixel	NOUN
esrj-105296	263	15	located	locate	VERB
esrj-105296	263	16	within	within	ADP
esrj-105296	263	17	cloud	cloud	NOUN
esrj-105296	263	18	shadows	shadow	NOUN
esrj-105296	263	19	were	be	AUX
esrj-105296	263	20	misclassified	misclassifie	VERB
esrj-105296	263	21	.	.	PUNCT
esrj-105296	264	1	some	some	DET
esrj-105296	264	2	studies	study	NOUN
esrj-105296	264	3	utilizing	utilize	VERB
esrj-105296	264	4	this	this	DET
esrj-105296	264	5	dataset	dataset	NOUN
esrj-105296	264	6	as	as	ADP
esrj-105296	264	7	a	a	DET
esrj-105296	264	8	benchmark	benchmark	NOUN
esrj-105296	264	9	also	also	ADV
esrj-105296	264	10	observed	observe	VERB
esrj-105296	264	11	misclassifications	misclassification	NOUN
esrj-105296	264	12	in	in	ADP
esrj-105296	264	13	shadow	shadow	NOUN
esrj-105296	264	14	areas	area	NOUN
esrj-105296	264	15	,	,	PUNCT
esrj-105296	264	16	while	while	SCONJ
esrj-105296	264	17	in	in	ADP
esrj-105296	264	18	a	a	DET
esrj-105296	264	19	few	few	ADJ
esrj-105296	264	20	cases	case	NOUN
esrj-105296	264	21	,	,	PUNCT
esrj-105296	264	22	the	the	DET
esrj-105296	264	23	shadowy	shadowy	ADJ
esrj-105296	264	24	areas	area	NOUN
esrj-105296	264	25	were	be	AUX
esrj-105296	264	26	removed	remove	VERB
esrj-105296	264	27	.	.	PUNCT
esrj-105296	265	1	it	it	PRON
esrj-105296	265	2	is	be	AUX
esrj-105296	265	3	crucial	crucial	ADJ
esrj-105296	265	4	to	to	PART
esrj-105296	265	5	retain	retain	VERB
esrj-105296	265	6	these	these	DET
esrj-105296	265	7	effects	effect	NOUN
esrj-105296	265	8	to	to	PART
esrj-105296	265	9	impartially	impartially	ADV
esrj-105296	265	10	test	test	VERB
esrj-105296	265	11	classification	classification	NOUN
esrj-105296	265	12	algorithms	algorithm	NOUN
esrj-105296	265	13	and	and	CCONJ
esrj-105296	265	14	understand	understand	VERB
esrj-105296	265	15	their	their	PRON
esrj-105296	265	16	performance	performance	NOUN
esrj-105296	265	17	underpotential	underpotential	ADJ
esrj-105296	265	18	real	real	ADJ
esrj-105296	265	19	-	-	PUNCT
esrj-105296	265	20	life	life	NOUN
esrj-105296	265	21	scenarios	scenario	NOUN
esrj-105296	265	22	.	.	PUNCT
esrj-105296	266	1	visual	visual	ADJ
esrj-105296	266	2	interpretation	interpretation	NOUN
esrj-105296	266	3	of	of	ADP
esrj-105296	266	4	the	the	DET
esrj-105296	266	5	classification	classification	NOUN
esrj-105296	266	6	maps	map	NOUN
esrj-105296	266	7	generated	generate	VERB
esrj-105296	266	8	for	for	ADP
esrj-105296	266	9	the	the	DET
esrj-105296	266	10	salinas	salinas	PROPN
esrj-105296	266	11	scene	scene	PROPN
esrj-105296	266	12	dataset	dataset	NOUN
esrj-105296	266	13	revealed	reveal	VERB
esrj-105296	266	14	numerous	numerous	ADJ
esrj-105296	266	15	misclassified	misclassified	ADJ
esrj-105296	266	16	pixels	pixel	NOUN
esrj-105296	266	17	,	,	PUNCT
esrj-105296	266	18	despite	despite	SCONJ
esrj-105296	266	19	high	high	ADJ
esrj-105296	266	20	accuracy	accuracy	NOUN
esrj-105296	266	21	values	value	NOUN
esrj-105296	266	22	being	be	AUX
esrj-105296	266	23	obtained	obtain	VERB
esrj-105296	266	24	.	.	PUNCT
esrj-105296	267	1	given	give	VERB
esrj-105296	267	2	that	that	SCONJ
esrj-105296	267	3	the	the	DET
esrj-105296	267	4	classes	class	NOUN
esrj-105296	267	5	predominantly	predominantly	ADV
esrj-105296	267	6	represent	represent	VERB
esrj-105296	267	7	vegetation	vegetation	NOUN
esrj-105296	267	8	species	specie	NOUN
esrj-105296	267	9	in	in	ADP
esrj-105296	267	10	the	the	DET
esrj-105296	267	11	image	image	NOUN
esrj-105296	267	12	,	,	PUNCT
esrj-105296	267	13	the	the	DET
esrj-105296	267	14	low	low	ADJ
esrj-105296	267	15	accuracy	accuracy	NOUN
esrj-105296	267	16	may	may	AUX
esrj-105296	267	17	stem	stem	VERB
esrj-105296	267	18	from	from	ADP
esrj-105296	267	19	similar	similar	ADJ
esrj-105296	267	20	spectral	spectral	ADJ
esrj-105296	267	21	characteristics	characteristic	NOUN
esrj-105296	267	22	and	and	CCONJ
esrj-105296	267	23	low	low	ADJ
esrj-105296	267	24	vegetation	vegetation	NOUN
esrj-105296	267	25	density	density	NOUN
esrj-105296	267	26	.	.	PUNCT
esrj-105296	268	1	therefore	therefore	ADV
esrj-105296	268	2	,	,	PUNCT
esrj-105296	268	3	assessing	assess	VERB
esrj-105296	268	4	classification	classification	NOUN
esrj-105296	268	5	performance	performance	NOUN
esrj-105296	268	6	should	should	AUX
esrj-105296	268	7	involve	involve	VERB
esrj-105296	268	8	not	not	PART
esrj-105296	268	9	only	only	ADV
esrj-105296	268	10	quantitative	quantitative	ADJ
esrj-105296	268	11	metrics	metric	NOUN
esrj-105296	268	12	but	but	CCONJ
esrj-105296	268	13	also	also	ADV
esrj-105296	268	14	visual	visual	ADJ
esrj-105296	268	15	interpretation	interpretation	NOUN
esrj-105296	268	16	of	of	ADP
esrj-105296	268	17	the	the	DET
esrj-105296	268	18	results	result	NOUN
esrj-105296	268	19	.	.	PUNCT
esrj-105296	269	1	upon	upon	SCONJ
esrj-105296	269	2	reviewing	review	VERB
esrj-105296	269	3	the	the	DET
esrj-105296	269	4	classification	classification	NOUN
esrj-105296	269	5	accuracies	accuracy	NOUN
esrj-105296	269	6	across	across	ADP
esrj-105296	269	7	datasets	dataset	NOUN
esrj-105296	269	8	,	,	PUNCT
esrj-105296	269	9	it	it	PRON
esrj-105296	269	10	was	be	AUX
esrj-105296	269	11	clear	clear	ADJ
esrj-105296	269	12	that	that	SCONJ
esrj-105296	269	13	both	both	DET
esrj-105296	269	14	cnn	cnn	PROPN
esrj-105296	269	15	algorithms	algorithm	NOUN
esrj-105296	269	16	consistently	consistently	ADV
esrj-105296	269	17	outperformed	outperform	VERB
esrj-105296	269	18	svm	svm	PROPN
esrj-105296	269	19	and	and	CCONJ
esrj-105296	269	20	rf	rf	NOUN
esrj-105296	269	21	.	.	PROPN
esrj-105296	269	22	notably	notably	ADV
esrj-105296	269	23	,	,	PUNCT
esrj-105296	269	24	while	while	SCONJ
esrj-105296	269	25	svm	svm	ADJ
esrj-105296	269	26	and	and	CCONJ
esrj-105296	269	27	rf	rf	NOUN
esrj-105296	269	28	classification	classification	NOUN
esrj-105296	269	29	maps	map	NOUN
esrj-105296	269	30	exhibited	exhibit	VERB
esrj-105296	269	31	a	a	DET
esrj-105296	269	32	salt	salt	NOUN
esrj-105296	269	33	-	-	PUNCT
esrj-105296	269	34	and	and	CCONJ
esrj-105296	269	35	-	-	PUNCT
esrj-105296	269	36	pepper	pepper	NOUN
esrj-105296	269	37	effect	effect	NOUN
esrj-105296	269	38	,	,	PUNCT
esrj-105296	269	39	cnn	cnn	PROPN
esrj-105296	269	40	tended	tend	VERB
esrj-105296	269	41	to	to	PART
esrj-105296	269	42	eliminate	eliminate	VERB
esrj-105296	269	43	details	detail	NOUN
esrj-105296	269	44	such	such	ADJ
esrj-105296	269	45	as	as	ADP
esrj-105296	269	46	roads	road	NOUN
esrj-105296	269	47	and	and	CCONJ
esrj-105296	269	48	small	small	ADJ
esrj-105296	269	49	objects	object	NOUN
esrj-105296	269	50	visible	visible	ADJ
esrj-105296	269	51	on	on	ADP
esrj-105296	269	52	the	the	DET
esrj-105296	269	53	true	true	ADJ
esrj-105296	269	54	color	color	NOUN
esrj-105296	269	55	images	image	NOUN
esrj-105296	269	56	of	of	ADP
esrj-105296	269	57	the	the	DET
esrj-105296	269	58	hsis	hsis	NOUN
esrj-105296	269	59	.	.	PUNCT
esrj-105296	270	1	statistical	statistical	ADJ
esrj-105296	270	2	analysis	analysis	NOUN
esrj-105296	270	3	using	use	VERB
esrj-105296	270	4	mcnemar	mcnemar	PROPN
esrj-105296	270	5	’s	’s	PART
esrj-105296	270	6	test	test	NOUN
esrj-105296	270	7	revealed	reveal	VERB
esrj-105296	270	8	significant	significant	ADJ
esrj-105296	270	9	differences	difference	NOUN
esrj-105296	270	10	in	in	ADP
esrj-105296	270	11	pairwise	pairwise	NOUN
esrj-105296	270	12	comparisons	comparison	NOUN
esrj-105296	270	13	of	of	ADP
esrj-105296	270	14	classification	classification	NOUN
esrj-105296	270	15	maps	map	NOUN
esrj-105296	270	16	generated	generate	VERB
esrj-105296	270	17	by	by	ADP
esrj-105296	270	18	the	the	DET
esrj-105296	270	19	algorithms	algorithm	NOUN
esrj-105296	270	20	.	.	PUNCT
esrj-105296	271	1	moreover	moreover	ADV
esrj-105296	271	2	,	,	PUNCT
esrj-105296	271	3	upon	upon	SCONJ
esrj-105296	271	4	scrutinizing	scrutinize	VERB
esrj-105296	271	5	the	the	DET
esrj-105296	271	6	processing	processing	NOUN
esrj-105296	271	7	times	time	NOUN
esrj-105296	271	8	of	of	ADP
esrj-105296	271	9	the	the	DET
esrj-105296	271	10	algorithms	algorithm	NOUN
esrj-105296	271	11	,	,	PUNCT
esrj-105296	271	12	it	it	PRON
esrj-105296	271	13	becomes	become	VERB
esrj-105296	271	14	challenging	challenge	VERB
esrj-105296	271	15	to	to	PART
esrj-105296	271	16	definitively	definitively	ADV
esrj-105296	271	17	determine	determine	VERB
esrj-105296	271	18	the	the	DET
esrj-105296	271	19	superiority	superiority	NOUN
esrj-105296	271	20	of	of	ADP
esrj-105296	271	21	one	one	NUM
esrj-105296	271	22	algorithm	algorithm	NOUN
esrj-105296	271	23	over	over	ADP
esrj-105296	271	24	another	another	PRON
esrj-105296	271	25	,	,	PUNCT
esrj-105296	271	26	as	as	SCONJ
esrj-105296	271	27	the	the	DET
esrj-105296	271	28	training	training	NOUN
esrj-105296	271	29	and	and	CCONJ
esrj-105296	271	30	classification	classification	NOUN
esrj-105296	271	31	times	time	NOUN
esrj-105296	271	32	vary	vary	VERB
esrj-105296	271	33	across	across	ADP
esrj-105296	271	34	different	different	ADJ
esrj-105296	271	35	datasets	dataset	NOUN
esrj-105296	271	36	.	.	PUNCT
esrj-105296	272	1	however	however	ADV
esrj-105296	272	2	,	,	PUNCT
esrj-105296	272	3	when	when	SCONJ
esrj-105296	272	4	focusing	focus	VERB
esrj-105296	272	5	solely	solely	ADV
esrj-105296	272	6	on	on	ADP
esrj-105296	272	7	classification	classification	NOUN
esrj-105296	272	8	success	success	NOUN
esrj-105296	272	9	,	,	PUNCT
esrj-105296	272	10	the	the	DET
esrj-105296	272	11	cnn	cnn	PROPN
esrj-105296	272	12	’s	’s	PART
esrj-105296	272	13	exceptional	exceptional	ADJ
esrj-105296	272	14	performance	performance	NOUN
esrj-105296	272	15	renders	render	VERB
esrj-105296	272	16	it	it	PRON
esrj-105296	272	17	an	an	DET
esrj-105296	272	18	appealing	appealing	ADJ
esrj-105296	272	19	option	option	NOUN
esrj-105296	272	20	for	for	ADP
esrj-105296	272	21	tasks	task	NOUN
esrj-105296	272	22	demanding	demand	VERB
esrj-105296	272	23	precise	precise	ADJ
esrj-105296	272	24	classification	classification	NOUN
esrj-105296	272	25	.	.	PUNCT
esrj-105296	273	1	the	the	DET
esrj-105296	273	2	classification	classification	NOUN
esrj-105296	273	3	outcomes	outcome	NOUN
esrj-105296	273	4	derived	derive	VERB
esrj-105296	273	5	from	from	ADP
esrj-105296	273	6	171investigation	171investigation	PROPN
esrj-105296	273	7	of	of	ADP
esrj-105296	273	8	the	the	DET
esrj-105296	273	9	performances	performance	NOUN
esrj-105296	273	10	of	of	ADP
esrj-105296	273	11	support	support	NOUN
esrj-105296	273	12	vector	vector	NOUN
esrj-105296	273	13	machine	machine	NOUN
esrj-105296	273	14	,	,	PUNCT
esrj-105296	273	15	random	random	ADJ
esrj-105296	273	16	forest	forest	NOUN
esrj-105296	273	17	,	,	PUNCT
esrj-105296	273	18	and	and	CCONJ
esrj-105296	273	19	3d-2d	3d-2d	NUM
esrj-105296	273	20	convolutional	convolutional	ADJ
esrj-105296	273	21	neural	neural	ADJ
esrj-105296	273	22	network	network	NOUN
esrj-105296	273	23	for	for	ADP
esrj-105296	273	24	hyperspectral	hyperspectral	ADJ
esrj-105296	273	25	image	image	NOUN
esrj-105296	273	26	classification	classification	NOUN
esrj-105296	273	27	the	the	DET
esrj-105296	273	28	modified	modify	VERB
esrj-105296	273	29	hybridsn	hybridsn	PROPN
esrj-105296	273	30	cnn	cnn	PROPN
esrj-105296	273	31	and	and	CCONJ
esrj-105296	273	32	hybridsn	hybridsn	PROPN
esrj-105296	273	33	cnn	cnn	PROPN
esrj-105296	273	34	algorithms	algorithm	NOUN
esrj-105296	273	35	underscored	underscore	VERB
esrj-105296	273	36	the	the	DET
esrj-105296	273	37	significance	significance	NOUN
esrj-105296	273	38	highlighted	highlight	VERB
esrj-105296	273	39	in	in	ADP
esrj-105296	273	40	literature	literature	NOUN
esrj-105296	273	41	regarding	regard	VERB
esrj-105296	273	42	the	the	DET
esrj-105296	273	43	selection	selection	NOUN
esrj-105296	273	44	of	of	ADP
esrj-105296	273	45	activation	activation	NOUN
esrj-105296	273	46	functions	function	NOUN
esrj-105296	273	47	and	and	CCONJ
esrj-105296	273	48	optimizers	optimizer	NOUN
esrj-105296	273	49	.	.	PUNCT
esrj-105296	274	1	8	8	X
esrj-105296	274	2	.	.	X
esrj-105296	274	3	conclusion	conclusion	NOUN
esrj-105296	274	4	this	this	DET
esrj-105296	274	5	study	study	NOUN
esrj-105296	274	6	examined	examine	VERB
esrj-105296	274	7	the	the	DET
esrj-105296	274	8	comparative	comparative	ADJ
esrj-105296	274	9	analysis	analysis	NOUN
esrj-105296	274	10	of	of	ADP
esrj-105296	274	11	four	four	NUM
esrj-105296	274	12	supervised	supervised	ADJ
esrj-105296	274	13	ml	ml	NOUN
esrj-105296	274	14	algorithms	algorithm	NOUN
esrj-105296	274	15	for	for	ADP
esrj-105296	274	16	hsi	hsi	PROPN
esrj-105296	274	17	classification	classification	NOUN
esrj-105296	274	18	utilizing	utilize	VERB
esrj-105296	274	19	three	three	NUM
esrj-105296	274	20	benchmark	benchmark	ADJ
esrj-105296	274	21	datasets	dataset	NOUN
esrj-105296	274	22	.	.	PUNCT
esrj-105296	275	1	the	the	DET
esrj-105296	275	2	investigation	investigation	NOUN
esrj-105296	275	3	provided	provide	VERB
esrj-105296	275	4	valuable	valuable	ADJ
esrj-105296	275	5	insights	insight	NOUN
esrj-105296	275	6	into	into	ADP
esrj-105296	275	7	the	the	DET
esrj-105296	275	8	strengths	strength	NOUN
esrj-105296	275	9	and	and	CCONJ
esrj-105296	275	10	limitations	limitation	NOUN
esrj-105296	275	11	of	of	ADP
esrj-105296	275	12	svm	svm	PROPN
esrj-105296	275	13	,	,	PUNCT
esrj-105296	275	14	rf	rf	ADJ
esrj-105296	275	15	,	,	PUNCT
esrj-105296	275	16	and	and	CCONJ
esrj-105296	275	17	cnn	cnn	PROPN
esrj-105296	275	18	in	in	ADP
esrj-105296	275	19	handling	handle	VERB
esrj-105296	275	20	varying	vary	VERB
esrj-105296	275	21	spectral	spectral	ADJ
esrj-105296	275	22	and	and	CCONJ
esrj-105296	275	23	spatial	spatial	ADJ
esrj-105296	275	24	characteristics	characteristic	NOUN
esrj-105296	275	25	of	of	ADP
esrj-105296	275	26	hsi	hsi	PROPN
esrj-105296	275	27	.	.	PUNCT
esrj-105296	276	1	it	it	PRON
esrj-105296	276	2	was	be	AUX
esrj-105296	276	3	observed	observe	VERB
esrj-105296	276	4	that	that	SCONJ
esrj-105296	276	5	the	the	DET
esrj-105296	276	6	performance	performance	NOUN
esrj-105296	276	7	of	of	ADP
esrj-105296	276	8	classification	classification	NOUN
esrj-105296	276	9	algorithms	algorithm	NOUN
esrj-105296	276	10	varied	vary	VERB
esrj-105296	276	11	across	across	ADP
esrj-105296	276	12	datasets	dataset	NOUN
esrj-105296	276	13	,	,	PUNCT
esrj-105296	276	14	with	with	ADP
esrj-105296	276	15	factors	factor	NOUN
esrj-105296	276	16	such	such	ADJ
esrj-105296	276	17	as	as	ADP
esrj-105296	276	18	insufficient	insufficient	ADJ
esrj-105296	276	19	ground	ground	NOUN
esrj-105296	276	20	truth	truth	NOUN
esrj-105296	276	21	data	datum	NOUN
esrj-105296	276	22	,	,	PUNCT
esrj-105296	276	23	mixed	mixed	ADJ
esrj-105296	276	24	pixels	pixel	NOUN
esrj-105296	276	25	,	,	PUNCT
esrj-105296	276	26	and	and	CCONJ
esrj-105296	276	27	spectral	spectral	ADJ
esrj-105296	276	28	similarity	similarity	NOUN
esrj-105296	276	29	between	between	ADP
esrj-105296	276	30	classes	class	NOUN
esrj-105296	276	31	affecting	affect	VERB
esrj-105296	276	32	classification	classification	NOUN
esrj-105296	276	33	performances	performance	NOUN
esrj-105296	276	34	.	.	PUNCT
esrj-105296	277	1	while	while	SCONJ
esrj-105296	277	2	svm	svm	PROPN
esrj-105296	277	3	and	and	CCONJ
esrj-105296	277	4	rf	rf	VERB
esrj-105296	277	5	performed	perform	VERB
esrj-105296	277	6	well	well	ADJ
esrj-105296	277	7	performance	performance	NOUN
esrj-105296	277	8	in	in	ADP
esrj-105296	277	9	certain	certain	ADJ
esrj-105296	277	10	cases	case	NOUN
esrj-105296	277	11	,	,	PUNCT
esrj-105296	277	12	particularly	particularly	ADV
esrj-105296	277	13	in	in	ADP
esrj-105296	277	14	datasets	dataset	NOUN
esrj-105296	277	15	with	with	ADP
esrj-105296	277	16	less	less	ADV
esrj-105296	277	17	complex	complex	ADJ
esrj-105296	277	18	spectral	spectral	ADJ
esrj-105296	277	19	characteristics	characteristic	NOUN
esrj-105296	277	20	,	,	PUNCT
esrj-105296	277	21	cnn	cnn	PROPN
esrj-105296	277	22	models	model	NOUN
esrj-105296	277	23	consistently	consistently	ADV
esrj-105296	277	24	outperformed	outperform	VERB
esrj-105296	277	25	them	they	PRON
esrj-105296	277	26	,	,	PUNCT
esrj-105296	277	27	especially	especially	ADV
esrj-105296	277	28	in	in	ADP
esrj-105296	277	29	datasets	dataset	NOUN
esrj-105296	277	30	with	with	ADP
esrj-105296	277	31	complex	complex	ADJ
esrj-105296	277	32	spectral	spectral	ADJ
esrj-105296	277	33	signatures	signature	NOUN
esrj-105296	277	34	and	and	CCONJ
esrj-105296	277	35	spatial	spatial	ADJ
esrj-105296	277	36	features	feature	NOUN
esrj-105296	277	37	.	.	PUNCT
esrj-105296	278	1	the	the	DET
esrj-105296	278	2	capacity	capacity	NOUN
esrj-105296	278	3	of	of	ADP
esrj-105296	278	4	cnn	cnn	PROPN
esrj-105296	278	5	to	to	PART
esrj-105296	278	6	extract	extract	VERB
esrj-105296	278	7	features	feature	NOUN
esrj-105296	278	8	and	and	CCONJ
esrj-105296	278	9	capture	capture	VERB
esrj-105296	278	10	complex	complex	ADJ
esrj-105296	278	11	spatial	spatial	ADJ
esrj-105296	278	12	patterns	pattern	NOUN
esrj-105296	278	13	made	make	VERB
esrj-105296	278	14	it	it	PRON
esrj-105296	278	15	a	a	DET
esrj-105296	278	16	compelling	compelling	ADJ
esrj-105296	278	17	choice	choice	NOUN
esrj-105296	278	18	for	for	ADP
esrj-105296	278	19	tasks	task	NOUN
esrj-105296	278	20	requiring	require	VERB
esrj-105296	278	21	high	high	ADJ
esrj-105296	278	22	precision	precision	NOUN
esrj-105296	278	23	classification	classification	NOUN
esrj-105296	278	24	.	.	PUNCT
esrj-105296	279	1	moreover	moreover	ADV
esrj-105296	279	2	,	,	PUNCT
esrj-105296	279	3	the	the	DET
esrj-105296	279	4	hybridsn	hybridsn	NOUN
esrj-105296	279	5	cnn	cnn	PROPN
esrj-105296	279	6	architecture	architecture	NOUN
esrj-105296	279	7	underwent	underwent	NOUN
esrj-105296	279	8	modifications	modification	NOUN
esrj-105296	279	9	with	with	ADP
esrj-105296	279	10	the	the	DET
esrj-105296	279	11	expectation	expectation	NOUN
esrj-105296	279	12	that	that	SCONJ
esrj-105296	279	13	it	it	PRON
esrj-105296	279	14	would	would	AUX
esrj-105296	279	15	influence	influence	VERB
esrj-105296	279	16	the	the	DET
esrj-105296	279	17	classification	classification	NOUN
esrj-105296	279	18	performance	performance	NOUN
esrj-105296	279	19	of	of	ADP
esrj-105296	279	20	activation	activation	NOUN
esrj-105296	279	21	functions	function	NOUN
esrj-105296	279	22	and	and	CCONJ
esrj-105296	279	23	optimizers	optimizer	NOUN
esrj-105296	279	24	in	in	ADP
esrj-105296	279	25	the	the	DET
esrj-105296	279	26	cnn	cnn	PROPN
esrj-105296	279	27	algorithm	algorithm	NOUN
esrj-105296	279	28	.	.	PUNCT
esrj-105296	280	1	in	in	ADP
esrj-105296	280	2	a	a	DET
esrj-105296	280	3	b	b	NOUN
esrj-105296	280	4	c	c	NOUN
esrj-105296	280	5	d	d	X
esrj-105296	280	6	e	e	X
esrj-105296	280	7	figure	figure	NOUN
esrj-105296	280	8	5	5	NUM
esrj-105296	280	9	.	.	PUNCT
esrj-105296	280	10	classification	classification	NOUN
esrj-105296	280	11	maps	map	NOUN
esrj-105296	280	12	for	for	ADP
esrj-105296	280	13	the	the	DET
esrj-105296	280	14	salinas	salinas	PROPN
esrj-105296	280	15	scene	scene	NOUN
esrj-105296	280	16	dataset	dataset	NOUN
esrj-105296	280	17	:	:	PUNCT
esrj-105296	280	18	(	(	PUNCT
esrj-105296	280	19	a	a	X
esrj-105296	280	20	)	)	PUNCT
esrj-105296	280	21	svm	svm	PROPN
esrj-105296	280	22	,	,	PUNCT
esrj-105296	280	23	(	(	PUNCT
esrj-105296	280	24	b	b	NOUN
esrj-105296	280	25	)	)	PUNCT
esrj-105296	280	26	rf	rf	NOUN
esrj-105296	280	27	,	,	PUNCT
esrj-105296	280	28	(	(	PUNCT
esrj-105296	280	29	c	c	X
esrj-105296	280	30	)	)	PUNCT
esrj-105296	280	31	hybridsn	hybridsn	PROPN
esrj-105296	280	32	cnn	cnn	PROPN
esrj-105296	280	33	,	,	PUNCT
esrj-105296	280	34	(	(	PUNCT
esrj-105296	280	35	d	d	X
esrj-105296	280	36	)	)	PUNCT
esrj-105296	280	37	modified	modify	VERB
esrj-105296	280	38	hybridsn	hybridsn	PROPN
esrj-105296	280	39	cnn	cnn	PROPN
esrj-105296	280	40	,	,	PUNCT
esrj-105296	280	41	and	and	CCONJ
esrj-105296	280	42	(	(	PUNCT
esrj-105296	280	43	e	e	NOUN
esrj-105296	280	44	)	)	PUNCT
esrj-105296	280	45	true	true	ADJ
esrj-105296	280	46	color	color	NOUN
esrj-105296	280	47	composition	composition	NOUN
esrj-105296	280	48	line	line	NOUN
esrj-105296	280	49	with	with	ADP
esrj-105296	280	50	literature	literature	NOUN
esrj-105296	280	51	recommendations	recommendation	NOUN
esrj-105296	280	52	,	,	PUNCT
esrj-105296	280	53	the	the	DET
esrj-105296	280	54	modified	modify	VERB
esrj-105296	280	55	hybridsn	hybridsn	PROPN
esrj-105296	280	56	cnn	cnn	PROPN
esrj-105296	280	57	algorithm	algorithm	PROPN
esrj-105296	280	58	,	,	PUNCT
esrj-105296	280	59	integrating	integrate	VERB
esrj-105296	280	60	the	the	DET
esrj-105296	280	61	adamax	adamax	NOUN
esrj-105296	280	62	optimizer	optimizer	NOUN
esrj-105296	280	63	and	and	CCONJ
esrj-105296	280	64	mish	mish	ADJ
esrj-105296	280	65	activation	activation	NOUN
esrj-105296	280	66	function	function	NOUN
esrj-105296	280	67	,	,	PUNCT
esrj-105296	280	68	exhibited	exhibit	VERB
esrj-105296	280	69	a	a	DET
esrj-105296	280	70	slight	slight	ADJ
esrj-105296	280	71	enhancement	enhancement	NOUN
esrj-105296	280	72	in	in	ADP
esrj-105296	280	73	classification	classification	NOUN
esrj-105296	280	74	performance	performance	NOUN
esrj-105296	280	75	.	.	PUNCT
esrj-105296	281	1	furthermore	furthermore	ADV
esrj-105296	281	2	,	,	PUNCT
esrj-105296	281	3	the	the	DET
esrj-105296	281	4	study	study	NOUN
esrj-105296	281	5	emphasized	emphasize	VERB
esrj-105296	281	6	the	the	DET
esrj-105296	281	7	significance	significance	NOUN
esrj-105296	281	8	of	of	ADP
esrj-105296	281	9	not	not	PART
esrj-105296	281	10	only	only	ADV
esrj-105296	281	11	relying	rely	VERB
esrj-105296	281	12	on	on	ADP
esrj-105296	281	13	quantitative	quantitative	ADJ
esrj-105296	281	14	metrics	metric	NOUN
esrj-105296	281	15	but	but	CCONJ
esrj-105296	281	16	also	also	ADV
esrj-105296	281	17	incorporating	incorporate	VERB
esrj-105296	281	18	visual	visual	ADJ
esrj-105296	281	19	interpretation	interpretation	NOUN
esrj-105296	281	20	of	of	ADP
esrj-105296	281	21	classification	classification	NOUN
esrj-105296	281	22	results	result	NOUN
esrj-105296	281	23	to	to	PART
esrj-105296	281	24	gain	gain	VERB
esrj-105296	281	25	a	a	DET
esrj-105296	281	26	comprehensive	comprehensive	ADJ
esrj-105296	281	27	understanding	understanding	NOUN
esrj-105296	281	28	of	of	ADP
esrj-105296	281	29	algorithm	algorithm	NOUN
esrj-105296	281	30	performance	performance	NOUN
esrj-105296	281	31	.	.	PUNCT
esrj-105296	282	1	overall	overall	ADV
esrj-105296	282	2	,	,	PUNCT
esrj-105296	282	3	the	the	DET
esrj-105296	282	4	findings	finding	NOUN
esrj-105296	282	5	contribute	contribute	VERB
esrj-105296	282	6	to	to	ADP
esrj-105296	282	7	advancing	advance	VERB
esrj-105296	282	8	the	the	DET
esrj-105296	282	9	field	field	NOUN
esrj-105296	282	10	of	of	ADP
esrj-105296	282	11	hsi	hsi	PROPN
esrj-105296	282	12	classification	classification	NOUN
esrj-105296	282	13	by	by	ADP
esrj-105296	282	14	providing	provide	VERB
esrj-105296	282	15	valuable	valuable	ADJ
esrj-105296	282	16	insights	insight	NOUN
esrj-105296	282	17	into	into	ADP
esrj-105296	282	18	the	the	DET
esrj-105296	282	19	comparative	comparative	ADJ
esrj-105296	282	20	performance	performance	NOUN
esrj-105296	282	21	of	of	ADP
esrj-105296	282	22	ml	ml	ADP
esrj-105296	282	23	algorithms	algorithm	NOUN
esrj-105296	282	24	across	across	ADP
esrj-105296	282	25	various	various	ADJ
esrj-105296	282	26	datasets	dataset	NOUN
esrj-105296	282	27	.	.	PUNCT
esrj-105296	283	1	these	these	DET
esrj-105296	283	2	insights	insight	NOUN
esrj-105296	283	3	can	can	AUX
esrj-105296	283	4	inform	inform	VERB
esrj-105296	283	5	the	the	DET
esrj-105296	283	6	selection	selection	NOUN
esrj-105296	283	7	of	of	ADP
esrj-105296	283	8	appropriate	appropriate	ADJ
esrj-105296	283	9	algorithms	algorithm	NOUN
esrj-105296	283	10	based	base	VERB
esrj-105296	283	11	on	on	ADP
esrj-105296	283	12	specific	specific	ADJ
esrj-105296	283	13	application	application	NOUN
esrj-105296	283	14	requirements	requirement	NOUN
esrj-105296	283	15	,	,	PUNCT
esrj-105296	283	16	ultimately	ultimately	ADV
esrj-105296	283	17	enhancing	enhance	VERB
esrj-105296	283	18	the	the	DET
esrj-105296	283	19	accuracy	accuracy	NOUN
esrj-105296	283	20	and	and	CCONJ
esrj-105296	283	21	efficiency	efficiency	NOUN
esrj-105296	283	22	of	of	ADP
esrj-105296	283	23	hsi	hsi	PROPN
esrj-105296	283	24	analysis	analysis	NOUN
esrj-105296	283	25	in	in	ADP
esrj-105296	283	26	various	various	ADJ
esrj-105296	283	27	domains	domain	NOUN
esrj-105296	283	28	.	.	PUNCT
esrj-105296	284	1	acknowledgments	acknowledgment	NOUN
esrj-105296	284	2	the	the	DET
esrj-105296	284	3	study	study	NOUN
esrj-105296	284	4	constitutes	constitute	VERB
esrj-105296	284	5	the	the	DET
esrj-105296	284	6	master	master	NOUN
esrj-105296	284	7	’s	’s	PART
esrj-105296	284	8	thesis	thesis	NOUN
esrj-105296	284	9	of	of	ADP
esrj-105296	284	10	the	the	DET
esrj-105296	284	11	first	first	ADJ
esrj-105296	284	12	author	author	NOUN
esrj-105296	284	13	.	.	PUNCT
esrj-105296	285	1	the	the	DET
esrj-105296	285	2	authors	author	NOUN
esrj-105296	285	3	thank	thank	VERB
esrj-105296	285	4	the	the	DET
esrj-105296	285	5	isprs	isprs	PROPN
esrj-105296	285	6	commission	commission	PROPN
esrj-105296	285	7	iii	iii	PROPN
esrj-105296	285	8	,	,	PUNCT
esrj-105296	285	9	wg	wg	VERB
esrj-105296	285	10	iii/4	iii/4	NOUN
esrj-105296	285	11	for	for	ADP
esrj-105296	285	12	providing	provide	VERB
esrj-105296	285	13	the	the	DET
esrj-105296	285	14	hyrank	hyrank	NOUN
esrj-105296	285	15	dataset	dataset	NOUN
esrj-105296	285	16	and	and	CCONJ
esrj-105296	285	17	to	to	ADP
esrj-105296	285	18	the	the	DET
esrj-105296	285	19	national	national	ADJ
esrj-105296	285	20	center	center	NOUN
esrj-105296	285	21	for	for	ADP
esrj-105296	285	22	airborne	airborne	ADJ
esrj-105296	285	23	laser	laser	NOUN
esrj-105296	285	24	mapping	mapping	NOUN
esrj-105296	285	25	,	,	PUNCT
esrj-105296	285	26	the	the	DET
esrj-105296	285	27	hyperspectral	hyperspectral	ADJ
esrj-105296	285	28	image	image	NOUN
esrj-105296	285	29	analysis	analysis	NOUN
esrj-105296	285	30	laboratory	laboratory	NOUN
esrj-105296	285	31	at	at	ADP
esrj-105296	285	32	the	the	DET
esrj-105296	285	33	university	university	PROPN
esrj-105296	285	34	of	of	ADP
esrj-105296	285	35	houston	houston	PROPN
esrj-105296	285	36	,	,	PUNCT
esrj-105296	285	37	and	and	CCONJ
esrj-105296	285	38	the	the	DET
esrj-105296	285	39	ieee	ieee	NOUN
esrj-105296	285	40	grss	grss	NOUN
esrj-105296	285	41	image	image	NOUN
esrj-105296	285	42	analysis	analysis	NOUN
esrj-105296	285	43	and	and	CCONJ
esrj-105296	285	44	data	datum	NOUN
esrj-105296	285	45	fusion	fusion	NOUN
esrj-105296	285	46	technical	technical	PROPN
esrj-105296	285	47	committee	committee	NOUN
esrj-105296	285	48	for	for	ADP
esrj-105296	285	49	providing	provide	VERB
esrj-105296	285	50	the	the	DET
esrj-105296	285	51	houston	houston	PROPN
esrj-105296	285	52	dataset	dataset	NOUN
esrj-105296	285	53	used	use	VERB
esrj-105296	285	54	in	in	ADP
esrj-105296	285	55	the	the	DET
esrj-105296	285	56	experiments	experiment	NOUN
esrj-105296	285	57	.	.	PUNCT
esrj-105296	286	1	172	172	NUM
esrj-105296	286	2	eren	eren	PROPN
esrj-105296	286	3	can	can	AUX
esrj-105296	286	4	seyrek	seyrek	VERB
esrj-105296	286	5	,	,	PUNCT
esrj-105296	286	6	murat	murat	PROPN
esrj-105296	286	7	uysal	uysal	PROPN
esrj-105296	286	8	funding	funding	NOUN
esrj-105296	286	9	this	this	DET
esrj-105296	286	10	study	study	NOUN
esrj-105296	286	11	was	be	AUX
esrj-105296	286	12	supported	support	VERB
esrj-105296	286	13	by	by	ADP
esrj-105296	286	14	afyon	afyon	NOUN
esrj-105296	286	15	kocatepe	kocatepe	PROPN
esrj-105296	286	16	university	university	PROPN
esrj-105296	286	17	scientific	scientific	ADJ
esrj-105296	286	18	research	research	NOUN
esrj-105296	286	19	projects	project	NOUN
esrj-105296	286	20	coordination	coordination	NOUN
esrj-105296	286	21	unit	unit	NOUN
esrj-105296	286	22	within	within	ADP
esrj-105296	286	23	the	the	DET
esrj-105296	286	24	20.fen.bi̇l.12	20.fen.bi̇l.12	NUM
esrj-105296	286	25	project	project	NOUN
esrj-105296	286	26	.	.	PUNCT
esrj-105296	287	1	references	reference	NOUN
esrj-105296	287	2	abadi	abadi	PROPN
esrj-105296	287	3	,	,	PUNCT
esrj-105296	287	4	m.	m.	NOUN
esrj-105296	287	5	,	,	PUNCT
esrj-105296	287	6	barham	barham	PROPN
esrj-105296	287	7	,	,	PUNCT
esrj-105296	287	8	p.	p.	PROPN
esrj-105296	287	9	,	,	PUNCT
esrj-105296	287	10	chen	chen	PROPN
esrj-105296	287	11	,	,	PUNCT
esrj-105296	287	12	j.	j.	PROPN
esrj-105296	287	13	,	,	PUNCT
esrj-105296	287	14	chen	chen	PROPN
esrj-105296	287	15	,	,	PUNCT
esrj-105296	287	16	z.	z.	PROPN
esrj-105296	287	17	,	,	PUNCT
esrj-105296	287	18	davis	davis	PROPN
esrj-105296	287	19	,	,	PUNCT
esrj-105296	287	20	a.	a.	NOUN
esrj-105296	287	21	,	,	PUNCT
esrj-105296	287	22	dean	dean	PROPN
esrj-105296	287	23	,	,	PUNCT
esrj-105296	287	24	j.	j.	PROPN
esrj-105296	287	25	,	,	PUNCT
esrj-105296	287	26	devin	devin	PROPN
esrj-105296	287	27	,	,	PUNCT
esrj-105296	287	28	m.	m.	NOUN
esrj-105296	287	29	,	,	PUNCT
esrj-105296	287	30	ghemawat	ghemawat	PROPN
esrj-105296	287	31	,	,	PUNCT
esrj-105296	287	32	s.	s.	PROPN
esrj-105296	287	33	,	,	PUNCT
esrj-105296	287	34	irving	irving	PROPN
esrj-105296	287	35	,	,	PUNCT
esrj-105296	287	36	g.	g.	PROPN
esrj-105296	287	37	,	,	PUNCT
esrj-105296	287	38	&	&	CCONJ
esrj-105296	287	39	isard	isard	ADJ
esrj-105296	287	40	,	,	PUNCT
esrj-105296	287	41	m.	m.	NOUN
esrj-105296	287	42	(	(	PUNCT
esrj-105296	287	43	2016	2016	NUM
esrj-105296	287	44	)	)	PUNCT
esrj-105296	287	45	.	.	PUNCT
esrj-105296	288	1	tensorflow	tensorflow	NOUN
esrj-105296	288	2	:	:	PUNCT
esrj-105296	288	3	a	a	DET
esrj-105296	288	4	system	system	NOUN
esrj-105296	288	5	for	for	ADP
esrj-105296	288	6	large	large	ADJ
esrj-105296	288	7	-	-	PUNCT
esrj-105296	288	8	scale	scale	NOUN
esrj-105296	288	9	machine	machine	NOUN
esrj-105296	288	10	learning	learning	NOUN
esrj-105296	288	11	.	.	PUNCT
esrj-105296	289	1	12th	12th	ADJ
esrj-105296	289	2	symposium	symposium	NOUN
esrj-105296	289	3	on	on	ADP
esrj-105296	289	4	operating	operating	NOUN
esrj-105296	289	5	systems	system	NOUN
esrj-105296	289	6	design	design	NOUN
esrj-105296	289	7	and	and	CCONJ
esrj-105296	289	8	implementation	implementation	NOUN
esrj-105296	289	9	.	.	PUNCT
esrj-105296	290	1	https://doi.org/10.48550/arxiv.1605.08695	https://doi.org/10.48550/arxiv.1605.08695	PROPN
esrj-105296	290	2	abdikan	abdikan	PROPN
esrj-105296	290	3	,	,	PUNCT
esrj-105296	290	4	s.	s.	PROPN
esrj-105296	290	5	,	,	PUNCT
esrj-105296	290	6	sekertekin	sekertekin	PROPN
esrj-105296	290	7	,	,	PUNCT
esrj-105296	290	8	a.	a.	NOUN
esrj-105296	290	9	,	,	PUNCT
esrj-105296	290	10	narin	narin	ADV
esrj-105296	290	11	,	,	PUNCT
esrj-105296	290	12	o.	o.	PROPN
esrj-105296	290	13	g.	g.	PROPN
esrj-105296	290	14	,	,	PUNCT
esrj-105296	290	15	delen	delen	PROPN
esrj-105296	290	16	,	,	PUNCT
esrj-105296	290	17	a.	a.	PROPN
esrj-105296	290	18	,	,	PUNCT
esrj-105296	290	19	&	&	CCONJ
esrj-105296	290	20	balik	balik	PROPN
esrj-105296	290	21	sanli	sanli	PROPN
esrj-105296	290	22	,	,	PUNCT
esrj-105296	290	23	f.	f.	PROPN
esrj-105296	290	24	(	(	PUNCT
esrj-105296	290	25	2023	2023	NUM
esrj-105296	290	26	)	)	PUNCT
esrj-105296	290	27	.	.	PUNCT
esrj-105296	291	1	a	a	DET
esrj-105296	291	2	comparative	comparative	ADJ
esrj-105296	291	3	analysis	analysis	NOUN
esrj-105296	291	4	of	of	ADP
esrj-105296	291	5	slr	slr	NOUN
esrj-105296	291	6	,	,	PUNCT
esrj-105296	291	7	mlr	mlr	PROPN
esrj-105296	291	8	,	,	PUNCT
esrj-105296	291	9	ann	ann	PROPN
esrj-105296	291	10	,	,	PUNCT
esrj-105296	291	11	xgboost	xgboost	PROPN
esrj-105296	291	12	and	and	CCONJ
esrj-105296	291	13	cnn	cnn	PROPN
esrj-105296	291	14	for	for	ADP
esrj-105296	291	15	crop	crop	NOUN
esrj-105296	291	16	height	height	NOUN
esrj-105296	291	17	estimation	estimation	NOUN
esrj-105296	291	18	of	of	ADP
esrj-105296	291	19	sunflower	sunflower	NOUN
esrj-105296	291	20	using	use	VERB
esrj-105296	291	21	sentinel-1	sentinel-1	NUM
esrj-105296	291	22	and	and	CCONJ
esrj-105296	291	23	sentinel-2	sentinel-2	NUM
esrj-105296	291	24	.	.	PUNCT
esrj-105296	292	1	advances	advance	NOUN
esrj-105296	292	2	in	in	ADP
esrj-105296	292	3	space	space	NOUN
esrj-105296	292	4	research	research	NOUN
esrj-105296	292	5	,	,	PUNCT
esrj-105296	292	6	71(7	71(7	NOUN
esrj-105296	292	7	)	)	PUNCT
esrj-105296	292	8	,	,	PUNCT
esrj-105296	292	9	3045	3045	NUM
esrj-105296	292	10	-	-	SYM
esrj-105296	292	11	3059	3059	NUM
esrj-105296	292	12	.	.	PUNCT
esrj-105296	293	1	https://doi.org/10.1016/j	https://doi.org/10.1016/j	NOUN
esrj-105296	293	2	.	.	PUNCT
esrj-105296	294	1	asr.2022.11.046	asr.2022.11.046	PROPN
esrj-105296	294	2	adão	adão	PROPN
esrj-105296	294	3	,	,	PUNCT
esrj-105296	294	4	t.	t.	PROPN
esrj-105296	294	5	,	,	PUNCT
esrj-105296	294	6	hruška	hruška	PROPN
esrj-105296	294	7	,	,	PUNCT
esrj-105296	294	8	j.	j.	PROPN
esrj-105296	294	9	,	,	PUNCT
esrj-105296	294	10	pádua	pádua	PROPN
esrj-105296	294	11	,	,	PUNCT
esrj-105296	294	12	l.	l.	PROPN
esrj-105296	294	13	,	,	PUNCT
esrj-105296	294	14	bessa	bessa	PROPN
esrj-105296	294	15	,	,	PUNCT
esrj-105296	294	16	j.	j.	PROPN
esrj-105296	294	17	,	,	PUNCT
esrj-105296	294	18	peres	peres	PROPN
esrj-105296	294	19	,	,	PUNCT
esrj-105296	294	20	e.	e.	PROPN
esrj-105296	294	21	,	,	PUNCT
esrj-105296	294	22	morais	morais	PROPN
esrj-105296	294	23	,	,	PUNCT
esrj-105296	294	24	r.	r.	PROPN
esrj-105296	294	25	,	,	PUNCT
esrj-105296	294	26	&	&	CCONJ
esrj-105296	294	27	sousa	sousa	PROPN
esrj-105296	294	28	,	,	PUNCT
esrj-105296	294	29	j.	j.	PROPN
esrj-105296	294	30	j.	j.	PROPN
esrj-105296	294	31	(	(	PUNCT
esrj-105296	294	32	2017	2017	NUM
esrj-105296	294	33	)	)	PUNCT
esrj-105296	294	34	.	.	PUNCT
esrj-105296	295	1	hyperspectral	hyperspectral	ADJ
esrj-105296	295	2	imaging	imaging	NOUN
esrj-105296	295	3	:	:	PUNCT
esrj-105296	295	4	a	a	DET
esrj-105296	295	5	review	review	NOUN
esrj-105296	295	6	on	on	ADP
esrj-105296	295	7	uav	uav	PROPN
esrj-105296	295	8	-	-	PUNCT
esrj-105296	295	9	based	base	VERB
esrj-105296	295	10	sensors	sensor	NOUN
esrj-105296	295	11	,	,	PUNCT
esrj-105296	295	12	data	datum	NOUN
esrj-105296	295	13	processing	processing	NOUN
esrj-105296	295	14	and	and	CCONJ
esrj-105296	295	15	applications	application	NOUN
esrj-105296	295	16	for	for	ADP
esrj-105296	295	17	agriculture	agriculture	NOUN
esrj-105296	295	18	and	and	CCONJ
esrj-105296	295	19	forestry	forestry	NOUN
esrj-105296	295	20	.	.	PUNCT
esrj-105296	296	1	remote	remote	ADJ
esrj-105296	296	2	sensing	sensing	NOUN
esrj-105296	296	3	,	,	PUNCT
esrj-105296	296	4	9(11	9(11	NUM
esrj-105296	296	5	)	)	PUNCT
esrj-105296	296	6	,	,	PUNCT
esrj-105296	296	7	1110	1110	NUM
esrj-105296	296	8	.	.	PUNCT
esrj-105296	297	1	https://doi.org/10.3390/rs9111110	https://doi.org/10.3390/rs9111110	PROPN
esrj-105296	297	2	agarwal	agarwal	PROPN
esrj-105296	297	3	,	,	PUNCT
esrj-105296	297	4	m.	m.	NOUN
esrj-105296	297	5	,	,	PUNCT
esrj-105296	297	6	rajak	rajak	PROPN
esrj-105296	297	7	,	,	PUNCT
esrj-105296	297	8	a.	a.	NOUN
esrj-105296	297	9	,	,	PUNCT
esrj-105296	297	10	&	&	CCONJ
esrj-105296	297	11	shrivastava	shrivastava	PROPN
esrj-105296	297	12	,	,	PUNCT
esrj-105296	297	13	a.	a.	PROPN
esrj-105296	297	14	k.	k.	PROPN
esrj-105296	297	15	(	(	PUNCT
esrj-105296	297	16	2021	2021	NUM
esrj-105296	297	17	)	)	PUNCT
esrj-105296	297	18	.	.	PUNCT
esrj-105296	298	1	assessment	assessment	NOUN
esrj-105296	298	2	of	of	ADP
esrj-105296	298	3	optimizers	optimizer	NOUN
esrj-105296	298	4	impact	impact	NOUN
esrj-105296	298	5	on	on	ADP
esrj-105296	298	6	image	image	NOUN
esrj-105296	298	7	recognition	recognition	NOUN
esrj-105296	298	8	with	with	ADP
esrj-105296	298	9	convolutional	convolutional	ADJ
esrj-105296	298	10	neural	neural	ADJ
esrj-105296	298	11	network	network	NOUN
esrj-105296	298	12	to	to	ADP
esrj-105296	298	13	adversarial	adversarial	ADJ
esrj-105296	298	14	datasets	dataset	NOUN
esrj-105296	298	15	.	.	PUNCT
esrj-105296	299	1	journal	journal	PROPN
esrj-105296	299	2	of	of	ADP
esrj-105296	299	3	physics	physics	PROPN
esrj-105296	299	4	:	:	PUNCT
esrj-105296	299	5	conference	conference	NOUN
esrj-105296	299	6	series	series	NOUN
esrj-105296	299	7	.	.	PUNCT
esrj-105296	300	1	https://doi	https://doi	PROPN
esrj-105296	300	2	.	.	PUNCT
esrj-105296	300	3	org/10.1088/1742	org/10.1088/1742	PROPN
esrj-105296	300	4	-	-	PUNCT
esrj-105296	300	5	6596/1998/1/012008	6596/1998/1/012008	NUM
esrj-105296	300	6	akar	akar	NOUN
esrj-105296	300	7	,	,	PUNCT
esrj-105296	300	8	o.	o.	PROPN
esrj-105296	300	9	,	,	PUNCT
esrj-105296	300	10	&	&	CCONJ
esrj-105296	300	11	tunc	tunc	PROPN
esrj-105296	300	12	gormus	gormus	PROPN
esrj-105296	300	13	,	,	PUNCT
esrj-105296	300	14	e.	e.	PROPN
esrj-105296	300	15	(	(	PUNCT
esrj-105296	300	16	2021	2021	NUM
esrj-105296	300	17	)	)	PUNCT
esrj-105296	300	18	.	.	PUNCT
esrj-105296	301	1	land	land	NOUN
esrj-105296	301	2	use	use	NOUN
esrj-105296	301	3	/	/	SYM
esrj-105296	301	4	land	land	NOUN
esrj-105296	301	5	cover	cover	NOUN
esrj-105296	301	6	mapping	mapping	NOUN
esrj-105296	301	7	from	from	ADP
esrj-105296	301	8	airborne	airborne	ADJ
esrj-105296	301	9	hyperspectral	hyperspectral	ADJ
esrj-105296	301	10	images	image	NOUN
esrj-105296	301	11	with	with	ADP
esrj-105296	301	12	machine	machine	NOUN
esrj-105296	301	13	learning	learn	VERB
esrj-105296	301	14	algorithms	algorithm	NOUN
esrj-105296	301	15	and	and	CCONJ
esrj-105296	301	16	contextual	contextual	ADJ
esrj-105296	301	17	information	information	NOUN
esrj-105296	301	18	.	.	PUNCT
esrj-105296	302	1	geocarto	geocarto	PROPN
esrj-105296	302	2	international	international	PROPN
esrj-105296	302	3	,	,	PUNCT
esrj-105296	302	4	1	1	NUM
esrj-105296	302	5	-	-	SYM
esrj-105296	302	6	28	28	NUM
esrj-105296	302	7	.	.	PUNCT
esrj-105296	303	1	https://doi.org/10.1080/	https://doi.org/10.1080/	NOUN
esrj-105296	303	2	10106049.2021.1945149	10106049.2021.1945149	NUM
esrj-105296	303	3	akın	akın	NOUN
esrj-105296	303	4	,	,	PUNCT
esrj-105296	303	5	a.	a.	NOUN
esrj-105296	303	6	t.	t.	PROPN
esrj-105296	303	7	,	,	PUNCT
esrj-105296	303	8	&	&	CCONJ
esrj-105296	303	9	cömert	cömert	PROPN
esrj-105296	303	10	,	,	PUNCT
esrj-105296	303	11	ç	ç	X
esrj-105296	303	12	.	.	PUNCT
esrj-105296	303	13	(	(	PUNCT
esrj-105296	303	14	2023	2023	NUM
esrj-105296	303	15	)	)	PUNCT
esrj-105296	303	16	.	.	PUNCT
esrj-105296	304	1	the	the	DET
esrj-105296	304	2	development	development	NOUN
esrj-105296	304	3	of	of	ADP
esrj-105296	304	4	an	an	DET
esrj-105296	304	5	augmented	augment	VERB
esrj-105296	304	6	reality	reality	NOUN
esrj-105296	304	7	audio	audio	NOUN
esrj-105296	304	8	application	application	NOUN
esrj-105296	304	9	for	for	ADP
esrj-105296	304	10	visually	visually	ADV
esrj-105296	304	11	impaired	impair	VERB
esrj-105296	304	12	persons	person	NOUN
esrj-105296	304	13	.	.	PUNCT
esrj-105296	305	1	multimedia	multimedia	NOUN
esrj-105296	305	2	tools	tool	NOUN
esrj-105296	305	3	and	and	CCONJ
esrj-105296	305	4	applications	application	NOUN
esrj-105296	305	5	,	,	PUNCT
esrj-105296	305	6	82(11	82(11	NUM
esrj-105296	305	7	)	)	PUNCT
esrj-105296	305	8	,	,	PUNCT
esrj-105296	305	9	17493	17493	NUM
esrj-105296	305	10	-	-	SYM
esrj-105296	305	11	17512	17512	NUM
esrj-105296	305	12	.	.	PUNCT
esrj-105296	306	1	https://doi.org/10.1007/s11042-022-14134-x	https://doi.org/10.1007/s11042-022-14134-x	PROPN
esrj-105296	306	2	ardouin	ardouin	PROPN
esrj-105296	306	3	,	,	PUNCT
esrj-105296	306	4	j.-p	j.-p	PROPN
esrj-105296	306	5	.	.	PROPN
esrj-105296	306	6	,	,	PUNCT
esrj-105296	306	7	lévesque	lévesque	PROPN
esrj-105296	306	8	,	,	PUNCT
esrj-105296	306	9	j.	j.	PROPN
esrj-105296	306	10	,	,	PUNCT
esrj-105296	306	11	&	&	CCONJ
esrj-105296	306	12	rea	rea	PROPN
esrj-105296	306	13	,	,	PUNCT
esrj-105296	306	14	t.	t.	PROPN
esrj-105296	306	15	a.	a.	NOUN
esrj-105296	306	16	(	(	PUNCT
esrj-105296	306	17	2007	2007	NUM
esrj-105296	306	18	)	)	PUNCT
esrj-105296	306	19	.	.	PUNCT
esrj-105296	307	1	a	a	DET
esrj-105296	307	2	demonstration	demonstration	NOUN
esrj-105296	307	3	of	of	ADP
esrj-105296	307	4	hyperspectral	hyperspectral	ADJ
esrj-105296	307	5	image	image	NOUN
esrj-105296	307	6	exploitation	exploitation	NOUN
esrj-105296	307	7	for	for	ADP
esrj-105296	307	8	military	military	ADJ
esrj-105296	307	9	applications	application	NOUN
esrj-105296	307	10	.	.	PUNCT
esrj-105296	308	1	10th	10th	ADJ
esrj-105296	308	2	international	international	ADJ
esrj-105296	308	3	conference	conference	NOUN
esrj-105296	308	4	on	on	ADP
esrj-105296	308	5	information	information	NOUN
esrj-105296	308	6	fusion	fusion	NOUN
esrj-105296	308	7	.	.	PUNCT
esrj-105296	309	1	https://doi.org/10.1109/icif.2007.4408184	https://doi.org/10.1109/icif.2007.4408184	PROPN
esrj-105296	309	2	ariff	ariff	NOUN
esrj-105296	309	3	,	,	PUNCT
esrj-105296	309	4	n.	n.	PROPN
esrj-105296	309	5	a.	a.	NOUN
esrj-105296	309	6	m.	m.	PROPN
esrj-105296	309	7	,	,	PUNCT
esrj-105296	309	8	&	&	CCONJ
esrj-105296	309	9	ismail	ismail	PROPN
esrj-105296	309	10	,	,	PUNCT
esrj-105296	309	11	a.	a.	PROPN
esrj-105296	309	12	r.	r.	PROPN
esrj-105296	309	13	(	(	PUNCT
esrj-105296	309	14	2023	2023	NUM
esrj-105296	309	15	)	)	PUNCT
esrj-105296	309	16	.	.	PUNCT
esrj-105296	310	1	study	study	NOUN
esrj-105296	310	2	of	of	ADP
esrj-105296	310	3	adam	adam	PROPN
esrj-105296	310	4	and	and	CCONJ
esrj-105296	310	5	adamax	adamax	ADJ
esrj-105296	310	6	optimizers	optimizer	NOUN
esrj-105296	310	7	on	on	ADP
esrj-105296	310	8	alexnet	alexnet	ADJ
esrj-105296	310	9	architecture	architecture	NOUN
esrj-105296	310	10	for	for	ADP
esrj-105296	310	11	voice	voice	NOUN
esrj-105296	310	12	biometric	biometric	ADJ
esrj-105296	310	13	authentication	authentication	NOUN
esrj-105296	310	14	system	system	NOUN
esrj-105296	310	15	.	.	PUNCT
esrj-105296	311	1	17th	17th	ADJ
esrj-105296	311	2	international	international	ADJ
esrj-105296	311	3	conference	conference	NOUN
esrj-105296	311	4	on	on	ADP
esrj-105296	311	5	ubiquitous	ubiquitous	ADJ
esrj-105296	311	6	information	information	NOUN
esrj-105296	311	7	management	management	NOUN
esrj-105296	311	8	and	and	CCONJ
esrj-105296	311	9	communication	communication	NOUN
esrj-105296	311	10	(	(	PUNCT
esrj-105296	311	11	imcom	imcom	PROPN
esrj-105296	311	12	)	)	PUNCT
esrj-105296	311	13	.	.	PUNCT
esrj-105296	312	1	https://doi.org/10.1109/imcom56909.2023.10035592	https://doi.org/10.1109/imcom56909.2023.10035592	PROPN
esrj-105296	312	2	basantia	basantia	PROPN
esrj-105296	312	3	,	,	PUNCT
esrj-105296	312	4	n.	n.	NOUN
esrj-105296	312	5	,	,	PUNCT
esrj-105296	312	6	nollet	nollet	NOUN
esrj-105296	312	7	,	,	PUNCT
esrj-105296	312	8	l.	l.	PROPN
esrj-105296	312	9	m.	m.	PROPN
esrj-105296	312	10	,	,	PUNCT
esrj-105296	312	11	&	&	CCONJ
esrj-105296	312	12	kamruzzaman	kamruzzaman	PROPN
esrj-105296	312	13	,	,	PUNCT
esrj-105296	312	14	m.	m.	NOUN
esrj-105296	312	15	(	(	PUNCT
esrj-105296	312	16	2018	2018	NUM
esrj-105296	312	17	)	)	PUNCT
esrj-105296	312	18	.	.	PUNCT
esrj-105296	313	1	hyperspectral	hyperspectral	ADJ
esrj-105296	313	2	imaging	imaging	NOUN
esrj-105296	313	3	analysis	analysis	NOUN
esrj-105296	313	4	and	and	CCONJ
esrj-105296	313	5	applications	application	NOUN
esrj-105296	313	6	for	for	ADP
esrj-105296	313	7	food	food	NOUN
esrj-105296	313	8	quality	quality	NOUN
esrj-105296	313	9	.	.	PUNCT
esrj-105296	314	1	crc	crc	PROPN
esrj-105296	314	2	press	press	PROPN
esrj-105296	314	3	.	.	PUNCT
esrj-105296	315	1	https://doi	https://doi	PROPN
esrj-105296	315	2	.	.	PROPN
esrj-105296	315	3	org/10.1201/9781315209203	org/10.1201/9781315209203	NOUN
esrj-105296	315	4	belgiu	belgiu	ADV
esrj-105296	315	5	,	,	PUNCT
esrj-105296	315	6	m.	m.	NOUN
esrj-105296	315	7	,	,	PUNCT
esrj-105296	315	8	&	&	CCONJ
esrj-105296	315	9	drăguţ	drăguţ	NOUN
esrj-105296	315	10	,	,	PUNCT
esrj-105296	315	11	l.	l.	PROPN
esrj-105296	315	12	(	(	PUNCT
esrj-105296	315	13	2016	2016	NUM
esrj-105296	315	14	)	)	PUNCT
esrj-105296	315	15	.	.	PUNCT
esrj-105296	316	1	random	random	ADJ
esrj-105296	316	2	forest	forest	NOUN
esrj-105296	316	3	in	in	ADP
esrj-105296	316	4	remote	remote	ADJ
esrj-105296	316	5	sensing	sensing	NOUN
esrj-105296	316	6	:	:	PUNCT
esrj-105296	316	7	a	a	DET
esrj-105296	316	8	review	review	NOUN
esrj-105296	316	9	of	of	ADP
esrj-105296	316	10	applications	application	NOUN
esrj-105296	316	11	and	and	CCONJ
esrj-105296	316	12	future	future	ADJ
esrj-105296	316	13	directions	direction	NOUN
esrj-105296	316	14	.	.	PUNCT
esrj-105296	317	1	isprs	isprs	NOUN
esrj-105296	317	2	journal	journal	PROPN
esrj-105296	317	3	of	of	ADP
esrj-105296	317	4	photogrammetry	photogrammetry	NOUN
esrj-105296	317	5	and	and	CCONJ
esrj-105296	317	6	remote	remote	ADJ
esrj-105296	317	7	sensing	sensing	NOUN
esrj-105296	317	8	,	,	PUNCT
esrj-105296	317	9	114	114	NUM
esrj-105296	317	10	,	,	PUNCT
esrj-105296	317	11	24	24	NUM
esrj-105296	317	12	-	-	SYM
esrj-105296	317	13	31	31	NUM
esrj-105296	317	14	.	.	PUNCT
esrj-105296	318	1	https://doi.org/10.1016/j.isprsjprs.2016.01.011	https://doi.org/10.1016/j.isprsjprs.2016.01.011	ADJ
esrj-105296	318	2	bera	bera	NOUN
esrj-105296	318	3	,	,	PUNCT
esrj-105296	318	4	s.	s.	PROPN
esrj-105296	318	5	,	,	PUNCT
esrj-105296	318	6	&	&	CCONJ
esrj-105296	318	7	shrivastava	shrivastava	PROPN
esrj-105296	318	8	,	,	PUNCT
esrj-105296	318	9	v.	v.	PROPN
esrj-105296	318	10	k.	k.	PROPN
esrj-105296	318	11	(	(	PUNCT
esrj-105296	318	12	2020	2020	NUM
esrj-105296	318	13	)	)	PUNCT
esrj-105296	318	14	.	.	PUNCT
esrj-105296	319	1	analysis	analysis	NOUN
esrj-105296	319	2	of	of	ADP
esrj-105296	319	3	various	various	ADJ
esrj-105296	319	4	optimizers	optimizer	NOUN
esrj-105296	319	5	on	on	ADP
esrj-105296	319	6	deep	deep	ADJ
esrj-105296	319	7	convolutional	convolutional	ADJ
esrj-105296	319	8	neural	neural	ADJ
esrj-105296	319	9	network	network	NOUN
esrj-105296	319	10	model	model	NOUN
esrj-105296	319	11	in	in	ADP
esrj-105296	319	12	the	the	DET
esrj-105296	319	13	application	application	NOUN
esrj-105296	319	14	of	of	ADP
esrj-105296	319	15	hyperspectral	hyperspectral	ADJ
esrj-105296	319	16	remote	remote	ADJ
esrj-105296	319	17	sensing	sense	VERB
esrj-105296	319	18	image	image	NOUN
esrj-105296	319	19	classification	classification	NOUN
esrj-105296	319	20	.	.	PUNCT
esrj-105296	320	1	international	international	ADJ
esrj-105296	320	2	journal	journal	NOUN
esrj-105296	320	3	of	of	ADP
esrj-105296	320	4	remote	remote	ADJ
esrj-105296	320	5	sensing	sensing	NOUN
esrj-105296	320	6	,	,	PUNCT
esrj-105296	320	7	41(7	41(7	NOUN
esrj-105296	320	8	)	)	PUNCT
esrj-105296	320	9	,	,	PUNCT
esrj-105296	320	10	2664	2664	NUM
esrj-105296	320	11	-	-	SYM
esrj-105296	320	12	2683	2683	NUM
esrj-105296	320	13	.	.	PUNCT
esrj-105296	321	1	https://doi.org/10.1080/01431161.20	https://doi.org/10.1080/01431161.20	NOUN
esrj-105296	321	2	19.1694725	19.1694725	NUM
esrj-105296	321	3	bhosle	bhosle	PROPN
esrj-105296	321	4	,	,	PUNCT
esrj-105296	321	5	k.	k.	PROPN
esrj-105296	321	6	,	,	PUNCT
esrj-105296	321	7	&	&	CCONJ
esrj-105296	321	8	musande	musande	PROPN
esrj-105296	321	9	,	,	PUNCT
esrj-105296	321	10	v.	v.	PROPN
esrj-105296	321	11	(	(	PUNCT
esrj-105296	321	12	2020	2020	NUM
esrj-105296	321	13	)	)	PUNCT
esrj-105296	321	14	.	.	PUNCT
esrj-105296	322	1	evaluation	evaluation	NOUN
esrj-105296	322	2	of	of	ADP
esrj-105296	322	3	cnn	cnn	PROPN
esrj-105296	322	4	model	model	NOUN
esrj-105296	322	5	by	by	ADP
esrj-105296	322	6	comparing	compare	VERB
esrj-105296	322	7	with	with	ADP
esrj-105296	322	8	convolutional	convolutional	ADJ
esrj-105296	322	9	autoencoder	autoencoder	NOUN
esrj-105296	322	10	and	and	CCONJ
esrj-105296	322	11	deep	deep	ADJ
esrj-105296	322	12	neural	neural	ADJ
esrj-105296	322	13	network	network	NOUN
esrj-105296	322	14	for	for	ADP
esrj-105296	322	15	crop	crop	NOUN
esrj-105296	322	16	classification	classification	NOUN
esrj-105296	322	17	on	on	ADP
esrj-105296	322	18	hyperspectral	hyperspectral	ADJ
esrj-105296	322	19	imagery	imagery	NOUN
esrj-105296	322	20	.	.	PUNCT
esrj-105296	323	1	geocarto	geocarto	PROPN
esrj-105296	323	2	international	international	PROPN
esrj-105296	323	3	,	,	PUNCT
esrj-105296	323	4	1	1	NUM
esrj-105296	323	5	-	-	SYM
esrj-105296	323	6	15	15	NUM
esrj-105296	323	7	.	.	PUNCT
esrj-105296	324	1	https://doi	https://doi	PROPN
esrj-105296	324	2	.	.	PUNCT
esrj-105296	324	3	org/10.1080/10106049.2020.1740950	org/10.1080/10106049.2020.1740950	PROPN
esrj-105296	324	4	boser	boser	PROPN
esrj-105296	324	5	,	,	PUNCT
esrj-105296	324	6	b.	b.	PROPN
esrj-105296	324	7	e.	e.	PROPN
esrj-105296	324	8	,	,	PUNCT
esrj-105296	324	9	guyon	guyon	PROPN
esrj-105296	324	10	,	,	PUNCT
esrj-105296	324	11	i.	i.	NOUN
esrj-105296	324	12	m.	m.	PROPN
esrj-105296	324	13	,	,	PUNCT
esrj-105296	324	14	&	&	CCONJ
esrj-105296	324	15	vapnik	vapnik	X
esrj-105296	324	16	,	,	PUNCT
esrj-105296	324	17	v.	v.	ADP
esrj-105296	324	18	n.	n.	PROPN
esrj-105296	324	19	(	(	PUNCT
esrj-105296	324	20	1992	1992	NUM
esrj-105296	324	21	)	)	PUNCT
esrj-105296	324	22	.	.	PUNCT
esrj-105296	325	1	a	a	DET
esrj-105296	325	2	training	training	NOUN
esrj-105296	325	3	algorithm	algorithm	NOUN
esrj-105296	325	4	for	for	ADP
esrj-105296	325	5	optimal	optimal	ADJ
esrj-105296	325	6	margin	margin	NOUN
esrj-105296	325	7	classifiers	classifier	NOUN
esrj-105296	325	8	.	.	PUNCT
esrj-105296	326	1	proceedings	proceeding	NOUN
esrj-105296	326	2	of	of	ADP
esrj-105296	326	3	the	the	DET
esrj-105296	326	4	5th	5th	ADJ
esrj-105296	326	5	annual	annual	ADJ
esrj-105296	326	6	acm	acm	NOUN
esrj-105296	326	7	workshop	workshop	NOUN
esrj-105296	326	8	on	on	ADP
esrj-105296	326	9	computational	computational	ADJ
esrj-105296	326	10	learning	learning	NOUN
esrj-105296	326	11	theory	theory	NOUN
esrj-105296	326	12	.	.	PUNCT
esrj-105296	327	1	https://doi	https://doi	X
esrj-105296	327	2	.	.	PUNCT
esrj-105296	327	3	org/10.1145/130385.130401	org/10.1145/130385.130401	PROPN
esrj-105296	327	4	breiman	breiman	PROPN
esrj-105296	327	5	,	,	PUNCT
esrj-105296	327	6	l.	l.	PROPN
esrj-105296	327	7	(	(	PUNCT
esrj-105296	327	8	2001	2001	NUM
esrj-105296	327	9	)	)	PUNCT
esrj-105296	327	10	.	.	PUNCT
esrj-105296	328	1	random	random	ADJ
esrj-105296	328	2	forests	forest	NOUN
esrj-105296	328	3	.	.	PUNCT
esrj-105296	329	1	machine	machine	NOUN
esrj-105296	329	2	learning	learning	PROPN
esrj-105296	329	3	,	,	PUNCT
esrj-105296	329	4	45(1	45(1	NOUN
esrj-105296	329	5	)	)	PUNCT
esrj-105296	329	6	,	,	PUNCT
esrj-105296	329	7	5	5	NUM
esrj-105296	329	8	-	-	SYM
esrj-105296	329	9	32	32	NUM
esrj-105296	329	10	.	.	PUNCT
esrj-105296	330	1	https://doi	https://doi	PROPN
esrj-105296	330	2	.	.	PUNCT
esrj-105296	330	3	org/10.1023	org/10.1023	PROPN
esrj-105296	330	4	/	/	SYM
esrj-105296	330	5	a:1010933404324	a:1010933404324	PROPN
esrj-105296	330	6	breiman	breiman	PROPN
esrj-105296	330	7	,	,	PUNCT
esrj-105296	330	8	l.	l.	PROPN
esrj-105296	330	9	,	,	PUNCT
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esrj-105296	330	11	,	,	PUNCT
esrj-105296	330	12	j.	j.	PROPN
esrj-105296	330	13	,	,	PUNCT
esrj-105296	330	14	stone	stone	PROPN
esrj-105296	330	15	,	,	PUNCT
esrj-105296	330	16	c.	c.	PROPN
esrj-105296	330	17	j.	j.	PROPN
esrj-105296	330	18	,	,	PUNCT
esrj-105296	330	19	&	&	CCONJ
esrj-105296	330	20	olshen	olshen	PROPN
esrj-105296	330	21	,	,	PUNCT
esrj-105296	330	22	r.	r.	PROPN
esrj-105296	330	23	a.	a.	PROPN
esrj-105296	330	24	(	(	PUNCT
esrj-105296	330	25	1984	1984	NUM
esrj-105296	330	26	)	)	PUNCT
esrj-105296	330	27	.	.	PUNCT
esrj-105296	331	1	classification	classification	NOUN
esrj-105296	331	2	and	and	CCONJ
esrj-105296	331	3	regression	regression	NOUN
esrj-105296	331	4	trees	tree	NOUN
esrj-105296	331	5	.	.	PUNCT
esrj-105296	332	1	crc	crc	PROPN
esrj-105296	332	2	press	press	PROPN
esrj-105296	332	3	.	.	PUNCT
esrj-105296	333	1	https://doi.org/10.1201/9781315139470	https://doi.org/10.1201/9781315139470	PROPN
esrj-105296	333	2	chan	chan	PROPN
esrj-105296	333	3	,	,	PUNCT
esrj-105296	333	4	j.	j.	PROPN
esrj-105296	333	5	c.	c.	PROPN
esrj-105296	333	6	w.	w.	PROPN
esrj-105296	333	7	,	,	PUNCT
esrj-105296	333	8	&	&	CCONJ
esrj-105296	333	9	paelinckx	paelinckx	PROPN
esrj-105296	333	10	,	,	PUNCT
esrj-105296	333	11	d.	d.	PROPN
esrj-105296	333	12	(	(	PUNCT
esrj-105296	333	13	2008	2008	NUM
esrj-105296	333	14	)	)	PUNCT
esrj-105296	333	15	.	.	PUNCT
esrj-105296	334	1	evaluation	evaluation	NOUN
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esrj-105296	334	3	random	random	ADJ
esrj-105296	334	4	forest	forest	NOUN
esrj-105296	334	5	and	and	CCONJ
esrj-105296	334	6	adaboost	adaboost	ADJ
esrj-105296	334	7	tree	tree	NOUN
esrj-105296	334	8	-	-	PUNCT
esrj-105296	334	9	based	base	VERB
esrj-105296	334	10	ensemble	ensemble	ADJ
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esrj-105296	334	18	mapping	mapping	NOUN
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esrj-105296	334	20	airborne	airborne	ADJ
esrj-105296	334	21	hyperspectral	hyperspectral	ADJ
esrj-105296	334	22	imagery	imagery	NOUN
esrj-105296	334	23	.	.	PUNCT
esrj-105296	335	1	remote	remote	ADJ
esrj-105296	335	2	sensing	sensing	NOUN
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esrj-105296	335	4	environment	environment	NOUN
esrj-105296	335	5	,	,	PUNCT
esrj-105296	335	6	112(6	112(6	NUM
esrj-105296	335	7	)	)	PUNCT
esrj-105296	335	8	,	,	PUNCT
esrj-105296	335	9	2999	2999	NUM
esrj-105296	335	10	-	-	SYM
esrj-105296	335	11	3011	3011	NUM
esrj-105296	335	12	.	.	PUNCT
esrj-105296	336	1	https://doi.org/10.1016/j	https://doi.org/10.1016/j	NOUN
esrj-105296	336	2	.	.	PUNCT
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esrj-105296	337	3	,	,	PUNCT
esrj-105296	337	4	s.	s.	PROPN
esrj-105296	337	5	,	,	PUNCT
esrj-105296	337	6	jin	jin	NOUN
esrj-105296	337	7	,	,	PUNCT
esrj-105296	337	8	m.	m.	NOUN
esrj-105296	337	9	,	,	PUNCT
esrj-105296	337	10	&	&	CCONJ
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esrj-105296	337	12	,	,	PUNCT
esrj-105296	337	13	j.	j.	PROPN
esrj-105296	337	14	(	(	PUNCT
esrj-105296	337	15	2020	2020	NUM
esrj-105296	337	16	)	)	PUNCT
esrj-105296	337	17	.	.	PUNCT
esrj-105296	338	1	hyperspectral	hyperspectral	ADJ
esrj-105296	338	2	remote	remote	ADJ
esrj-105296	338	3	sensing	sense	VERB
esrj-105296	338	4	image	image	NOUN
esrj-105296	338	5	classification	classification	NOUN
esrj-105296	338	6	based	base	VERB
esrj-105296	338	7	on	on	ADP
esrj-105296	338	8	dense	dense	ADJ
esrj-105296	338	9	residual	residual	ADJ
esrj-105296	338	10	three	three	NUM
esrj-105296	338	11	-	-	PUNCT
esrj-105296	338	12	dimensional	dimensional	ADJ
esrj-105296	338	13	convolutional	convolutional	ADJ
esrj-105296	338	14	neural	neural	ADJ
esrj-105296	338	15	network	network	NOUN
esrj-105296	338	16	.	.	PUNCT
esrj-105296	339	1	multimedia	multimedia	NOUN
esrj-105296	339	2	tools	tool	NOUN
esrj-105296	339	3	and	and	CCONJ
esrj-105296	339	4	applications	application	NOUN
esrj-105296	339	5	,	,	PUNCT
esrj-105296	339	6	1	1	NUM
esrj-105296	339	7	-	-	SYM
esrj-105296	339	8	24	24	NUM
esrj-105296	339	9	.	.	PUNCT
esrj-105296	340	1	https://doi	https://doi	X
esrj-105296	340	2	.	.	PUNCT
esrj-105296	340	3	org/10.1007	org/10.1007	PROPN
esrj-105296	340	4	/	/	SYM
esrj-105296	340	5	s11042	s11042	PROPN
esrj-105296	340	6	-	-	PUNCT
esrj-105296	340	7	020	020	NUM
esrj-105296	340	8	-	-	PUNCT
esrj-105296	340	9	09480	09480	NUM
esrj-105296	340	10	-	-	SYM
esrj-105296	340	11	7	7	NUM
esrj-105296	340	12	chen	chen	PROPN
esrj-105296	340	13	,	,	PUNCT
esrj-105296	340	14	y.	y.	PROPN
esrj-105296	340	15	,	,	PUNCT
esrj-105296	340	16	jiang	jiang	PROPN
esrj-105296	340	17	,	,	PUNCT
esrj-105296	340	18	h.	h.	PROPN
esrj-105296	340	19	,	,	PUNCT
esrj-105296	340	20	li	li	PROPN
esrj-105296	340	21	,	,	PUNCT
esrj-105296	340	22	c.	c.	PROPN
esrj-105296	340	23	,	,	PUNCT
esrj-105296	340	24	jia	jia	PROPN
esrj-105296	340	25	,	,	PUNCT
esrj-105296	340	26	x.	x.	PROPN
esrj-105296	340	27	,	,	PUNCT
esrj-105296	340	28	&	&	CCONJ
esrj-105296	340	29	ghamisi	ghamisi	PROPN
esrj-105296	340	30	,	,	PUNCT
esrj-105296	340	31	p.	p.	NOUN
esrj-105296	340	32	(	(	PUNCT
esrj-105296	340	33	2016	2016	NUM
esrj-105296	340	34	)	)	PUNCT
esrj-105296	340	35	.	.	PUNCT
esrj-105296	341	1	deep	deep	ADJ
esrj-105296	341	2	feature	feature	NOUN
esrj-105296	341	3	extraction	extraction	NOUN
esrj-105296	341	4	and	and	CCONJ
esrj-105296	341	5	classification	classification	NOUN
esrj-105296	341	6	of	of	ADP
esrj-105296	341	7	hyperspectral	hyperspectral	ADJ
esrj-105296	341	8	images	image	NOUN
esrj-105296	341	9	based	base	VERB
esrj-105296	341	10	on	on	ADP
esrj-105296	341	11	convolutional	convolutional	ADJ
esrj-105296	341	12	neural	neural	ADJ
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esrj-105296	341	14	.	.	PUNCT
esrj-105296	342	1	ieee	ieee	NOUN
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esrj-105296	342	3	on	on	ADP
esrj-105296	342	4	geoscience	geoscience	NOUN
esrj-105296	342	5	and	and	CCONJ
esrj-105296	342	6	remote	remote	ADJ
esrj-105296	342	7	sensing	sensing	NOUN
esrj-105296	342	8	,	,	PUNCT
esrj-105296	342	9	54(10	54(10	NOUN
esrj-105296	342	10	)	)	PUNCT
esrj-105296	342	11	,	,	PUNCT
esrj-105296	342	12	6232	6232	NUM
esrj-105296	342	13	-	-	SYM
esrj-105296	342	14	6251	6251	NUM
esrj-105296	342	15	.	.	PUNCT
esrj-105296	343	1	https://doi.org/10.1109/tgrs.2016.2584107	https://doi.org/10.1109/tgrs.2016.2584107	PROPN
esrj-105296	343	2	cheng	cheng	PROPN
esrj-105296	343	3	,	,	PUNCT
esrj-105296	343	4	g.	g.	PROPN
esrj-105296	343	5	,	,	PUNCT
esrj-105296	343	6	yan	yan	PROPN
esrj-105296	343	7	,	,	PUNCT
esrj-105296	343	8	b.	b.	PROPN
esrj-105296	343	9	,	,	PUNCT
esrj-105296	343	10	shi	shi	PROPN
esrj-105296	343	11	,	,	PUNCT
esrj-105296	343	12	p.	p.	PROPN
esrj-105296	343	13	,	,	PUNCT
esrj-105296	343	14	li	li	PROPN
esrj-105296	343	15	,	,	PUNCT
esrj-105296	343	16	k.	k.	PROPN
esrj-105296	343	17	,	,	PUNCT
esrj-105296	343	18	yao	yao	PROPN
esrj-105296	343	19	,	,	PUNCT
esrj-105296	343	20	x.	x.	PROPN
esrj-105296	343	21	,	,	PUNCT
esrj-105296	343	22	guo	guo	PROPN
esrj-105296	343	23	,	,	PUNCT
esrj-105296	343	24	l.	l.	PROPN
esrj-105296	343	25	,	,	PUNCT
esrj-105296	343	26	&	&	CCONJ
esrj-105296	343	27	han	han	PROPN
esrj-105296	343	28	,	,	PUNCT
esrj-105296	343	29	j.	j.	PROPN
esrj-105296	343	30	(	(	PUNCT
esrj-105296	343	31	2022	2022	NUM
esrj-105296	343	32	)	)	PUNCT
esrj-105296	343	33	.	.	PUNCT
esrj-105296	344	1	prototype	prototype	NOUN
esrj-105296	344	2	-	-	PUNCT
esrj-105296	344	3	cnn	cnn	PROPN
esrj-105296	344	4	for	for	ADP
esrj-105296	344	5	few	few	ADJ
esrj-105296	344	6	-	-	PUNCT
esrj-105296	344	7	shot	shoot	VERB
esrj-105296	344	8	object	object	NOUN
esrj-105296	344	9	detection	detection	NOUN
esrj-105296	344	10	in	in	ADP
esrj-105296	344	11	remote	remote	ADJ
esrj-105296	344	12	sensing	sensing	NOUN
esrj-105296	344	13	images	image	NOUN
esrj-105296	344	14	.	.	PUNCT
esrj-105296	345	1	ieee	ieee	NOUN
esrj-105296	345	2	transactions	transaction	NOUN
esrj-105296	345	3	on	on	ADP
esrj-105296	345	4	geoscience	geoscience	NOUN
esrj-105296	345	5	and	and	CCONJ
esrj-105296	345	6	remote	remote	ADJ
esrj-105296	345	7	sensing	sensing	NOUN
esrj-105296	345	8	,	,	PUNCT
esrj-105296	345	9	60	60	NUM
esrj-105296	345	10	,	,	PUNCT
esrj-105296	345	11	1	1	NUM
esrj-105296	345	12	-	-	SYM
esrj-105296	345	13	10	10	NUM
esrj-105296	345	14	.	.	PUNCT
esrj-105296	346	1	https://	https://	PROPN
esrj-105296	346	2	doi.org/10.1109/tgrs.2021.3078507	doi.org/10.1109/tgrs.2021.3078507	PROPN
esrj-105296	346	3	chollet	chollet	NOUN
esrj-105296	346	4	,	,	PUNCT
esrj-105296	346	5	f.	f.	PROPN
esrj-105296	346	6	(	(	PUNCT
esrj-105296	346	7	2015	2015	NUM
esrj-105296	346	8	)	)	PUNCT
esrj-105296	346	9	.	.	PUNCT
esrj-105296	347	1	keras	keras	PROPN
esrj-105296	347	2	.	.	PUNCT
esrj-105296	348	1	https://github.com/fchollet/keras	https://github.com/fchollet/keras	PROPN
esrj-105296	348	2	christovam	christovam	PROPN
esrj-105296	348	3	,	,	PUNCT
esrj-105296	348	4	l.	l.	PROPN
esrj-105296	348	5	e.	e.	PROPN
esrj-105296	348	6	,	,	PUNCT
esrj-105296	348	7	pessoa	pessoa	NOUN
esrj-105296	348	8	,	,	PUNCT
esrj-105296	348	9	g.	g.	PROPN
esrj-105296	348	10	g.	g.	PROPN
esrj-105296	348	11	,	,	PUNCT
esrj-105296	348	12	shimabukuro	shimabukuro	PROPN
esrj-105296	348	13	,	,	PUNCT
esrj-105296	348	14	m.	m.	PROPN
esrj-105296	348	15	h.	h.	PROPN
esrj-105296	348	16	,	,	PUNCT
esrj-105296	348	17	&	&	CCONJ
esrj-105296	348	18	galo	galo	PROPN
esrj-105296	348	19	,	,	PUNCT
esrj-105296	348	20	m.	m.	PROPN
esrj-105296	348	21	l.	l.	PROPN
esrj-105296	348	22	b.	b.	PROPN
esrj-105296	348	23	t.	t.	PROPN
esrj-105296	348	24	(	(	PUNCT
esrj-105296	348	25	2019	2019	NUM
esrj-105296	348	26	,	,	PUNCT
esrj-105296	348	27	10–14	10–14	NUM
esrj-105296	348	28	june	june	PROPN
esrj-105296	348	29	2019	2019	NUM
esrj-105296	348	30	)	)	PUNCT
esrj-105296	348	31	.	.	PUNCT
esrj-105296	349	1	land	land	NOUN
esrj-105296	349	2	use	use	NOUN
esrj-105296	349	3	and	and	CCONJ
esrj-105296	349	4	land	land	NOUN
esrj-105296	349	5	cover	cover	NOUN
esrj-105296	349	6	classification	classification	NOUN
esrj-105296	349	7	using	use	VERB
esrj-105296	349	8	hyperspectral	hyperspectral	ADJ
esrj-105296	349	9	imagery	imagery	NOUN
esrj-105296	349	10	:	:	PUNCT
esrj-105296	349	11	evaluating	evaluate	VERB
esrj-105296	349	12	the	the	DET
esrj-105296	349	13	performance	performance	NOUN
esrj-105296	349	14	of	of	ADP
esrj-105296	349	15	spectral	spectral	ADJ
esrj-105296	349	16	angle	angle	PROPN
esrj-105296	349	17	mapper	mapper	PROPN
esrj-105296	349	18	,	,	PUNCT
esrj-105296	349	19	support	support	VERB
esrj-105296	349	20	vector	vector	NOUN
esrj-105296	349	21	machine	machine	NOUN
esrj-105296	349	22	and	and	CCONJ
esrj-105296	349	23	random	random	ADJ
esrj-105296	349	24	forest	forest	NOUN
esrj-105296	349	25	.	.	PUNCT
esrj-105296	350	1	international	international	ADJ
esrj-105296	350	2	archives	archive	NOUN
esrj-105296	350	3	of	of	ADP
esrj-105296	350	4	the	the	DET
esrj-105296	350	5	photogrammetry	photogrammetry	NOUN
esrj-105296	350	6	,	,	PUNCT
esrj-105296	350	7	remote	remote	ADJ
esrj-105296	350	8	sensing	sensing	NOUN
esrj-105296	350	9	&	&	CCONJ
esrj-105296	350	10	spatial	spatial	ADJ
esrj-105296	350	11	information	information	NOUN
esrj-105296	350	12	sciences	sciences	PROPN
esrj-105296	350	13	,	,	PUNCT
esrj-105296	350	14	enschede	enschede	PROPN
esrj-105296	350	15	,	,	PUNCT
esrj-105296	350	16	the	the	DET
esrj-105296	350	17	netherlands	netherlands	PROPN
esrj-105296	350	18	.	.	PUNCT
esrj-105296	351	1	https://doi.org/10.5194/isprs-archives-xlii-2-w13-1841-2019	https://doi.org/10.5194/isprs-archives-xlii-2-w13-1841-2019	PROPN
esrj-105296	351	2	cortes	corte	VERB
esrj-105296	351	3	,	,	PUNCT
esrj-105296	351	4	c.	c.	NOUN
esrj-105296	351	5	,	,	PUNCT
esrj-105296	351	6	&	&	CCONJ
esrj-105296	351	7	vapnik	vapnik	X
esrj-105296	351	8	,	,	PUNCT
esrj-105296	351	9	v.	v.	PROPN
esrj-105296	351	10	(	(	PUNCT
esrj-105296	351	11	1995	1995	NUM
esrj-105296	351	12	)	)	PUNCT
esrj-105296	351	13	.	.	PUNCT
esrj-105296	352	1	support	support	NOUN
esrj-105296	352	2	-	-	PUNCT
esrj-105296	352	3	vector	vector	NOUN
esrj-105296	352	4	networks	network	NOUN
esrj-105296	352	5	.	.	PUNCT
esrj-105296	353	1	machine	machine	NOUN
esrj-105296	353	2	learning	learning	PROPN
esrj-105296	353	3	,	,	PUNCT
esrj-105296	353	4	20(3	20(3	NOUN
esrj-105296	353	5	)	)	PUNCT
esrj-105296	353	6	,	,	PUNCT
esrj-105296	353	7	273	273	NUM
esrj-105296	353	8	-	-	SYM
esrj-105296	353	9	297	297	NUM
esrj-105296	353	10	.	.	PUNCT
esrj-105296	354	1	https://doi.org/10.1007/bf00994018	https://doi.org/10.1007/bf00994018	PROPN
esrj-105296	354	2	dubey	dubey	PROPN
esrj-105296	354	3	,	,	PUNCT
esrj-105296	354	4	s.	s.	PROPN
esrj-105296	354	5	r.	r.	PROPN
esrj-105296	354	6	,	,	PUNCT
esrj-105296	354	7	singh	singh	PROPN
esrj-105296	354	8	,	,	PUNCT
esrj-105296	354	9	s.	s.	PROPN
esrj-105296	354	10	k.	k.	PROPN
esrj-105296	354	11	,	,	PUNCT
esrj-105296	354	12	&	&	CCONJ
esrj-105296	354	13	chaudhuri	chaudhuri	PROPN
esrj-105296	354	14	,	,	PUNCT
esrj-105296	354	15	b.	b.	PROPN
esrj-105296	354	16	b.	b.	PROPN
esrj-105296	354	17	(	(	PUNCT
esrj-105296	354	18	2022	2022	NUM
esrj-105296	354	19	)	)	PUNCT
esrj-105296	354	20	.	.	PUNCT
esrj-105296	355	1	activation	activation	NOUN
esrj-105296	355	2	functions	function	NOUN
esrj-105296	355	3	in	in	ADP
esrj-105296	355	4	deep	deep	ADJ
esrj-105296	355	5	learning	learning	NOUN
esrj-105296	355	6	:	:	PUNCT
esrj-105296	355	7	a	a	DET
esrj-105296	355	8	comprehensive	comprehensive	ADJ
esrj-105296	355	9	survey	survey	NOUN
esrj-105296	355	10	and	and	CCONJ
esrj-105296	355	11	benchmark	benchmark	NOUN
esrj-105296	355	12	.	.	PUNCT
esrj-105296	356	1	neurocomputing	neurocomputing	NOUN
esrj-105296	356	2	,	,	PUNCT
esrj-105296	356	3	503	503	NUM
esrj-105296	356	4	,	,	PUNCT
esrj-105296	356	5	92	92	NUM
esrj-105296	356	6	-	-	SYM
esrj-105296	356	7	108	108	NUM
esrj-105296	356	8	.	.	PUNCT
esrj-105296	357	1	https://doi.org/10.1016/j.neucom.2022.06.111	https://doi.org/10.1016/j.neucom.2022.06.111	PROPN
esrj-105296	357	2	erturk	erturk	PROPN
esrj-105296	357	3	,	,	PUNCT
esrj-105296	357	4	a.	a.	PROPN
esrj-105296	357	5	,	,	PUNCT
esrj-105296	357	6	iordache	iordache	PROPN
esrj-105296	357	7	,	,	PUNCT
esrj-105296	357	8	m.	m.	NOUN
esrj-105296	357	9	d.	d.	PROPN
esrj-105296	357	10	,	,	PUNCT
esrj-105296	357	11	&	&	CCONJ
esrj-105296	357	12	plaza	plaza	PROPN
esrj-105296	357	13	,	,	PUNCT
esrj-105296	357	14	a.	a.	NOUN
esrj-105296	357	15	(	(	PUNCT
esrj-105296	357	16	2015	2015	NUM
esrj-105296	357	17	)	)	PUNCT
esrj-105296	357	18	.	.	PUNCT
esrj-105296	358	1	sparse	sparse	VERB
esrj-105296	358	2	unmixing	unmixing	NOUN
esrj-105296	358	3	-	-	PUNCT
esrj-105296	358	4	based	base	VERB
esrj-105296	358	5	change	change	NOUN
esrj-105296	358	6	detection	detection	NOUN
esrj-105296	358	7	for	for	ADP
esrj-105296	358	8	multitemporal	multitemporal	ADJ
esrj-105296	358	9	hyperspectral	hyperspectral	ADJ
esrj-105296	358	10	images	image	NOUN
esrj-105296	358	11	.	.	PUNCT
esrj-105296	359	1	ieee	ieee	PROPN
esrj-105296	359	2	journal	journal	PROPN
esrj-105296	359	3	of	of	ADP
esrj-105296	359	4	selected	select	VERB
esrj-105296	359	5	topics	topic	NOUN
esrj-105296	359	6	in	in	ADP
esrj-105296	359	7	applied	apply	VERB
esrj-105296	359	8	earth	earth	NOUN
esrj-105296	359	9	observations	observation	NOUN
esrj-105296	359	10	and	and	CCONJ
esrj-105296	359	11	remote	remote	ADJ
esrj-105296	359	12	sensing	sensing	NOUN
esrj-105296	359	13	,	,	PUNCT
esrj-105296	359	14	9(2	9(2	NUM
esrj-105296	359	15	)	)	PUNCT
esrj-105296	359	16	,	,	PUNCT
esrj-105296	359	17	708	708	NUM
esrj-105296	359	18	-	-	SYM
esrj-105296	359	19	719	719	NUM
esrj-105296	359	20	.	.	PUNCT
esrj-105296	360	1	https://doi.org/10.1109/jstars.2015.2477431	https://doi.org/10.1109/jstars.2015.2477431	PROPN
esrj-105296	360	2	fırat	fırat	PROPN
esrj-105296	360	3	,	,	PUNCT
esrj-105296	360	4	h.	h.	PROPN
esrj-105296	360	5	,	,	PUNCT
esrj-105296	360	6	asker	asker	NOUN
esrj-105296	360	7	,	,	PUNCT
esrj-105296	360	8	m.	m.	PROPN
esrj-105296	360	9	e.	e.	PROPN
esrj-105296	360	10	,	,	PUNCT
esrj-105296	360	11	bayındır	bayındır	PROPN
esrj-105296	360	12	,	,	PUNCT
esrj-105296	360	13	m.	m.	NOUN
esrj-105296	360	14	i̇.	i̇.	PROPN
esrj-105296	360	15	,	,	PUNCT
esrj-105296	360	16	&	&	CCONJ
esrj-105296	360	17	hanbay	hanbay	PROPN
esrj-105296	360	18	,	,	PUNCT
esrj-105296	360	19	d.	d.	PROPN
esrj-105296	360	20	(	(	PUNCT
esrj-105296	360	21	2022	2022	NUM
esrj-105296	360	22	)	)	PUNCT
esrj-105296	360	23	.	.	PUNCT
esrj-105296	361	1	hybrid	hybrid	NOUN
esrj-105296	361	2	3d/2d	3d/2d	NUM
esrj-105296	361	3	complete	complete	ADJ
esrj-105296	361	4	inception	inception	NOUN
esrj-105296	361	5	module	module	NOUN
esrj-105296	361	6	and	and	CCONJ
esrj-105296	361	7	convolutional	convolutional	ADJ
esrj-105296	361	8	neural	neural	ADJ
esrj-105296	361	9	network	network	NOUN
esrj-105296	361	10	for	for	ADP
esrj-105296	361	11	hyperspectral	hyperspectral	ADJ
esrj-105296	361	12	remote	remote	ADJ
esrj-105296	361	13	sensing	sense	VERB
esrj-105296	361	14	image	image	NOUN
esrj-105296	361	15	classification	classification	NOUN
esrj-105296	361	16	.	.	PUNCT
esrj-105296	362	1	neural	neural	ADJ
esrj-105296	362	2	processing	processing	NOUN
esrj-105296	362	3	letters	letter	NOUN
esrj-105296	362	4	,	,	PUNCT
esrj-105296	362	5	1	1	NUM
esrj-105296	362	6	-	-	SYM
esrj-105296	362	7	44	44	NUM
esrj-105296	362	8	.	.	PUNCT
esrj-105296	363	1	https://doi.org/10.1007/s11063-022-10929-z	https://doi.org/10.1007/s11063-022-10929-z	NOUN
esrj-105296	363	2	foody	foody	PROPN
esrj-105296	363	3	,	,	PUNCT
esrj-105296	363	4	g.	g.	PROPN
esrj-105296	363	5	m.	m.	PROPN
esrj-105296	363	6	(	(	PUNCT
esrj-105296	363	7	2004	2004	NUM
esrj-105296	363	8	)	)	PUNCT
esrj-105296	363	9	.	.	PUNCT
esrj-105296	364	1	thematic	thematic	ADJ
esrj-105296	364	2	map	map	NOUN
esrj-105296	364	3	comparison	comparison	NOUN
esrj-105296	364	4	.	.	PUNCT
esrj-105296	365	1	photogrammetric	photogrammetric	ADJ
esrj-105296	365	2	engineering	engineering	NOUN
esrj-105296	365	3	&	&	CCONJ
esrj-105296	365	4	remote	remote	ADJ
esrj-105296	365	5	sensing	sensing	NOUN
esrj-105296	365	6	,	,	PUNCT
esrj-105296	365	7	70(5	70(5	NUM
esrj-105296	365	8	)	)	PUNCT
esrj-105296	365	9	,	,	PUNCT
esrj-105296	365	10	627	627	NUM
esrj-105296	365	11	-	-	SYM
esrj-105296	365	12	633	633	NUM
esrj-105296	365	13	.	.	PUNCT
esrj-105296	366	1	https://doi.org/10.14358/pers.70.5.627	https://doi.org/10.14358/pers.70.5.627	PROPN
esrj-105296	366	2	ghanbari	ghanbari	NOUN
esrj-105296	366	3	,	,	PUNCT
esrj-105296	366	4	h.	h.	PROPN
esrj-105296	366	5	,	,	PUNCT
esrj-105296	366	6	mahdianpari	mahdianpari	PROPN
esrj-105296	366	7	,	,	PUNCT
esrj-105296	366	8	m.	m.	NOUN
esrj-105296	366	9	,	,	PUNCT
esrj-105296	366	10	homayouni	homayouni	PROPN
esrj-105296	366	11	,	,	PUNCT
esrj-105296	366	12	s.	s.	PROPN
esrj-105296	366	13	,	,	PUNCT
esrj-105296	366	14	&	&	CCONJ
esrj-105296	366	15	mohammadimanesh	mohammadimanesh	PROPN
esrj-105296	366	16	,	,	PUNCT
esrj-105296	366	17	f.	f.	PROPN
esrj-105296	366	18	(	(	PUNCT
esrj-105296	366	19	2021	2021	NUM
esrj-105296	366	20	)	)	PUNCT
esrj-105296	366	21	.	.	PUNCT
esrj-105296	367	1	a	a	DET
esrj-105296	367	2	meta	meta	ADJ
esrj-105296	367	3	-	-	PUNCT
esrj-105296	367	4	analysis	analysis	NOUN
esrj-105296	367	5	of	of	ADP
esrj-105296	367	6	convolutional	convolutional	ADJ
esrj-105296	367	7	neural	neural	ADJ
esrj-105296	367	8	networks	network	NOUN
esrj-105296	367	9	for	for	ADP
esrj-105296	367	10	remote	remote	ADJ
esrj-105296	367	11	sensing	sense	VERB
esrj-105296	367	12	applications	application	NOUN
esrj-105296	367	13	.	.	PUNCT
esrj-105296	368	1	ieee	ieee	PROPN
esrj-105296	368	2	journal	journal	PROPN
esrj-105296	368	3	of	of	ADP
esrj-105296	368	4	selected	select	VERB
esrj-105296	368	5	topics	topic	NOUN
esrj-105296	368	6	in	in	ADP
esrj-105296	368	7	applied	apply	VERB
esrj-105296	368	8	earth	earth	NOUN
esrj-105296	368	9	observations	observation	NOUN
esrj-105296	368	10	and	and	CCONJ
esrj-105296	368	11	remote	remote	ADJ
esrj-105296	368	12	sensing	sensing	NOUN
esrj-105296	368	13	,	,	PUNCT
esrj-105296	368	14	14	14	NUM
esrj-105296	368	15	,	,	PUNCT
esrj-105296	368	16	3602	3602	NUM
esrj-105296	368	17	-	-	SYM
esrj-105296	368	18	3613	3613	NUM
esrj-105296	368	19	.	.	PUNCT
esrj-105296	369	1	https://doi	https://doi	PROPN
esrj-105296	369	2	.	.	PUNCT
esrj-105296	369	3	org/10.1109	org/10.1109	PROPN
esrj-105296	369	4	/	/	SYM
esrj-105296	369	5	jstars.2021.3065569	jstars.2021.3065569	PROPN
esrj-105296	369	6	gualtieri	gualtieri	PROPN
esrj-105296	369	7	,	,	PUNCT
esrj-105296	369	8	j.	j.	PROPN
esrj-105296	369	9	,	,	PUNCT
esrj-105296	369	10	chettri	chettri	PROPN
esrj-105296	369	11	,	,	PUNCT
esrj-105296	369	12	s.	s.	PROPN
esrj-105296	369	13	r.	r.	PROPN
esrj-105296	369	14	,	,	PUNCT
esrj-105296	369	15	cromp	cromp	PROPN
esrj-105296	369	16	,	,	PUNCT
esrj-105296	369	17	r.	r.	PROPN
esrj-105296	369	18	,	,	PUNCT
esrj-105296	369	19	&	&	CCONJ
esrj-105296	369	20	johnson	johnson	PROPN
esrj-105296	369	21	,	,	PUNCT
esrj-105296	369	22	l.	l.	PROPN
esrj-105296	369	23	(	(	PUNCT
esrj-105296	369	24	1999	1999	NUM
esrj-105296	369	25	)	)	PUNCT
esrj-105296	369	26	.	.	PUNCT
esrj-105296	370	1	support	support	NOUN
esrj-105296	370	2	vector	vector	NOUN
esrj-105296	370	3	machine	machine	NOUN
esrj-105296	370	4	classifiers	classifier	NOUN
esrj-105296	370	5	as	as	SCONJ
esrj-105296	370	6	applied	apply	VERB
esrj-105296	370	7	to	to	ADP
esrj-105296	370	8	aviris	aviris	PROPN
esrj-105296	370	9	data	datum	NOUN
esrj-105296	370	10	.	.	PUNCT
esrj-105296	371	1	proceedings	proceeding	NOUN
esrj-105296	371	2	eighth	eighth	ADJ
esrj-105296	371	3	jpl	jpl	PROPN
esrj-105296	371	4	airborne	airborne	ADJ
esrj-105296	371	5	geoscience	geoscience	NOUN
esrj-105296	371	6	workshop	workshop	NOUN
esrj-105296	371	7	,	,	PUNCT
esrj-105296	371	8	pasadena	pasadena	PROPN
esrj-105296	371	9	.	.	PUNCT
esrj-105296	372	1	hang	hang	PROPN
esrj-105296	372	2	,	,	PUNCT
esrj-105296	372	3	r.	r.	PROPN
esrj-105296	372	4	,	,	PUNCT
esrj-105296	372	5	li	li	PROPN
esrj-105296	372	6	,	,	PUNCT
esrj-105296	372	7	z.	z.	PROPN
esrj-105296	372	8	,	,	PUNCT
esrj-105296	372	9	ghamisi	ghamisi	PROPN
esrj-105296	372	10	,	,	PUNCT
esrj-105296	372	11	p.	p.	PROPN
esrj-105296	372	12	,	,	PUNCT
esrj-105296	372	13	hong	hong	PROPN
esrj-105296	372	14	,	,	PUNCT
esrj-105296	372	15	d.	d.	PROPN
esrj-105296	372	16	,	,	PUNCT
esrj-105296	372	17	xia	xia	PROPN
esrj-105296	372	18	,	,	PUNCT
esrj-105296	372	19	g.	g.	PROPN
esrj-105296	372	20	,	,	PUNCT
esrj-105296	372	21	&	&	CCONJ
esrj-105296	372	22	liu	liu	PROPN
esrj-105296	372	23	,	,	PUNCT
esrj-105296	372	24	q.	q.	PROPN
esrj-105296	372	25	(	(	PUNCT
esrj-105296	372	26	2020	2020	NUM
esrj-105296	372	27	)	)	PUNCT
esrj-105296	372	28	.	.	PUNCT
esrj-105296	373	1	classification	classification	NOUN
esrj-105296	373	2	of	of	ADP
esrj-105296	373	3	hyperspectral	hyperspectral	ADJ
esrj-105296	373	4	and	and	CCONJ
esrj-105296	373	5	lidar	lidar	NOUN
esrj-105296	373	6	data	datum	NOUN
esrj-105296	373	7	using	use	VERB
esrj-105296	373	8	coupled	couple	VERB
esrj-105296	373	9	cnns	cnn	NOUN
esrj-105296	373	10	.	.	PUNCT
esrj-105296	374	1	arxiv	arxiv	PROPN
esrj-105296	374	2	preprint	preprint	PROPN
esrj-105296	374	3	arxiv:2002.01144v1	arxiv:2002.01144v1	PROPN
esrj-105296	374	4	,	,	PUNCT
esrj-105296	374	5	1	1	NUM
esrj-105296	374	6	,	,	PUNCT
esrj-105296	374	7	1	1	NUM
esrj-105296	374	8	-	-	SYM
esrj-105296	374	9	12	12	NUM
esrj-105296	374	10	.	.	PUNCT
esrj-105296	375	1	https://doi.org/10.48550/arxiv.2002.01144	https://doi.org/10.48550/arxiv.2002.01144	PROPN
esrj-105296	375	2	hao	hao	PROPN
esrj-105296	375	3	,	,	PUNCT
esrj-105296	375	4	w.	w.	PROPN
esrj-105296	375	5	,	,	PUNCT
esrj-105296	375	6	yizhou	yizhou	PROPN
esrj-105296	375	7	,	,	PUNCT
esrj-105296	375	8	w.	w.	PROPN
esrj-105296	375	9	,	,	PUNCT
esrj-105296	375	10	yaqin	yaqin	PROPN
esrj-105296	375	11	,	,	PUNCT
esrj-105296	375	12	l.	l.	PROPN
esrj-105296	375	13	,	,	PUNCT
esrj-105296	375	14	&	&	CCONJ
esrj-105296	375	15	zhili	zhili	PROPN
esrj-105296	375	16	,	,	PUNCT
esrj-105296	375	17	s.	s.	PROPN
esrj-105296	375	18	(	(	PUNCT
esrj-105296	375	19	2020	2020	NUM
esrj-105296	375	20	)	)	PUNCT
esrj-105296	375	21	.	.	PUNCT
esrj-105296	376	1	the	the	DET
esrj-105296	376	2	role	role	NOUN
esrj-105296	376	3	of	of	ADP
esrj-105296	376	4	activation	activation	NOUN
esrj-105296	376	5	function	function	NOUN
esrj-105296	376	6	in	in	ADP
esrj-105296	376	7	cnn	cnn	PROPN
esrj-105296	376	8	.	.	PUNCT
esrj-105296	377	1	2nd	2nd	ADJ
esrj-105296	377	2	international	international	ADJ
esrj-105296	377	3	conference	conference	NOUN
esrj-105296	377	4	on	on	ADP
esrj-105296	377	5	information	information	NOUN
esrj-105296	377	6	technology	technology	NOUN
esrj-105296	377	7	and	and	CCONJ
esrj-105296	377	8	computer	computer	NOUN
esrj-105296	377	9	application	application	NOUN
esrj-105296	377	10	(	(	PUNCT
esrj-105296	377	11	itca	itca	NOUN
esrj-105296	377	12	)	)	PUNCT
esrj-105296	377	13	.	.	PUNCT
esrj-105296	378	1	https://doi.org/https://doi.org/10.1109/	https://doi.org/https://doi.org/10.1109/	PROPN
esrj-105296	378	2	itca52113.2020.00096	itca52113.2020.00096	PROPN
esrj-105296	378	3	heiden	heiden	PROPN
esrj-105296	378	4	,	,	PUNCT
esrj-105296	378	5	u.	u.	PROPN
esrj-105296	378	6	,	,	PUNCT
esrj-105296	378	7	heldens	heldens	PROPN
esrj-105296	378	8	,	,	PUNCT
esrj-105296	378	9	w.	w.	NOUN
esrj-105296	378	10	,	,	PUNCT
esrj-105296	378	11	roessner	roessner	NOUN
esrj-105296	378	12	,	,	PUNCT
esrj-105296	378	13	s.	s.	PROPN
esrj-105296	378	14	,	,	PUNCT
esrj-105296	378	15	segl	segl	ADJ
esrj-105296	378	16	,	,	PUNCT
esrj-105296	378	17	k.	k.	PROPN
esrj-105296	378	18	,	,	PUNCT
esrj-105296	378	19	esch	esch	PROPN
esrj-105296	378	20	,	,	PUNCT
esrj-105296	378	21	t.	t.	PROPN
esrj-105296	378	22	,	,	PUNCT
esrj-105296	378	23	&	&	CCONJ
esrj-105296	378	24	mueller	mueller	PROPN
esrj-105296	378	25	,	,	PUNCT
esrj-105296	378	26	a.	a.	PROPN
esrj-105296	378	27	(	(	PUNCT
esrj-105296	378	28	2012	2012	NUM
esrj-105296	378	29	)	)	PUNCT
esrj-105296	378	30	.	.	PUNCT
esrj-105296	379	1	urban	urban	ADJ
esrj-105296	379	2	structure	structure	NOUN
esrj-105296	379	3	type	type	NOUN
esrj-105296	379	4	characterization	characterization	NOUN
esrj-105296	379	5	using	use	VERB
esrj-105296	379	6	hyperspectral	hyperspectral	ADJ
esrj-105296	379	7	remote	remote	ADJ
esrj-105296	379	8	sensing	sensing	NOUN
esrj-105296	379	9	and	and	CCONJ
esrj-105296	379	10	height	height	NOUN
esrj-105296	379	11	information	information	NOUN
esrj-105296	379	12	.	.	PUNCT
esrj-105296	380	1	landscape	landscape	NOUN
esrj-105296	380	2	and	and	CCONJ
esrj-105296	380	3	urban	urban	ADJ
esrj-105296	380	4	planning	planning	NOUN
esrj-105296	380	5	,	,	PUNCT
esrj-105296	380	6	105(4	105(4	NUM
esrj-105296	380	7	)	)	PUNCT
esrj-105296	380	8	,	,	PUNCT
esrj-105296	380	9	361375	361375	NUM
esrj-105296	380	10	.	.	PUNCT
esrj-105296	381	1	https://doi.org/10.1016/j.landurbplan.2012.01.001	https://doi.org/10.1016/j.landurbplan.2012.01.001	PROPN
esrj-105296	381	2	hsu	hsu	PROPN
esrj-105296	381	3	,	,	PUNCT
esrj-105296	381	4	c.	c.	PROPN
esrj-105296	381	5	w.	w.	PROPN
esrj-105296	381	6	,	,	PUNCT
esrj-105296	381	7	chang	chang	PROPN
esrj-105296	381	8	,	,	PUNCT
esrj-105296	381	9	c.	c.	PROPN
esrj-105296	381	10	c.	c.	PROPN
esrj-105296	381	11	,	,	PUNCT
esrj-105296	381	12	&	&	CCONJ
esrj-105296	381	13	lin	lin	PROPN
esrj-105296	381	14	,	,	PUNCT
esrj-105296	381	15	c.	c.	PROPN
esrj-105296	381	16	j.	j.	PROPN
esrj-105296	381	17	(	(	PUNCT
esrj-105296	381	18	2003	2003	NUM
esrj-105296	381	19	)	)	PUNCT
esrj-105296	381	20	.	.	PUNCT
esrj-105296	382	1	a	a	DET
esrj-105296	382	2	practical	practical	ADJ
esrj-105296	382	3	guide	guide	NOUN
esrj-105296	382	4	to	to	PART
esrj-105296	382	5	support	support	VERB
esrj-105296	382	6	vector	vector	NOUN
esrj-105296	382	7	classification	classification	NOUN
esrj-105296	382	8	.	.	PUNCT
esrj-105296	383	1	taipei	taipei	PROPN
esrj-105296	383	2	,	,	PUNCT
esrj-105296	383	3	taiwan	taiwan	PROPN
esrj-105296	383	4	.	.	PUNCT
esrj-105296	384	1	https://doi.org/10.48550/arxiv.1605.08695	https://doi.org/10.48550/arxiv.1605.08695	PROPN
esrj-105296	384	2	https://doi.org/10.1016/j.asr.2022.11.046	https://doi.org/10.1016/j.asr.2022.11.046	ADJ
esrj-105296	384	3	https://doi.org/10.1016/j.asr.2022.11.046	https://doi.org/10.1016/j.asr.2022.11.046	PROPN
esrj-105296	384	4	https://doi.org/10.3390/rs9111110	https://doi.org/10.3390/rs9111110	PROPN
esrj-105296	384	5	https://doi.org/10.1088/1742-6596/1998/1/012008	https://doi.org/10.1088/1742-6596/1998/1/012008	PROPN
esrj-105296	384	6	https://doi.org/10.1088/1742-6596/1998/1/012008	https://doi.org/10.1088/1742-6596/1998/1/012008	PROPN
esrj-105296	384	7	https://doi.org/10.1080/10106049.2021.1945149	https://doi.org/10.1080/10106049.2021.1945149	VERB
esrj-105296	384	8	https://doi.org/10.1080/10106049.2021.1945149	https://doi.org/10.1080/10106049.2021.1945149	X
esrj-105296	384	9	https://doi.org/10.1007/s11042-022-14134-x	https://doi.org/10.1007/s11042-022-14134-x	PROPN
esrj-105296	385	1	https://doi.org/10.1109/icif.2007.4408184	https://doi.org/10.1109/icif.2007.4408184	PROPN
esrj-105296	385	2	https://doi.org/10.1109/imcom56909.2023.10035592	https://doi.org/10.1109/imcom56909.2023.10035592	PROPN
esrj-105296	385	3	https://doi.org/10.1109/imcom56909.2023.10035592	https://doi.org/10.1109/imcom56909.2023.10035592	PROPN
esrj-105296	385	4	https://doi.org/10.1201/9781315209203	https://doi.org/10.1201/9781315209203	ADJ
esrj-105296	385	5	https://doi.org/10.1201/9781315209203	https://doi.org/10.1201/9781315209203	NUM
esrj-105296	385	6	https://doi.org/10.1016/j.isprsjprs.2016.01.011	https://doi.org/10.1016/j.isprsjprs.2016.01.011	NUM
esrj-105296	385	7	https://doi.org/10.1016/j.isprsjprs.2016.01.011	https://doi.org/10.1016/j.isprsjprs.2016.01.011	ADJ
esrj-105296	385	8	https://doi.org/10.1080/01431161.2019.1694725	https://doi.org/10.1080/01431161.2019.1694725	ADJ
esrj-105296	385	9	https://doi.org/10.1080/01431161.2019.1694725	https://doi.org/10.1080/01431161.2019.1694725	PROPN
esrj-105296	385	10	https://doi.org/10.1080/10106049.2020.1740950	https://doi.org/10.1080/10106049.2020.1740950	X
esrj-105296	386	1	https://doi.org/10.1080/10106049.2020.1740950	https://doi.org/10.1080/10106049.2020.1740950	X
esrj-105296	387	1	https://doi.org/10.1145/130385.130401	https://doi.org/10.1145/130385.130401	NOUN
esrj-105296	387	2	https://doi.org/10.1145/130385.130401	https://doi.org/10.1145/130385.130401	ADV
esrj-105296	388	1	https://doi.org/10.1023/a:1010933404324	https://doi.org/10.1023/a:1010933404324	ADP
esrj-105296	388	2	https://doi.org/10.1023/a:1010933404324	https://doi.org/10.1023/a:1010933404324	ADJ
esrj-105296	388	3	https://doi.org/10.1201/9781315139470	https://doi.org/10.1201/9781315139470	NOUN
esrj-105296	388	4	https://doi.org/10.1016/j.rse.2008.02.011	https://doi.org/10.1016/j.rse.2008.02.011	NOUN
esrj-105296	388	5	https://doi.org/10.1016/j.rse.2008.02.011	https://doi.org/10.1016/j.rse.2008.02.011	CCONJ
esrj-105296	388	6	https://doi.org/10.1007/s11042-020-09480-7	https://doi.org/10.1007/s11042-020-09480-7	NUM
esrj-105296	388	7	https://doi.org/10.1007/s11042-020-09480-7	https://doi.org/10.1007/s11042-020-09480-7	NUM
esrj-105296	388	8	https://doi.org/10.1109/tgrs.2016.2584107	https://doi.org/10.1109/tgrs.2016.2584107	PROPN
esrj-105296	388	9	https://doi.org/10.1109/tgrs.2021.3078507	https://doi.org/10.1109/tgrs.2021.3078507	PROPN
esrj-105296	388	10	https://doi.org/10.1109/tgrs.2021.3078507	https://doi.org/10.1109/tgrs.2021.3078507	PROPN
esrj-105296	388	11	https://github.com/fchollet/keras	https://github.com/fchollet/kera	NOUN
esrj-105296	388	12	https://doi.org/10.5194/isprs-archives-xlii-2-w13-1841-2019	https://doi.org/10.5194/isprs-archives-xlii-2-w13-1841-2019	PROPN
esrj-105296	388	13	https://doi.org/10.5194/isprs-archives-xlii-2-w13-1841-2019	https://doi.org/10.5194/isprs-archives-xlii-2-w13-1841-2019	PROPN
esrj-105296	388	14	https://doi.org/10.1007/bf00994018	https://doi.org/10.1007/bf00994018	VERB
esrj-105296	389	1	https://doi.org/10.1016/j.neucom.2022.06.111	https://doi.org/10.1016/j.neucom.2022.06.111	PROPN
esrj-105296	389	2	https://doi.org/10.1109/jstars.2015.2477431	https://doi.org/10.1109/jstars.2015.2477431	PROPN
esrj-105296	389	3	https://doi.org/10.1007/s11063-022-10929-z	https://doi.org/10.1007/s11063-022-10929-z	PROPN
esrj-105296	390	1	https://doi.org/10.14358/pers.70.5.627	https://doi.org/10.14358/pers.70.5.627	PROPN
esrj-105296	391	1	https://doi.org/10.1109/jstars.2021.3065569	https://doi.org/10.1109/jstars.2021.3065569	PROPN
esrj-105296	392	1	https://doi.org/10.1109/jstars.2021.3065569	https://doi.org/10.1109/jstars.2021.3065569	PROPN
esrj-105296	392	2	https://doi.org/10.48550/arxiv.2002.01144	https://doi.org/10.48550/arxiv.2002.01144	PROPN
esrj-105296	392	3	https://doi.org/https://doi.org/10.1109/itca52113.2020.00096	https://doi.org/https://doi.org/10.1109/itca52113.2020.00096	ADP
esrj-105296	392	4	https://doi.org/https://doi.org/10.1109/itca52113.2020.00096	https://doi.org/https://doi.org/10.1109/itca52113.2020.00096	PROPN
esrj-105296	392	5	https://doi.org/10.1016/j.landurbplan.2012.01.001	https://doi.org/10.1016/j.landurbplan.2012.01.001	PROPN
esrj-105296	392	6	173investigation	173investigation	PROPN
esrj-105296	392	7	of	of	ADP
esrj-105296	392	8	the	the	DET
esrj-105296	392	9	performances	performance	NOUN
esrj-105296	392	10	of	of	ADP
esrj-105296	392	11	support	support	NOUN
esrj-105296	392	12	vector	vector	NOUN
esrj-105296	392	13	machine	machine	NOUN
esrj-105296	392	14	,	,	PUNCT
esrj-105296	392	15	random	random	ADJ
esrj-105296	392	16	forest	forest	NOUN
esrj-105296	392	17	,	,	PUNCT
esrj-105296	392	18	and	and	CCONJ
esrj-105296	392	19	3d-2d	3d-2d	NUM
esrj-105296	392	20	convolutional	convolutional	ADJ
esrj-105296	392	21	neural	neural	ADJ
esrj-105296	392	22	network	network	NOUN
esrj-105296	392	23	for	for	ADP
esrj-105296	392	24	hyperspectral	hyperspectral	ADJ
esrj-105296	392	25	image	image	NOUN
esrj-105296	392	26	classification	classification	NOUN
esrj-105296	392	27	karantzalos	karantzalo	NOUN
esrj-105296	392	28	,	,	PUNCT
esrj-105296	392	29	k.	k.	PROPN
esrj-105296	392	30	,	,	PUNCT
esrj-105296	392	31	karakizi	karakizi	PROPN
esrj-105296	392	32	,	,	PUNCT
esrj-105296	392	33	c.	c.	PROPN
esrj-105296	392	34	,	,	PUNCT
esrj-105296	392	35	kandylakis	kandylakis	PROPN
esrj-105296	392	36	,	,	PUNCT
esrj-105296	392	37	z.	z.	PROPN
esrj-105296	392	38	,	,	PUNCT
esrj-105296	392	39	&	&	CCONJ
esrj-105296	392	40	antoniou	antoniou	PROPN
esrj-105296	392	41	,	,	PUNCT
esrj-105296	392	42	g.	g.	PROPN
esrj-105296	392	43	(	(	PUNCT
esrj-105296	392	44	2018	2018	NUM
esrj-105296	392	45	)	)	PUNCT
esrj-105296	392	46	.	.	PUNCT
esrj-105296	393	1	hyrank	hyrank	VERB
esrj-105296	393	2	hyperspectral	hyperspectral	ADJ
esrj-105296	393	3	satellite	satellite	NOUN
esrj-105296	393	4	dataset	dataset	NOUN
esrj-105296	394	1	i	i	PRON
esrj-105296	394	2	(	(	PUNCT
esrj-105296	394	3	version	version	NOUN
esrj-105296	394	4	v001	v001	NUM
esrj-105296	394	5	)	)	PUNCT
esrj-105296	394	6	.	.	PUNCT
esrj-105296	395	1	https://doi.org/10.5281/	https://doi.org/10.5281/	PROPN
esrj-105296	395	2	zenodo.1222202	zenodo.1222202	PROPN
esrj-105296	395	3	kavzoglu	kavzoglu	PROPN
esrj-105296	395	4	,	,	PUNCT
esrj-105296	395	5	t.	t.	PROPN
esrj-105296	395	6	,	,	PUNCT
esrj-105296	395	7	&	&	CCONJ
esrj-105296	395	8	colkesen	colkesen	PROPN
esrj-105296	395	9	,	,	PUNCT
esrj-105296	395	10	i.	i.	NOUN
esrj-105296	395	11	(	(	PUNCT
esrj-105296	395	12	2009	2009	NUM
esrj-105296	395	13	)	)	PUNCT
esrj-105296	395	14	.	.	PUNCT
esrj-105296	396	1	a	a	DET
esrj-105296	396	2	kernel	kernel	NOUN
esrj-105296	396	3	functions	function	VERB
esrj-105296	396	4	analysis	analysis	NOUN
esrj-105296	396	5	for	for	ADP
esrj-105296	396	6	support	support	NOUN
esrj-105296	396	7	vector	vector	NOUN
esrj-105296	396	8	machines	machine	NOUN
esrj-105296	396	9	for	for	ADP
esrj-105296	396	10	land	land	NOUN
esrj-105296	396	11	cover	cover	NOUN
esrj-105296	396	12	classification	classification	NOUN
esrj-105296	396	13	.	.	PUNCT
esrj-105296	397	1	international	international	ADJ
esrj-105296	397	2	journal	journal	PROPN
esrj-105296	397	3	of	of	ADP
esrj-105296	397	4	applied	apply	VERB
esrj-105296	397	5	earth	earth	NOUN
esrj-105296	397	6	observation	observation	NOUN
esrj-105296	397	7	and	and	CCONJ
esrj-105296	397	8	geoinformation	geoinformation	NOUN
esrj-105296	397	9	,	,	PUNCT
esrj-105296	397	10	11(5	11(5	NUM
esrj-105296	397	11	)	)	PUNCT
esrj-105296	397	12	,	,	PUNCT
esrj-105296	397	13	352	352	NUM
esrj-105296	397	14	-	-	SYM
esrj-105296	397	15	359	359	NUM
esrj-105296	397	16	.	.	PUNCT
esrj-105296	398	1	https://	https://	PROPN
esrj-105296	398	2	doi.org/10.1016/j.jag.2009.06.002	doi.org/10.1016/j.jag.2009.06.002	PROPN
esrj-105296	398	3	kingma	kingma	PROPN
esrj-105296	398	4	,	,	PUNCT
esrj-105296	398	5	d.	d.	PROPN
esrj-105296	398	6	p.	p.	PROPN
esrj-105296	398	7	,	,	PUNCT
esrj-105296	398	8	&	&	CCONJ
esrj-105296	398	9	ba	ba	PROPN
esrj-105296	398	10	,	,	PUNCT
esrj-105296	398	11	j.	j.	PROPN
esrj-105296	398	12	(	(	PUNCT
esrj-105296	398	13	2014	2014	NUM
esrj-105296	398	14	)	)	PUNCT
esrj-105296	398	15	.	.	PUNCT
esrj-105296	399	1	adam	adam	PROPN
esrj-105296	399	2	:	:	PUNCT
esrj-105296	399	3	a	a	DET
esrj-105296	399	4	method	method	NOUN
esrj-105296	399	5	for	for	ADP
esrj-105296	399	6	stochastic	stochastic	ADJ
esrj-105296	399	7	optimization	optimization	NOUN
esrj-105296	399	8	.	.	PUNCT
esrj-105296	400	1	arxiv	arxiv	PROPN
esrj-105296	400	2	preprint	preprint	VERB
esrj-105296	400	3	arxiv:1412.6980	arxiv:1412.6980	NUM
esrj-105296	400	4	,	,	PUNCT
esrj-105296	400	5	1	1	NUM
esrj-105296	400	6	,	,	PUNCT
esrj-105296	400	7	1	1	NUM
esrj-105296	400	8	-	-	SYM
esrj-105296	400	9	15	15	NUM
esrj-105296	400	10	.	.	PUNCT
esrj-105296	401	1	https://doi.org/10.48550/arxiv.1412.6980	https://doi.org/10.48550/arxiv.1412.6980	PROPN
esrj-105296	401	2	krizhevsky	krizhevsky	NOUN
esrj-105296	401	3	,	,	PUNCT
esrj-105296	401	4	a.	a.	PROPN
esrj-105296	401	5	,	,	PUNCT
esrj-105296	401	6	sutskever	sutskever	PROPN
esrj-105296	401	7	,	,	PUNCT
esrj-105296	401	8	i.	i.	PROPN
esrj-105296	401	9	,	,	PUNCT
esrj-105296	401	10	&	&	CCONJ
esrj-105296	401	11	hinton	hinton	PROPN
esrj-105296	401	12	,	,	PUNCT
esrj-105296	401	13	g.	g.	PROPN
esrj-105296	401	14	e.	e.	PROPN
esrj-105296	401	15	(	(	PUNCT
esrj-105296	401	16	2012	2012	NUM
esrj-105296	401	17	)	)	PUNCT
esrj-105296	401	18	.	.	PUNCT
esrj-105296	402	1	imagenet	imagenet	PROPN
esrj-105296	402	2	classification	classification	NOUN
esrj-105296	402	3	with	with	ADP
esrj-105296	402	4	deep	deep	ADJ
esrj-105296	402	5	convolutional	convolutional	ADJ
esrj-105296	402	6	neural	neural	ADJ
esrj-105296	402	7	networks	network	NOUN
esrj-105296	402	8	.	.	PUNCT
esrj-105296	403	1	proceedings	proceeding	NOUN
esrj-105296	403	2	of	of	ADP
esrj-105296	403	3	the	the	DET
esrj-105296	403	4	25th	25th	ADJ
esrj-105296	403	5	international	international	ADJ
esrj-105296	403	6	conference	conference	NOUN
esrj-105296	403	7	on	on	ADP
esrj-105296	403	8	neural	neural	ADJ
esrj-105296	403	9	information	information	NOUN
esrj-105296	403	10	processing	processing	NOUN
esrj-105296	403	11	systems	system	NOUN
esrj-105296	403	12	volume	volume	NOUN
esrj-105296	403	13	1	1	NUM
esrj-105296	403	14	,	,	PUNCT
esrj-105296	403	15	lake	lake	PROPN
esrj-105296	403	16	tahoe	tahoe	PROPN
esrj-105296	403	17	,	,	PUNCT
esrj-105296	403	18	nevada	nevada	PROPN
esrj-105296	403	19	.	.	PUNCT
esrj-105296	404	1	https://doi.org/10.1145/3065386	https://doi.org/10.1145/3065386	X
esrj-105296	405	1	kulkarni	kulkarni	PROPN
esrj-105296	405	2	,	,	PUNCT
esrj-105296	405	3	v.	v.	PROPN
esrj-105296	405	4	y.	y.	PROPN
esrj-105296	405	5	,	,	PUNCT
esrj-105296	405	6	&	&	CCONJ
esrj-105296	405	7	sinha	sinha	PROPN
esrj-105296	405	8	,	,	PUNCT
esrj-105296	405	9	p.	p.	PROPN
esrj-105296	405	10	k.	k.	PROPN
esrj-105296	405	11	(	(	PUNCT
esrj-105296	405	12	2012	2012	NUM
esrj-105296	405	13	)	)	PUNCT
esrj-105296	405	14	.	.	PUNCT
esrj-105296	406	1	pruning	prune	VERB
esrj-105296	406	2	of	of	ADP
esrj-105296	406	3	random	random	ADJ
esrj-105296	406	4	forest	forest	NOUN
esrj-105296	406	5	classifiers	classifier	NOUN
esrj-105296	406	6	:	:	PUNCT
esrj-105296	406	7	a	a	DET
esrj-105296	406	8	survey	survey	NOUN
esrj-105296	406	9	and	and	CCONJ
esrj-105296	406	10	future	future	ADJ
esrj-105296	406	11	directions	direction	NOUN
esrj-105296	406	12	.	.	PUNCT
esrj-105296	407	1	international	international	ADJ
esrj-105296	407	2	conference	conference	NOUN
esrj-105296	407	3	on	on	ADP
esrj-105296	407	4	data	data	PROPN
esrj-105296	407	5	science	science	PROPN
esrj-105296	407	6	&	&	CCONJ
esrj-105296	407	7	engineering	engineering	PROPN
esrj-105296	407	8	(	(	PUNCT
esrj-105296	407	9	icdse	icdse	PROPN
esrj-105296	407	10	)	)	PUNCT
esrj-105296	407	11	.	.	PUNCT
esrj-105296	408	1	https://doi.org/10.1109/icdse.2012.6282329	https://doi.org/10.1109/icdse.2012.6282329	PROPN
esrj-105296	408	2	lecun	lecun	PROPN
esrj-105296	408	3	,	,	PUNCT
esrj-105296	408	4	y.	y.	PROPN
esrj-105296	408	5	,	,	PUNCT
esrj-105296	408	6	bengio	bengio	PROPN
esrj-105296	408	7	,	,	PUNCT
esrj-105296	408	8	y.	y.	PROPN
esrj-105296	408	9	,	,	PUNCT
esrj-105296	408	10	&	&	CCONJ
esrj-105296	408	11	hinton	hinton	PROPN
esrj-105296	408	12	,	,	PUNCT
esrj-105296	408	13	g.	g.	PROPN
esrj-105296	408	14	(	(	PUNCT
esrj-105296	408	15	2015	2015	NUM
esrj-105296	408	16	)	)	PUNCT
esrj-105296	408	17	.	.	PUNCT
esrj-105296	409	1	deep	deep	ADJ
esrj-105296	409	2	learning	learning	NOUN
esrj-105296	409	3	.	.	PUNCT
esrj-105296	410	1	nature	nature	NOUN
esrj-105296	410	2	,	,	PUNCT
esrj-105296	410	3	521(7553	521(7553	NUM
esrj-105296	410	4	)	)	PUNCT
esrj-105296	410	5	,	,	PUNCT
esrj-105296	410	6	436	436	NUM
esrj-105296	410	7	-	-	SYM
esrj-105296	410	8	444	444	NUM
esrj-105296	410	9	.	.	PUNCT
esrj-105296	411	1	https://doi.org/10.1038/nature14539	https://doi.org/10.1038/nature14539	NOUN
esrj-105296	411	2	lecun	lecun	PROPN
esrj-105296	411	3	,	,	PUNCT
esrj-105296	411	4	y.	y.	PROPN
esrj-105296	411	5	,	,	PUNCT
esrj-105296	411	6	bottou	bottou	PROPN
esrj-105296	411	7	,	,	PUNCT
esrj-105296	411	8	l.	l.	PROPN
esrj-105296	411	9	,	,	PUNCT
esrj-105296	411	10	bengio	bengio	PROPN
esrj-105296	411	11	,	,	PUNCT
esrj-105296	411	12	y.	y.	PROPN
esrj-105296	411	13	,	,	PUNCT
esrj-105296	411	14	&	&	CCONJ
esrj-105296	411	15	haffner	haffner	PROPN
esrj-105296	411	16	,	,	PUNCT
esrj-105296	411	17	p.	p.	NOUN
esrj-105296	411	18	(	(	PUNCT
esrj-105296	411	19	1998	1998	NUM
esrj-105296	411	20	)	)	PUNCT
esrj-105296	411	21	.	.	PUNCT
esrj-105296	412	1	gradient	gradient	NOUN
esrj-105296	412	2	-	-	PUNCT
esrj-105296	412	3	based	base	VERB
esrj-105296	412	4	learning	learning	NOUN
esrj-105296	412	5	applied	apply	VERB
esrj-105296	412	6	to	to	ADP
esrj-105296	412	7	document	document	NOUN
esrj-105296	412	8	recognition	recognition	NOUN
esrj-105296	412	9	.	.	PUNCT
esrj-105296	413	1	proceedings	proceeding	NOUN
esrj-105296	413	2	of	of	ADP
esrj-105296	413	3	the	the	DET
esrj-105296	413	4	ieee	ieee	NOUN
esrj-105296	413	5	,	,	PUNCT
esrj-105296	413	6	86(11	86(11	NUM
esrj-105296	413	7	)	)	PUNCT
esrj-105296	413	8	,	,	PUNCT
esrj-105296	413	9	2278	2278	NUM
esrj-105296	413	10	-	-	SYM
esrj-105296	413	11	2324	2324	NUM
esrj-105296	413	12	.	.	PUNCT
esrj-105296	414	1	https://doi.org/10.1109/5.726791	https://doi.org/10.1109/5.726791	PROPN
esrj-105296	414	2	li	li	PROPN
esrj-105296	414	3	,	,	PUNCT
esrj-105296	414	4	y.	y.	PROPN
esrj-105296	414	5	,	,	PUNCT
esrj-105296	414	6	zhang	zhang	PROPN
esrj-105296	414	7	,	,	PUNCT
esrj-105296	414	8	h.	h.	PROPN
esrj-105296	414	9	,	,	PUNCT
esrj-105296	414	10	xue	xue	PROPN
esrj-105296	414	11	,	,	PUNCT
esrj-105296	414	12	x.	x.	PROPN
esrj-105296	414	13	,	,	PUNCT
esrj-105296	414	14	jiang	jiang	PROPN
esrj-105296	414	15	,	,	PUNCT
esrj-105296	414	16	y.	y.	PROPN
esrj-105296	414	17	,	,	PUNCT
esrj-105296	414	18	&	&	CCONJ
esrj-105296	414	19	shen	shen	PROPN
esrj-105296	414	20	,	,	PUNCT
esrj-105296	414	21	q.	q.	PROPN
esrj-105296	414	22	(	(	PUNCT
esrj-105296	414	23	2018	2018	NUM
esrj-105296	414	24	)	)	PUNCT
esrj-105296	414	25	.	.	PUNCT
esrj-105296	415	1	deep	deep	ADJ
esrj-105296	415	2	learning	learning	NOUN
esrj-105296	415	3	for	for	ADP
esrj-105296	415	4	remote	remote	ADJ
esrj-105296	415	5	sensing	sense	VERB
esrj-105296	415	6	image	image	NOUN
esrj-105296	415	7	classification	classification	NOUN
esrj-105296	415	8	:	:	PUNCT
esrj-105296	415	9	a	a	DET
esrj-105296	415	10	survey	survey	NOUN
esrj-105296	415	11	.	.	PUNCT
esrj-105296	416	1	wiley	wiley	PROPN
esrj-105296	416	2	interdisciplinary	interdisciplinary	ADJ
esrj-105296	416	3	reviews	review	NOUN
esrj-105296	416	4	:	:	PUNCT
esrj-105296	416	5	data	datum	NOUN
esrj-105296	416	6	mining	mining	NOUN
esrj-105296	416	7	and	and	CCONJ
esrj-105296	416	8	knowledge	knowledge	NOUN
esrj-105296	416	9	discovery	discovery	NOUN
esrj-105296	416	10	,	,	PUNCT
esrj-105296	416	11	8(6	8(6	NUM
esrj-105296	416	12	)	)	PUNCT
esrj-105296	416	13	,	,	PUNCT
esrj-105296	416	14	e1264	e1264	NOUN
esrj-105296	416	15	.	.	PUNCT
esrj-105296	417	1	https://doi	https://doi	PROPN
esrj-105296	417	2	.	.	PUNCT
esrj-105296	418	1	org/10.1002	org/10.1002	PROPN
esrj-105296	418	2	/	/	SYM
esrj-105296	418	3	widm.1264	widm.1264	PROPN
esrj-105296	418	4	loggenberg	loggenberg	PROPN
esrj-105296	418	5	,	,	PUNCT
esrj-105296	418	6	k.	k.	PROPN
esrj-105296	418	7	,	,	PUNCT
esrj-105296	418	8	strever	strever	PROPN
esrj-105296	418	9	,	,	PUNCT
esrj-105296	418	10	a.	a.	NOUN
esrj-105296	418	11	,	,	PUNCT
esrj-105296	418	12	greyling	greyling	NOUN
esrj-105296	418	13	,	,	PUNCT
esrj-105296	418	14	b.	b.	PROPN
esrj-105296	418	15	,	,	PUNCT
esrj-105296	418	16	&	&	CCONJ
esrj-105296	418	17	poona	poona	PROPN
esrj-105296	418	18	,	,	PUNCT
esrj-105296	418	19	n.	n.	PROPN
esrj-105296	418	20	(	(	PUNCT
esrj-105296	418	21	2018	2018	NUM
esrj-105296	418	22	)	)	PUNCT
esrj-105296	418	23	.	.	PUNCT
esrj-105296	419	1	modelling	model	VERB
esrj-105296	419	2	water	water	NOUN
esrj-105296	419	3	stress	stress	NOUN
esrj-105296	419	4	in	in	ADP
esrj-105296	419	5	a	a	DET
esrj-105296	419	6	shiraz	shiraz	NOUN
esrj-105296	419	7	vineyard	vineyard	NOUN
esrj-105296	419	8	using	use	VERB
esrj-105296	419	9	hyperspectral	hyperspectral	ADJ
esrj-105296	419	10	imaging	imaging	NOUN
esrj-105296	419	11	and	and	CCONJ
esrj-105296	419	12	machine	machine	NOUN
esrj-105296	419	13	learning	learning	NOUN
esrj-105296	419	14	.	.	PUNCT
esrj-105296	420	1	remote	remote	ADJ
esrj-105296	420	2	sensing	sensing	NOUN
esrj-105296	420	3	,	,	PUNCT
esrj-105296	420	4	10(2	10(2	NUM
esrj-105296	420	5	)	)	PUNCT
esrj-105296	420	6	,	,	PUNCT
esrj-105296	420	7	202	202	NUM
esrj-105296	420	8	.	.	PUNCT
esrj-105296	421	1	https://doi.org/10.3390/	https://doi.org/10.3390/	PROPN
esrj-105296	421	2	rs10020202	rs10020202	PROPN
esrj-105296	421	3	lu	lu	PROPN
esrj-105296	421	4	,	,	PUNCT
esrj-105296	421	5	g.	g.	PROPN
esrj-105296	421	6	,	,	PUNCT
esrj-105296	421	7	&	&	CCONJ
esrj-105296	421	8	fei	fei	PROPN
esrj-105296	421	9	,	,	PUNCT
esrj-105296	421	10	b.	b.	PROPN
esrj-105296	421	11	(	(	PUNCT
esrj-105296	421	12	2014	2014	NUM
esrj-105296	421	13	)	)	PUNCT
esrj-105296	421	14	.	.	PUNCT
esrj-105296	422	1	medical	medical	ADJ
esrj-105296	422	2	hyperspectral	hyperspectral	ADJ
esrj-105296	422	3	imaging	imaging	NOUN
esrj-105296	422	4	:	:	PUNCT
esrj-105296	422	5	a	a	DET
esrj-105296	422	6	review	review	NOUN
esrj-105296	422	7	.	.	PUNCT
esrj-105296	423	1	journal	journal	PROPN
esrj-105296	423	2	of	of	ADP
esrj-105296	423	3	biomedical	biomedical	ADJ
esrj-105296	423	4	optics	optic	NOUN
esrj-105296	423	5	,	,	PUNCT
esrj-105296	423	6	19(1	19(1	NUM
esrj-105296	423	7	)	)	PUNCT
esrj-105296	423	8	,	,	PUNCT
esrj-105296	423	9	010901	010901	NUM
esrj-105296	423	10	.	.	PUNCT
esrj-105296	424	1	https://doi.org/10.1117/1.jbo.19.1.010901	https://doi.org/10.1117/1.jbo.19.1.010901	PROPN
esrj-105296	424	2	luo	luo	PROPN
esrj-105296	424	3	,	,	PUNCT
esrj-105296	424	4	y.	y.	PROPN
esrj-105296	424	5	,	,	PUNCT
esrj-105296	424	6	zou	zou	PROPN
esrj-105296	424	7	,	,	PUNCT
esrj-105296	424	8	j.	j.	PROPN
esrj-105296	424	9	,	,	PUNCT
esrj-105296	424	10	yao	yao	PROPN
esrj-105296	424	11	,	,	PUNCT
esrj-105296	424	12	c.	c.	PROPN
esrj-105296	424	13	,	,	PUNCT
esrj-105296	424	14	zhao	zhao	PROPN
esrj-105296	424	15	,	,	PUNCT
esrj-105296	424	16	x.	x.	PROPN
esrj-105296	424	17	,	,	PUNCT
esrj-105296	424	18	li	li	PROPN
esrj-105296	424	19	,	,	PUNCT
esrj-105296	424	20	t.	t.	PROPN
esrj-105296	424	21	,	,	PUNCT
esrj-105296	424	22	&	&	CCONJ
esrj-105296	424	23	bai	bai	PROPN
esrj-105296	424	24	,	,	PUNCT
esrj-105296	424	25	g.	g.	PROPN
esrj-105296	424	26	(	(	PUNCT
esrj-105296	424	27	2018	2018	NUM
esrj-105296	424	28	)	)	PUNCT
esrj-105296	424	29	.	.	PUNCT
esrj-105296	425	1	hsi	hsi	PROPN
esrj-105296	425	2	-	-	PUNCT
esrj-105296	425	3	cnn	cnn	PROPN
esrj-105296	425	4	:	:	PUNCT
esrj-105296	425	5	a	a	DET
esrj-105296	425	6	novel	novel	ADJ
esrj-105296	425	7	convolution	convolution	NOUN
esrj-105296	425	8	neural	neural	ADJ
esrj-105296	425	9	network	network	NOUN
esrj-105296	425	10	for	for	ADP
esrj-105296	425	11	hyperspectral	hyperspectral	ADJ
esrj-105296	425	12	image	image	NOUN
esrj-105296	425	13	.	.	PUNCT
esrj-105296	426	1	2018	2018	NUM
esrj-105296	426	2	international	international	ADJ
esrj-105296	426	3	conference	conference	NOUN
esrj-105296	426	4	on	on	ADP
esrj-105296	426	5	audio	audio	NOUN
esrj-105296	426	6	,	,	PUNCT
esrj-105296	426	7	language	language	NOUN
esrj-105296	426	8	and	and	CCONJ
esrj-105296	426	9	image	image	NOUN
esrj-105296	426	10	processing	processing	NOUN
esrj-105296	426	11	(	(	PUNCT
esrj-105296	426	12	icalip	icalip	PROPN
esrj-105296	426	13	)	)	PUNCT
esrj-105296	426	14	,	,	PUNCT
esrj-105296	426	15	beijing	beijing	PROPN
esrj-105296	426	16	.	.	PUNCT
esrj-105296	427	1	https://doi.org/10.1109/icalip.2018.8455251	https://doi.org/10.1109/icalip.2018.8455251	PROPN
esrj-105296	427	2	melgani	melgani	PROPN
esrj-105296	427	3	,	,	PUNCT
esrj-105296	427	4	f.	f.	PROPN
esrj-105296	427	5	,	,	PUNCT
esrj-105296	427	6	&	&	CCONJ
esrj-105296	427	7	bruzzone	bruzzone	NOUN
esrj-105296	427	8	,	,	PUNCT
esrj-105296	427	9	l.	l.	PROPN
esrj-105296	427	10	(	(	PUNCT
esrj-105296	427	11	2004	2004	NUM
esrj-105296	427	12	)	)	PUNCT
esrj-105296	427	13	.	.	PUNCT
esrj-105296	428	1	classification	classification	NOUN
esrj-105296	428	2	of	of	ADP
esrj-105296	428	3	hyperspectral	hyperspectral	ADJ
esrj-105296	428	4	remote	remote	ADJ
esrj-105296	428	5	sensing	sensing	NOUN
esrj-105296	428	6	images	image	NOUN
esrj-105296	428	7	with	with	ADP
esrj-105296	428	8	support	support	NOUN
esrj-105296	428	9	vector	vector	NOUN
esrj-105296	428	10	machines	machine	NOUN
esrj-105296	428	11	.	.	PUNCT
esrj-105296	429	1	ieee	ieee	NOUN
esrj-105296	429	2	transactions	transaction	NOUN
esrj-105296	429	3	on	on	ADP
esrj-105296	429	4	geoscience	geoscience	NOUN
esrj-105296	429	5	and	and	CCONJ
esrj-105296	429	6	remote	remote	ADJ
esrj-105296	429	7	sensing	sensing	NOUN
esrj-105296	429	8	,	,	PUNCT
esrj-105296	429	9	42(8	42(8	NUM
esrj-105296	429	10	)	)	PUNCT
esrj-105296	429	11	,	,	PUNCT
esrj-105296	429	12	1778	1778	NUM
esrj-105296	429	13	-	-	SYM
esrj-105296	429	14	1790	1790	NUM
esrj-105296	429	15	.	.	PUNCT
esrj-105296	430	1	https://doi.org/10.1109/	https://doi.org/10.1109/	PROPN
esrj-105296	430	2	tgrs.2004.831865	tgrs.2004.831865	PROPN
esrj-105296	430	3	meng	meng	PROPN
esrj-105296	430	4	,	,	PUNCT
esrj-105296	430	5	z.	z.	PROPN
esrj-105296	430	6	,	,	PUNCT
esrj-105296	430	7	zhao	zhao	PROPN
esrj-105296	430	8	,	,	PUNCT
esrj-105296	430	9	f.	f.	PROPN
esrj-105296	430	10	,	,	PUNCT
esrj-105296	430	11	liang	liang	PROPN
esrj-105296	430	12	,	,	PUNCT
esrj-105296	430	13	m.	m.	NOUN
esrj-105296	430	14	,	,	PUNCT
esrj-105296	430	15	&	&	CCONJ
esrj-105296	430	16	xie	xie	PROPN
esrj-105296	430	17	,	,	PUNCT
esrj-105296	430	18	w.	w.	PROPN
esrj-105296	430	19	(	(	PUNCT
esrj-105296	430	20	2021	2021	NUM
esrj-105296	430	21	)	)	PUNCT
esrj-105296	430	22	.	.	PUNCT
esrj-105296	431	1	deep	deep	ADJ
esrj-105296	431	2	residual	residual	ADJ
esrj-105296	431	3	involution	involution	NOUN
esrj-105296	431	4	network	network	NOUN
esrj-105296	431	5	for	for	ADP
esrj-105296	431	6	hyperspectral	hyperspectral	ADJ
esrj-105296	431	7	image	image	NOUN
esrj-105296	431	8	classification	classification	NOUN
esrj-105296	431	9	.	.	PUNCT
esrj-105296	432	1	remote	remote	ADJ
esrj-105296	432	2	sensing	sensing	NOUN
esrj-105296	432	3	,	,	PUNCT
esrj-105296	432	4	13(16	13(16	NUM
esrj-105296	432	5	)	)	PUNCT
esrj-105296	432	6	,	,	PUNCT
esrj-105296	432	7	3055	3055	NUM
esrj-105296	432	8	.	.	PUNCT
esrj-105296	433	1	https://doi.org/10.3390/rs13163055	https://doi.org/10.3390/rs13163055	PROPN
esrj-105296	433	2	misra	misra	PROPN
esrj-105296	433	3	,	,	PUNCT
esrj-105296	433	4	d.	d.	PROPN
esrj-105296	433	5	(	(	PUNCT
esrj-105296	433	6	2019	2019	NUM
esrj-105296	433	7	)	)	PUNCT
esrj-105296	433	8	.	.	PUNCT
esrj-105296	434	1	mish	mish	PROPN
esrj-105296	434	2	:	:	PUNCT
esrj-105296	434	3	a	a	DET
esrj-105296	434	4	self	self	NOUN
esrj-105296	434	5	regularized	regularize	VERB
esrj-105296	434	6	non	non	ADJ
esrj-105296	434	7	-	-	ADJ
esrj-105296	434	8	monotonic	monotonic	ADJ
esrj-105296	434	9	activation	activation	NOUN
esrj-105296	434	10	function	function	NOUN
esrj-105296	434	11	.	.	PUNCT
esrj-105296	435	1	arxiv	arxiv	PROPN
esrj-105296	435	2	preprint	preprint	NOUN
esrj-105296	435	3	arxiv:1908.08681	arxiv:1908.08681	PROPN
esrj-105296	435	4	.	.	PUNCT
esrj-105296	436	1	https://doi.org/10.48550/arxiv.1908.08681	https://doi.org/10.48550/arxiv.1908.08681	PROPN
esrj-105296	436	2	mountrakis	mountrakis	PROPN
esrj-105296	436	3	,	,	PUNCT
esrj-105296	436	4	g.	g.	PROPN
esrj-105296	436	5	,	,	PUNCT
esrj-105296	436	6	i	i	PRON
esrj-105296	436	7	m	m	PROPN
esrj-105296	436	8	,	,	PUNCT
esrj-105296	436	9	j.	j.	PROPN
esrj-105296	436	10	,	,	PUNCT
esrj-105296	436	11	&	&	CCONJ
esrj-105296	436	12	ogole	ogole	PROPN
esrj-105296	436	13	,	,	PUNCT
esrj-105296	436	14	c.	c.	PROPN
esrj-105296	436	15	(	(	PUNCT
esrj-105296	436	16	2011	2011	NUM
esrj-105296	436	17	)	)	PUNCT
esrj-105296	436	18	.	.	PUNCT
esrj-105296	437	1	support	support	NOUN
esrj-105296	437	2	vector	vector	NOUN
esrj-105296	437	3	machines	machine	NOUN
esrj-105296	437	4	in	in	ADP
esrj-105296	437	5	remote	remote	ADJ
esrj-105296	437	6	sensing	sensing	NOUN
esrj-105296	437	7	:	:	PUNCT
esrj-105296	437	8	a	a	DET
esrj-105296	437	9	review	review	NOUN
esrj-105296	437	10	.	.	PUNCT
esrj-105296	438	1	isprs	isprs	PROPN
esrj-105296	438	2	journal	journal	PROPN
esrj-105296	438	3	of	of	ADP
esrj-105296	438	4	photogrammetry	photogrammetry	NOUN
esrj-105296	438	5	and	and	CCONJ
esrj-105296	438	6	remote	remote	ADJ
esrj-105296	438	7	sensing	sensing	NOUN
esrj-105296	438	8	,	,	PUNCT
esrj-105296	438	9	66(3	66(3	NOUN
esrj-105296	438	10	)	)	PUNCT
esrj-105296	438	11	,	,	PUNCT
esrj-105296	438	12	247	247	NUM
esrj-105296	438	13	-	-	SYM
esrj-105296	438	14	259	259	NUM
esrj-105296	438	15	.	.	PUNCT
esrj-105296	439	1	https://doi.org/10.1016/j.isprsjprs.2010.11.001	https://doi.org/10.1016/j.isprsjprs.2010.11.001	NOUN
esrj-105296	439	2	nair	nair	NOUN
esrj-105296	439	3	,	,	PUNCT
esrj-105296	439	4	v.	v.	PROPN
esrj-105296	439	5	,	,	PUNCT
esrj-105296	439	6	&	&	CCONJ
esrj-105296	439	7	hinton	hinton	PROPN
esrj-105296	439	8	,	,	PUNCT
esrj-105296	439	9	g.	g.	PROPN
esrj-105296	439	10	e.	e.	PROPN
esrj-105296	439	11	(	(	PUNCT
esrj-105296	439	12	2010	2010	NUM
esrj-105296	439	13	)	)	PUNCT
esrj-105296	439	14	.	.	PUNCT
esrj-105296	440	1	rectified	rectify	VERB
esrj-105296	440	2	linear	linear	PROPN
esrj-105296	440	3	units	unit	NOUN
esrj-105296	440	4	improve	improve	VERB
esrj-105296	440	5	restricted	restrict	VERB
esrj-105296	440	6	boltzmann	boltzmann	PROPN
esrj-105296	440	7	machines	machine	NOUN
esrj-105296	440	8	.	.	PUNCT
esrj-105296	441	1	the	the	DET
esrj-105296	441	2	27th	27th	ADJ
esrj-105296	441	3	international	international	ADJ
esrj-105296	441	4	conference	conference	NOUN
esrj-105296	441	5	on	on	ADP
esrj-105296	441	6	machine	machine	NOUN
esrj-105296	441	7	learning	learning	NOUN
esrj-105296	441	8	(	(	PUNCT
esrj-105296	441	9	icml	icml	PROPN
esrj-105296	441	10	2010	2010	NUM
esrj-105296	441	11	)	)	PUNCT
esrj-105296	441	12	,	,	PUNCT
esrj-105296	441	13	haifa	haifa	PROPN
esrj-105296	441	14	,	,	PUNCT
esrj-105296	441	15	israel	israel	PROPN
esrj-105296	441	16	.	.	PUNCT
esrj-105296	442	1	pal	pal	PROPN
esrj-105296	442	2	,	,	PUNCT
esrj-105296	442	3	m.	m.	NOUN
esrj-105296	442	4	(	(	PUNCT
esrj-105296	442	5	2005	2005	NUM
esrj-105296	442	6	)	)	PUNCT
esrj-105296	442	7	.	.	PUNCT
esrj-105296	443	1	random	random	ADJ
esrj-105296	443	2	forest	forest	NOUN
esrj-105296	443	3	classifier	classifier	NOUN
esrj-105296	443	4	for	for	ADP
esrj-105296	443	5	remote	remote	ADJ
esrj-105296	443	6	sensing	sense	VERB
esrj-105296	443	7	classification	classification	NOUN
esrj-105296	443	8	.	.	PUNCT
esrj-105296	444	1	international	international	ADJ
esrj-105296	444	2	journal	journal	NOUN
esrj-105296	444	3	of	of	ADP
esrj-105296	444	4	remote	remote	ADJ
esrj-105296	444	5	sensing	sensing	NOUN
esrj-105296	444	6	,	,	PUNCT
esrj-105296	444	7	26(1	26(1	NUM
esrj-105296	444	8	)	)	PUNCT
esrj-105296	444	9	,	,	PUNCT
esrj-105296	444	10	217	217	NUM
esrj-105296	444	11	-	-	SYM
esrj-105296	444	12	222	222	NUM
esrj-105296	444	13	.	.	PUNCT
esrj-105296	445	1	https://doi.org/10	https://doi.org/10	PROPN
esrj-105296	445	2	.	.	PUNCT
esrj-105296	446	1	1080/01431160412331269698	1080/01431160412331269698	NUM
esrj-105296	446	2	pal	pal	NOUN
esrj-105296	446	3	,	,	PUNCT
esrj-105296	446	4	m.	m.	NOUN
esrj-105296	446	5	,	,	PUNCT
esrj-105296	446	6	&	&	CCONJ
esrj-105296	446	7	mather	mather	PROPN
esrj-105296	446	8	,	,	PUNCT
esrj-105296	446	9	p.	p.	NOUN
esrj-105296	446	10	(	(	PUNCT
esrj-105296	446	11	2005	2005	NUM
esrj-105296	446	12	)	)	PUNCT
esrj-105296	446	13	.	.	PUNCT
esrj-105296	447	1	support	support	NOUN
esrj-105296	447	2	vector	vector	NOUN
esrj-105296	447	3	machines	machine	NOUN
esrj-105296	447	4	for	for	ADP
esrj-105296	447	5	classification	classification	NOUN
esrj-105296	447	6	in	in	ADP
esrj-105296	447	7	remote	remote	ADJ
esrj-105296	447	8	sensing	sensing	NOUN
esrj-105296	447	9	.	.	PUNCT
esrj-105296	448	1	international	international	ADJ
esrj-105296	448	2	journal	journal	NOUN
esrj-105296	448	3	of	of	ADP
esrj-105296	448	4	remote	remote	ADJ
esrj-105296	448	5	sensing	sensing	NOUN
esrj-105296	448	6	,	,	PUNCT
esrj-105296	448	7	26(5	26(5	NUM
esrj-105296	448	8	)	)	PUNCT
esrj-105296	448	9	,	,	PUNCT
esrj-105296	448	10	1007	1007	NUM
esrj-105296	448	11	-	-	SYM
esrj-105296	448	12	1011	1011	NUM
esrj-105296	448	13	.	.	PUNCT
esrj-105296	449	1	https://doi.org/10.1080/01431160512331314083	https://doi.org/10.1080/01431160512331314083	PROPN
esrj-105296	449	2	park	park	PROPN
esrj-105296	449	3	,	,	PUNCT
esrj-105296	449	4	b.	b.	PROPN
esrj-105296	449	5	,	,	PUNCT
esrj-105296	449	6	&	&	CCONJ
esrj-105296	449	7	lu	lu	PROPN
esrj-105296	449	8	,	,	PUNCT
esrj-105296	449	9	r.	r.	PROPN
esrj-105296	449	10	(	(	PUNCT
esrj-105296	449	11	2015	2015	NUM
esrj-105296	449	12	)	)	PUNCT
esrj-105296	449	13	.	.	PUNCT
esrj-105296	450	1	hyperspectral	hyperspectral	ADJ
esrj-105296	450	2	imaging	imaging	NOUN
esrj-105296	450	3	technology	technology	NOUN
esrj-105296	450	4	in	in	ADP
esrj-105296	450	5	food	food	NOUN
esrj-105296	450	6	and	and	CCONJ
esrj-105296	450	7	agriculture	agriculture	NOUN
esrj-105296	450	8	.	.	PUNCT
esrj-105296	451	1	springer	springer	NOUN
esrj-105296	451	2	.	.	PUNCT
esrj-105296	452	1	https://doi.org/10.1007/978-1-4939-2836-1	https://doi.org/10.1007/978-1-4939-2836-1	PROPN
esrj-105296	452	2	pedregosa	pedregosa	PROPN
esrj-105296	452	3	,	,	PUNCT
esrj-105296	452	4	f.	f.	PROPN
esrj-105296	452	5	,	,	PUNCT
esrj-105296	452	6	varoquaux	varoquaux	PROPN
esrj-105296	452	7	,	,	PUNCT
esrj-105296	452	8	g.	g.	PROPN
esrj-105296	452	9	,	,	PUNCT
esrj-105296	452	10	gramfort	gramfort	NOUN
esrj-105296	452	11	,	,	PUNCT
esrj-105296	452	12	a.	a.	PROPN
esrj-105296	452	13	,	,	PUNCT
esrj-105296	452	14	michel	michel	PROPN
esrj-105296	452	15	,	,	PUNCT
esrj-105296	452	16	v.	v.	PROPN
esrj-105296	452	17	,	,	PUNCT
esrj-105296	452	18	thirion	thirion	NOUN
esrj-105296	452	19	,	,	PUNCT
esrj-105296	452	20	b.	b.	PROPN
esrj-105296	452	21	,	,	PUNCT
esrj-105296	452	22	grisel	grisel	PROPN
esrj-105296	452	23	,	,	PUNCT
esrj-105296	452	24	o.	o.	PROPN
esrj-105296	452	25	,	,	PUNCT
esrj-105296	452	26	blondel	blondel	NOUN
esrj-105296	452	27	,	,	PUNCT
esrj-105296	452	28	m.	m.	NOUN
esrj-105296	452	29	,	,	PUNCT
esrj-105296	452	30	prettenhofer	prettenhofer	NOUN
esrj-105296	452	31	,	,	PUNCT
esrj-105296	452	32	p.	p.	PROPN
esrj-105296	452	33	,	,	PUNCT
esrj-105296	452	34	weiss	weiss	PROPN
esrj-105296	452	35	,	,	PUNCT
esrj-105296	452	36	r.	r.	PROPN
esrj-105296	452	37	,	,	PUNCT
esrj-105296	452	38	&	&	CCONJ
esrj-105296	452	39	dubourg	dubourg	PROPN
esrj-105296	452	40	,	,	PUNCT
esrj-105296	452	41	v.	v.	PROPN
esrj-105296	452	42	(	(	PUNCT
esrj-105296	452	43	2011	2011	NUM
esrj-105296	452	44	)	)	PUNCT
esrj-105296	452	45	.	.	PUNCT
esrj-105296	453	1	scikit	scikit	NOUN
esrj-105296	453	2	-	-	PUNCT
esrj-105296	453	3	learn	learn	VERB
esrj-105296	453	4	:	:	PUNCT
esrj-105296	453	5	machine	machine	NOUN
esrj-105296	453	6	learning	learning	NOUN
esrj-105296	453	7	in	in	ADP
esrj-105296	453	8	python	python	PROPN
esrj-105296	453	9	.	.	PUNCT
esrj-105296	454	1	the	the	DET
esrj-105296	454	2	journal	journal	NOUN
esrj-105296	454	3	of	of	ADP
esrj-105296	454	4	machine	machine	NOUN
esrj-105296	454	5	learning	learn	VERB
esrj-105296	454	6	research	research	NOUN
esrj-105296	454	7	,	,	PUNCT
esrj-105296	454	8	12	12	NUM
esrj-105296	454	9	,	,	PUNCT
esrj-105296	454	10	2825	2825	NUM
esrj-105296	454	11	-	-	SYM
esrj-105296	454	12	2830	2830	NUM
esrj-105296	454	13	.	.	PUNCT
esrj-105296	455	1	https://doi.org/10.48550/arxiv.1201.0490	https://doi.org/10.48550/arxiv.1201.0490	PROPN
esrj-105296	455	2	rodriguez	rodriguez	NOUN
esrj-105296	455	3	-	-	PUNCT
esrj-105296	455	4	galiano	galiano	PROPN
esrj-105296	455	5	,	,	PUNCT
esrj-105296	455	6	v.	v.	PROPN
esrj-105296	455	7	f.	f.	PROPN
esrj-105296	455	8	,	,	PUNCT
esrj-105296	455	9	ghimire	ghimire	PROPN
esrj-105296	455	10	,	,	PUNCT
esrj-105296	455	11	b.	b.	PROPN
esrj-105296	455	12	,	,	PUNCT
esrj-105296	455	13	rogan	rogan	PROPN
esrj-105296	455	14	,	,	PUNCT
esrj-105296	455	15	j.	j.	PROPN
esrj-105296	455	16	,	,	PUNCT
esrj-105296	455	17	chica	chica	PROPN
esrj-105296	455	18	-	-	PUNCT
esrj-105296	455	19	olmo	olmo	PROPN
esrj-105296	455	20	,	,	PUNCT
esrj-105296	455	21	m.	m.	NOUN
esrj-105296	455	22	,	,	PUNCT
esrj-105296	455	23	&	&	CCONJ
esrj-105296	455	24	rigol	rigol	PROPN
esrj-105296	455	25	-	-	PUNCT
esrj-105296	455	26	sanchez	sanchez	PROPN
esrj-105296	455	27	,	,	PUNCT
esrj-105296	455	28	j.	j.	PROPN
esrj-105296	455	29	p.	p.	PROPN
esrj-105296	455	30	(	(	PUNCT
esrj-105296	455	31	2012	2012	NUM
esrj-105296	455	32	)	)	PUNCT
esrj-105296	455	33	.	.	PUNCT
esrj-105296	456	1	an	an	DET
esrj-105296	456	2	assessment	assessment	NOUN
esrj-105296	456	3	of	of	ADP
esrj-105296	456	4	the	the	DET
esrj-105296	456	5	effectiveness	effectiveness	NOUN
esrj-105296	456	6	of	of	ADP
esrj-105296	456	7	a	a	DET
esrj-105296	456	8	random	random	ADJ
esrj-105296	456	9	forest	forest	NOUN
esrj-105296	456	10	classifier	classifier	NOUN
esrj-105296	456	11	for	for	ADP
esrj-105296	456	12	land	land	NOUN
esrj-105296	456	13	-	-	PUNCT
esrj-105296	456	14	cover	cover	NOUN
esrj-105296	456	15	classification	classification	NOUN
esrj-105296	456	16	.	.	PUNCT
esrj-105296	457	1	isprs	isprs	PROPN
esrj-105296	457	2	journal	journal	PROPN
esrj-105296	457	3	of	of	ADP
esrj-105296	457	4	photogrammetry	photogrammetry	NOUN
esrj-105296	457	5	and	and	CCONJ
esrj-105296	457	6	remote	remote	ADJ
esrj-105296	457	7	sensing	sensing	NOUN
esrj-105296	457	8	,	,	PUNCT
esrj-105296	457	9	67	67	NUM
esrj-105296	457	10	,	,	PUNCT
esrj-105296	457	11	93	93	NUM
esrj-105296	457	12	-	-	SYM
esrj-105296	457	13	104	104	NUM
esrj-105296	457	14	.	.	PUNCT
esrj-105296	458	1	https://doi.org/10.1016/j	https://doi.org/10.1016/j	NOUN
esrj-105296	458	2	.	.	PUNCT
esrj-105296	459	1	isprsjprs.2011.11.002	isprsjprs.2011.11.002	PUNCT
esrj-105296	459	2	roy	roy	PROPN
esrj-105296	459	3	,	,	PUNCT
esrj-105296	459	4	s.	s.	PROPN
esrj-105296	459	5	k.	k.	PROPN
esrj-105296	459	6	,	,	PUNCT
esrj-105296	459	7	krishna	krishna	PROPN
esrj-105296	459	8	,	,	PUNCT
esrj-105296	459	9	g.	g.	PROPN
esrj-105296	459	10	,	,	PUNCT
esrj-105296	459	11	dubey	dubey	PROPN
esrj-105296	459	12	,	,	PUNCT
esrj-105296	459	13	s.	s.	PROPN
esrj-105296	459	14	r.	r.	PROPN
esrj-105296	459	15	,	,	PUNCT
esrj-105296	459	16	&	&	CCONJ
esrj-105296	459	17	chaudhuri	chaudhuri	PROPN
esrj-105296	459	18	,	,	PUNCT
esrj-105296	459	19	b.	b.	PROPN
esrj-105296	459	20	b.	b.	PROPN
esrj-105296	459	21	(	(	PUNCT
esrj-105296	459	22	2019	2019	NUM
esrj-105296	459	23	)	)	PUNCT
esrj-105296	459	24	.	.	PUNCT
esrj-105296	460	1	hybridsn	hybridsn	NOUN
esrj-105296	460	2	:	:	PUNCT
esrj-105296	460	3	exploring	explore	VERB
esrj-105296	460	4	3	3	NUM
esrj-105296	460	5	-	-	PUNCT
esrj-105296	460	6	d–2	d–2	NOUN
esrj-105296	460	7	-	-	PUNCT
esrj-105296	460	8	d	d	NOUN
esrj-105296	460	9	cnn	cnn	NOUN
esrj-105296	460	10	feature	feature	NOUN
esrj-105296	460	11	hierarchy	hierarchy	NOUN
esrj-105296	460	12	for	for	ADP
esrj-105296	460	13	hyperspectral	hyperspectral	ADJ
esrj-105296	460	14	image	image	NOUN
esrj-105296	460	15	classification	classification	NOUN
esrj-105296	460	16	.	.	PUNCT
esrj-105296	461	1	ieee	ieee	NOUN
esrj-105296	461	2	geoscience	geoscience	PROPN
esrj-105296	461	3	and	and	CCONJ
esrj-105296	461	4	remote	remote	ADJ
esrj-105296	461	5	sensing	sense	VERB
esrj-105296	461	6	letters	letter	NOUN
esrj-105296	461	7	,	,	PUNCT
esrj-105296	461	8	17(2	17(2	NUM
esrj-105296	461	9	)	)	PUNCT
esrj-105296	461	10	,	,	PUNCT
esrj-105296	461	11	277281	277281	NUM
esrj-105296	461	12	.	.	PUNCT
esrj-105296	462	1	https://doi.org/10.1109/lgrs.2019.2918719	https://doi.org/10.1109/lgrs.2019.2918719	PROPN
esrj-105296	462	2	sahin	sahin	PROPN
esrj-105296	462	3	,	,	PUNCT
esrj-105296	462	4	e.	e.	PROPN
esrj-105296	462	5	k.	k.	PROPN
esrj-105296	462	6	,	,	PUNCT
esrj-105296	462	7	colkesen	colkesen	PROPN
esrj-105296	462	8	,	,	PUNCT
esrj-105296	462	9	i.	i.	NOUN
esrj-105296	462	10	,	,	PUNCT
esrj-105296	462	11	&	&	CCONJ
esrj-105296	462	12	kavzoglu	kavzoglu	PROPN
esrj-105296	462	13	,	,	PUNCT
esrj-105296	462	14	t.	t.	PROPN
esrj-105296	462	15	(	(	PUNCT
esrj-105296	462	16	2020	2020	NUM
esrj-105296	462	17	)	)	PUNCT
esrj-105296	462	18	.	.	PUNCT
esrj-105296	463	1	a	a	DET
esrj-105296	463	2	comparative	comparative	ADJ
esrj-105296	463	3	assessment	assessment	NOUN
esrj-105296	463	4	of	of	ADP
esrj-105296	463	5	canonical	canonical	ADJ
esrj-105296	463	6	correlation	correlation	NOUN
esrj-105296	463	7	forest	forest	NOUN
esrj-105296	463	8	,	,	PUNCT
esrj-105296	463	9	random	random	ADJ
esrj-105296	463	10	forest	forest	NOUN
esrj-105296	463	11	,	,	PUNCT
esrj-105296	463	12	rotation	rotation	NOUN
esrj-105296	463	13	forest	forest	NOUN
esrj-105296	463	14	and	and	CCONJ
esrj-105296	463	15	logistic	logistic	ADJ
esrj-105296	463	16	regression	regression	NOUN
esrj-105296	463	17	methods	method	NOUN
esrj-105296	463	18	for	for	ADP
esrj-105296	463	19	landslide	landslide	NOUN
esrj-105296	463	20	susceptibility	susceptibility	NOUN
esrj-105296	463	21	mapping	mapping	NOUN
esrj-105296	463	22	.	.	PUNCT
esrj-105296	464	1	geocarto	geocarto	PROPN
esrj-105296	464	2	international	international	PROPN
esrj-105296	464	3	,	,	PUNCT
esrj-105296	464	4	35(4	35(4	NUM
esrj-105296	464	5	)	)	PUNCT
esrj-105296	464	6	,	,	PUNCT
esrj-105296	464	7	341	341	NUM
esrj-105296	464	8	-	-	SYM
esrj-105296	464	9	363	363	NUM
esrj-105296	464	10	.	.	PUNCT
esrj-105296	465	1	https://doi.org/10.1080/10106049.2018.1	https://doi.org/10.1080/10106049.2018.1	NOUN
esrj-105296	465	2	516248	516248	NUM
esrj-105296	465	3	seyrek	seyrek	NOUN
esrj-105296	465	4	,	,	PUNCT
esrj-105296	465	5	e.	e.	PROPN
esrj-105296	465	6	c.	c.	PROPN
esrj-105296	465	7	,	,	PUNCT
esrj-105296	465	8	&	&	CCONJ
esrj-105296	465	9	uysal	uysal	PROPN
esrj-105296	465	10	,	,	PUNCT
esrj-105296	465	11	m.	m.	NOUN
esrj-105296	465	12	(	(	PUNCT
esrj-105296	465	13	2024	2024	NUM
esrj-105296	465	14	)	)	PUNCT
esrj-105296	465	15	.	.	PUNCT
esrj-105296	466	1	a	a	DET
esrj-105296	466	2	comparative	comparative	ADJ
esrj-105296	466	3	analysis	analysis	NOUN
esrj-105296	466	4	of	of	ADP
esrj-105296	466	5	various	various	ADJ
esrj-105296	466	6	activation	activation	NOUN
esrj-105296	466	7	functions	function	NOUN
esrj-105296	466	8	and	and	CCONJ
esrj-105296	466	9	optimizers	optimizer	NOUN
esrj-105296	466	10	in	in	ADP
esrj-105296	466	11	a	a	DET
esrj-105296	466	12	convolutional	convolutional	ADJ
esrj-105296	466	13	neural	neural	ADJ
esrj-105296	466	14	network	network	NOUN
esrj-105296	466	15	for	for	ADP
esrj-105296	466	16	hyperspectral	hyperspectral	ADJ
esrj-105296	466	17	image	image	NOUN
esrj-105296	466	18	classification	classification	NOUN
esrj-105296	466	19	.	.	PUNCT
esrj-105296	467	1	multimedia	multimedia	NOUN
esrj-105296	467	2	tools	tool	NOUN
esrj-105296	467	3	and	and	CCONJ
esrj-105296	467	4	applications	application	NOUN
esrj-105296	467	5	,	,	PUNCT
esrj-105296	467	6	83(18	83(18	NUM
esrj-105296	467	7	)	)	PUNCT
esrj-105296	467	8	,	,	PUNCT
esrj-105296	467	9	53785	53785	NUM
esrj-105296	467	10	-	-	SYM
esrj-105296	467	11	53816	53816	NUM
esrj-105296	467	12	.	.	PUNCT
esrj-105296	468	1	https://doi.org/10.1007/s11042-023-17546-5	https://doi.org/10.1007/s11042-023-17546-5	NUM
esrj-105296	468	2	sheykhmousa	sheykhmousa	NOUN
esrj-105296	468	3	,	,	PUNCT
esrj-105296	468	4	m.	m.	NOUN
esrj-105296	468	5	,	,	PUNCT
esrj-105296	468	6	mahdianpari	mahdianpari	PROPN
esrj-105296	468	7	,	,	PUNCT
esrj-105296	468	8	m.	m.	NOUN
esrj-105296	468	9	,	,	PUNCT
esrj-105296	468	10	ghanbari	ghanbari	PROPN
esrj-105296	468	11	,	,	PUNCT
esrj-105296	468	12	h.	h.	PROPN
esrj-105296	468	13	,	,	PUNCT
esrj-105296	468	14	mohammadimanesh	mohammadimanesh	PROPN
esrj-105296	468	15	,	,	PUNCT
esrj-105296	468	16	f.	f.	PROPN
esrj-105296	468	17	,	,	PUNCT
esrj-105296	468	18	ghamisi	ghamisi	PROPN
esrj-105296	468	19	,	,	PUNCT
esrj-105296	468	20	p.	p.	PROPN
esrj-105296	468	21	,	,	PUNCT
esrj-105296	468	22	&	&	CCONJ
esrj-105296	468	23	homayouni	homayouni	PROPN
esrj-105296	468	24	,	,	PUNCT
esrj-105296	468	25	s.	s.	PROPN
esrj-105296	468	26	(	(	PUNCT
esrj-105296	468	27	2020	2020	NUM
esrj-105296	468	28	)	)	PUNCT
esrj-105296	468	29	.	.	PUNCT
esrj-105296	469	1	support	support	NOUN
esrj-105296	469	2	vector	vector	NOUN
esrj-105296	469	3	machine	machine	NOUN
esrj-105296	469	4	versus	versus	ADP
esrj-105296	469	5	random	random	ADJ
esrj-105296	469	6	forest	forest	NOUN
esrj-105296	469	7	for	for	ADP
esrj-105296	469	8	remote	remote	ADJ
esrj-105296	469	9	sensing	sense	VERB
esrj-105296	469	10	image	image	NOUN
esrj-105296	469	11	classification	classification	NOUN
esrj-105296	469	12	:	:	PUNCT
esrj-105296	469	13	a	a	DET
esrj-105296	469	14	meta	meta	ADJ
esrj-105296	469	15	-	-	PUNCT
esrj-105296	469	16	analysis	analysis	NOUN
esrj-105296	469	17	and	and	CCONJ
esrj-105296	469	18	systematic	systematic	ADJ
esrj-105296	469	19	review	review	NOUN
esrj-105296	469	20	.	.	PUNCT
esrj-105296	470	1	ieee	ieee	PROPN
esrj-105296	470	2	journal	journal	PROPN
esrj-105296	470	3	of	of	ADP
esrj-105296	470	4	selected	select	VERB
esrj-105296	470	5	topics	topic	NOUN
esrj-105296	470	6	in	in	ADP
esrj-105296	470	7	applied	apply	VERB
esrj-105296	470	8	earth	earth	NOUN
esrj-105296	470	9	observations	observation	NOUN
esrj-105296	470	10	and	and	CCONJ
esrj-105296	470	11	remote	remote	ADJ
esrj-105296	470	12	sensing	sensing	NOUN
esrj-105296	470	13	,	,	PUNCT
esrj-105296	470	14	13	13	NUM
esrj-105296	470	15	,	,	PUNCT
esrj-105296	470	16	6308	6308	NUM
esrj-105296	470	17	-	-	SYM
esrj-105296	470	18	6325	6325	NUM
esrj-105296	470	19	.	.	PUNCT
esrj-105296	471	1	https://doi	https://doi	PROPN
esrj-105296	471	2	.	.	PUNCT
esrj-105296	471	3	org/10.1109	org/10.1109	PROPN
esrj-105296	471	4	/	/	SYM
esrj-105296	471	5	jstars.2020.3026724	jstars.2020.3026724	PROPN
esrj-105296	471	6	si	si	X
esrj-105296	471	7	,	,	PUNCT
esrj-105296	471	8	y.	y.	PROPN
esrj-105296	471	9	,	,	PUNCT
esrj-105296	471	10	gong	gong	PROPN
esrj-105296	471	11	,	,	PUNCT
esrj-105296	471	12	d.	d.	PROPN
esrj-105296	471	13	,	,	PUNCT
esrj-105296	471	14	guo	guo	PROPN
esrj-105296	471	15	,	,	PUNCT
esrj-105296	471	16	y.	y.	PROPN
esrj-105296	471	17	,	,	PUNCT
esrj-105296	471	18	zhu	zhu	PROPN
esrj-105296	471	19	,	,	PUNCT
esrj-105296	471	20	x.	x.	PROPN
esrj-105296	471	21	,	,	PUNCT
esrj-105296	471	22	huang	huang	PROPN
esrj-105296	471	23	,	,	PUNCT
esrj-105296	471	24	q.	q.	PROPN
esrj-105296	471	25	,	,	PUNCT
esrj-105296	471	26	evans	evans	PROPN
esrj-105296	471	27	,	,	PUNCT
esrj-105296	471	28	j.	j.	PROPN
esrj-105296	471	29	,	,	PUNCT
esrj-105296	471	30	he	he	PRON
esrj-105296	471	31	,	,	PUNCT
esrj-105296	471	32	s.	s.	PROPN
esrj-105296	471	33	,	,	PUNCT
esrj-105296	471	34	&	&	CCONJ
esrj-105296	471	35	sun	sun	PROPN
esrj-105296	471	36	,	,	PUNCT
esrj-105296	471	37	y.	y.	PROPN
esrj-105296	471	38	(	(	PUNCT
esrj-105296	471	39	2021	2021	NUM
esrj-105296	471	40	)	)	PUNCT
esrj-105296	471	41	.	.	PUNCT
esrj-105296	472	1	an	an	DET
esrj-105296	472	2	advanced	advanced	ADJ
esrj-105296	472	3	spectral	spectral	ADJ
esrj-105296	472	4	–	–	PUNCT
esrj-105296	472	5	spatial	spatial	ADJ
esrj-105296	472	6	classification	classification	NOUN
esrj-105296	472	7	framework	framework	NOUN
esrj-105296	472	8	for	for	ADP
esrj-105296	472	9	hyperspectral	hyperspectral	ADJ
esrj-105296	472	10	imagery	imagery	NOUN
esrj-105296	472	11	based	base	VERB
esrj-105296	472	12	on	on	ADP
esrj-105296	472	13	deeplab	deeplab	PROPN
esrj-105296	472	14	v3	v3	PROPN
esrj-105296	472	15	+	+	PROPN
esrj-105296	472	16	.	.	PROPN
esrj-105296	472	17	applied	apply	VERB
esrj-105296	472	18	sciences	science	NOUN
esrj-105296	472	19	,	,	PUNCT
esrj-105296	472	20	11(12	11(12	NUM
esrj-105296	472	21	)	)	PUNCT
esrj-105296	472	22	,	,	PUNCT
esrj-105296	472	23	5703	5703	NUM
esrj-105296	472	24	.	.	PUNCT
esrj-105296	473	1	https://	https://	PROPN
esrj-105296	473	2	doi.org/10.3390/app11125703	doi.org/10.3390/app11125703	PROPN
esrj-105296	473	3	stuart	stuart	PROPN
esrj-105296	473	4	,	,	PUNCT
esrj-105296	473	5	m.	m.	PROPN
esrj-105296	473	6	b.	b.	PROPN
esrj-105296	473	7	,	,	PUNCT
esrj-105296	473	8	mcgonigle	mcgonigle	PROPN
esrj-105296	473	9	,	,	PUNCT
esrj-105296	473	10	a.	a.	PROPN
esrj-105296	473	11	j.	j.	PROPN
esrj-105296	473	12	,	,	PUNCT
esrj-105296	473	13	&	&	CCONJ
esrj-105296	473	14	willmott	willmott	PROPN
esrj-105296	473	15	,	,	PUNCT
esrj-105296	473	16	j.	j.	PROPN
esrj-105296	473	17	r.	r.	PROPN
esrj-105296	473	18	(	(	PUNCT
esrj-105296	473	19	2019	2019	NUM
esrj-105296	473	20	)	)	PUNCT
esrj-105296	473	21	.	.	PUNCT
esrj-105296	474	1	hyperspectral	hyperspectral	ADJ
esrj-105296	474	2	imaging	imaging	NOUN
esrj-105296	474	3	in	in	ADP
esrj-105296	474	4	environmental	environmental	ADJ
esrj-105296	474	5	monitoring	monitoring	NOUN
esrj-105296	474	6	:	:	PUNCT
esrj-105296	474	7	a	a	DET
esrj-105296	474	8	review	review	NOUN
esrj-105296	474	9	of	of	ADP
esrj-105296	474	10	recent	recent	ADJ
esrj-105296	474	11	developments	development	NOUN
esrj-105296	474	12	and	and	CCONJ
esrj-105296	474	13	technological	technological	ADJ
esrj-105296	474	14	advances	advance	NOUN
esrj-105296	474	15	in	in	ADP
esrj-105296	474	16	compact	compact	ADJ
esrj-105296	474	17	field	field	NOUN
esrj-105296	474	18	deployable	deployable	ADJ
esrj-105296	474	19	systems	system	NOUN
esrj-105296	474	20	.	.	PUNCT
esrj-105296	475	1	sensors	sensor	NOUN
esrj-105296	475	2	,	,	PUNCT
esrj-105296	475	3	19(14	19(14	NOUN
esrj-105296	475	4	)	)	PUNCT
esrj-105296	475	5	,	,	PUNCT
esrj-105296	475	6	3071	3071	NUM
esrj-105296	475	7	.	.	PUNCT
esrj-105296	476	1	https://doi.org/10.3390/s19143071	https://doi.org/10.3390/s19143071	PROPN
esrj-105296	476	2	teke	teke	PROPN
esrj-105296	476	3	,	,	PUNCT
esrj-105296	476	4	m.	m.	NOUN
esrj-105296	476	5	,	,	PUNCT
esrj-105296	476	6	deveci	deveci	PROPN
esrj-105296	476	7	,	,	PUNCT
esrj-105296	476	8	h.	h.	PROPN
esrj-105296	476	9	s.	s.	PROPN
esrj-105296	476	10	,	,	PUNCT
esrj-105296	476	11	haliloğlu	haliloğlu	NOUN
esrj-105296	476	12	,	,	PUNCT
esrj-105296	476	13	o.	o.	PROPN
esrj-105296	476	14	,	,	PUNCT
esrj-105296	476	15	gürbüz	gürbüz	PROPN
esrj-105296	476	16	,	,	PUNCT
esrj-105296	476	17	s.	s.	PROPN
esrj-105296	476	18	z.	z.	PROPN
esrj-105296	476	19	,	,	PUNCT
esrj-105296	476	20	&	&	CCONJ
esrj-105296	476	21	sakarya	sakarya	NOUN
esrj-105296	476	22	,	,	PUNCT
esrj-105296	476	23	u.	u.	PROPN
esrj-105296	476	24	(	(	PUNCT
esrj-105296	476	25	2013	2013	NUM
esrj-105296	476	26	)	)	PUNCT
esrj-105296	476	27	.	.	PUNCT
esrj-105296	477	1	a	a	DET
esrj-105296	477	2	short	short	ADJ
esrj-105296	477	3	survey	survey	NOUN
esrj-105296	477	4	of	of	ADP
esrj-105296	477	5	hyperspectral	hyperspectral	ADJ
esrj-105296	477	6	remote	remote	ADJ
esrj-105296	477	7	sensing	sense	VERB
esrj-105296	477	8	applications	application	NOUN
esrj-105296	477	9	in	in	ADP
esrj-105296	477	10	agriculture	agriculture	NOUN
esrj-105296	477	11	.	.	PUNCT
esrj-105296	478	1	6th	6th	ADJ
esrj-105296	478	2	international	international	ADJ
esrj-105296	478	3	conference	conference	NOUN
esrj-105296	478	4	on	on	ADP
esrj-105296	478	5	recent	recent	ADJ
esrj-105296	478	6	advances	advance	NOUN
esrj-105296	478	7	in	in	ADP
esrj-105296	478	8	space	space	NOUN
esrj-105296	478	9	technologies	technology	NOUN
esrj-105296	478	10	(	(	PUNCT
esrj-105296	478	11	rast	rast	PROPN
esrj-105296	478	12	)	)	PUNCT
esrj-105296	478	13	.	.	PUNCT
esrj-105296	479	1	https://doi.org/10.1109/rast.2013.6581194	https://doi.org/10.1109/rast.2013.6581194	PROPN
esrj-105296	479	2	ustuner	ustuner	NOUN
esrj-105296	479	3	,	,	PUNCT
esrj-105296	479	4	m.	m.	NOUN
esrj-105296	479	5	(	(	PUNCT
esrj-105296	479	6	2024	2024	NUM
esrj-105296	479	7	)	)	PUNCT
esrj-105296	479	8	.	.	PUNCT
esrj-105296	480	1	randomized	randomize	VERB
esrj-105296	480	2	principal	principal	ADJ
esrj-105296	480	3	component	component	NOUN
esrj-105296	480	4	analysis	analysis	NOUN
esrj-105296	480	5	for	for	ADP
esrj-105296	480	6	hyperspectral	hyperspectral	ADJ
esrj-105296	480	7	image	image	NOUN
esrj-105296	480	8	classification	classification	NOUN
esrj-105296	480	9	.	.	PUNCT
esrj-105296	481	1	arxiv	arxiv	PROPN
esrj-105296	481	2	preprint	preprint	NOUN
esrj-105296	481	3	arxiv:2403.09117	arxiv:2403.09117	NOUN
esrj-105296	481	4	.	.	PUNCT
esrj-105296	482	1	https://doi	https://doi	PROPN
esrj-105296	482	2	.	.	PUNCT
esrj-105296	482	3	org/10.48550	org/10.48550	PROPN
esrj-105296	482	4	/	/	SYM
esrj-105296	482	5	arxiv.2403.09117	arxiv.2403.09117	PROPN
esrj-105296	482	6	van	van	PROPN
esrj-105296	482	7	der	der	PROPN
esrj-105296	482	8	meer	meer	PROPN
esrj-105296	482	9	,	,	PUNCT
esrj-105296	482	10	f.	f.	PROPN
esrj-105296	482	11	d.	d.	PROPN
esrj-105296	482	12	,	,	PUNCT
esrj-105296	482	13	van	van	PROPN
esrj-105296	482	14	der	der	PROPN
esrj-105296	482	15	werff	werff	NOUN
esrj-105296	482	16	,	,	PUNCT
esrj-105296	482	17	h.	h.	PROPN
esrj-105296	482	18	m.	m.	PROPN
esrj-105296	482	19	,	,	PUNCT
esrj-105296	482	20	van	van	PROPN
esrj-105296	482	21	ruitenbeek	ruitenbeek	NOUN
esrj-105296	482	22	,	,	PUNCT
esrj-105296	482	23	f.	f.	PROPN
esrj-105296	482	24	j.	j.	PROPN
esrj-105296	482	25	,	,	PUNCT
esrj-105296	482	26	hecker	hecker	PROPN
esrj-105296	482	27	,	,	PUNCT
esrj-105296	482	28	c.	c.	PROPN
esrj-105296	482	29	a.	a.	PROPN
esrj-105296	482	30	,	,	PUNCT
esrj-105296	482	31	bakker	bakker	PROPN
esrj-105296	482	32	,	,	PUNCT
esrj-105296	482	33	w.	w.	PROPN
esrj-105296	482	34	h.	h.	PROPN
esrj-105296	482	35	,	,	PUNCT
esrj-105296	482	36	noomen	nooman	NOUN
esrj-105296	482	37	,	,	PUNCT
esrj-105296	482	38	m.	m.	PROPN
esrj-105296	482	39	f.	f.	PROPN
esrj-105296	482	40	,	,	PUNCT
esrj-105296	482	41	van	van	PROPN
esrj-105296	482	42	der	der	NOUN
esrj-105296	482	43	meijde	meijde	NOUN
esrj-105296	482	44	,	,	PUNCT
esrj-105296	482	45	m.	m.	NOUN
esrj-105296	482	46	,	,	PUNCT
esrj-105296	482	47	carranza	carranza	PROPN
esrj-105296	482	48	,	,	PUNCT
esrj-105296	482	49	e.	e.	PROPN
esrj-105296	482	50	j.	j.	PROPN
esrj-105296	482	51	m.	m.	PROPN
esrj-105296	482	52	,	,	PUNCT
esrj-105296	482	53	de	de	PROPN
esrj-105296	482	54	smeth	smeth	PROPN
esrj-105296	482	55	,	,	PUNCT
esrj-105296	482	56	j.	j.	PROPN
esrj-105296	482	57	b.	b.	PROPN
esrj-105296	482	58	,	,	PUNCT
esrj-105296	482	59	&	&	CCONJ
esrj-105296	482	60	woldai	woldai	PROPN
esrj-105296	482	61	,	,	PUNCT
esrj-105296	482	62	t.	t.	PROPN
esrj-105296	482	63	(	(	PUNCT
esrj-105296	482	64	2012	2012	NUM
esrj-105296	482	65	)	)	PUNCT
esrj-105296	482	66	.	.	PUNCT
esrj-105296	483	1	multi	multi	ADJ
esrj-105296	483	2	-	-	ADJ
esrj-105296	483	3	and	and	CCONJ
esrj-105296	483	4	hyperspectral	hyperspectral	ADJ
esrj-105296	483	5	geologic	geologic	ADJ
esrj-105296	483	6	remote	remote	ADJ
esrj-105296	483	7	sensing	sensing	NOUN
esrj-105296	483	8	:	:	PUNCT
esrj-105296	483	9	a	a	DET
esrj-105296	483	10	review	review	NOUN
esrj-105296	483	11	.	.	PUNCT
esrj-105296	484	1	international	international	ADJ
esrj-105296	484	2	journal	journal	PROPN
esrj-105296	484	3	of	of	ADP
esrj-105296	484	4	applied	apply	VERB
esrj-105296	484	5	earth	earth	NOUN
esrj-105296	484	6	observation	observation	NOUN
esrj-105296	484	7	and	and	CCONJ
esrj-105296	484	8	geoinformation	geoinformation	NOUN
esrj-105296	484	9	,	,	PUNCT
esrj-105296	484	10	14(1	14(1	NUM
esrj-105296	484	11	)	)	PUNCT
esrj-105296	484	12	,	,	PUNCT
esrj-105296	484	13	112	112	NUM
esrj-105296	484	14	-	-	SYM
esrj-105296	484	15	128	128	NUM
esrj-105296	484	16	.	.	PUNCT
esrj-105296	485	1	https://doi.org/10.1016/j	https://doi.org/10.1016/j	NOUN
esrj-105296	485	2	.	.	PUNCT
esrj-105296	486	1	jag.2011.08.002	jag.2011.08.002	PROPN
esrj-105296	486	2	vani	vani	PROPN
esrj-105296	486	3	,	,	PUNCT
esrj-105296	486	4	s.	s.	PROPN
esrj-105296	486	5	,	,	PUNCT
esrj-105296	486	6	&	&	CCONJ
esrj-105296	486	7	rao	rao	PROPN
esrj-105296	486	8	,	,	PUNCT
esrj-105296	486	9	t.	t.	NOUN
esrj-105296	486	10	m.	m.	NOUN
esrj-105296	486	11	(	(	PUNCT
esrj-105296	486	12	2019	2019	NUM
esrj-105296	486	13	)	)	PUNCT
esrj-105296	486	14	.	.	PUNCT
esrj-105296	487	1	an	an	DET
esrj-105296	487	2	experimental	experimental	ADJ
esrj-105296	487	3	approach	approach	NOUN
esrj-105296	487	4	towards	towards	ADP
esrj-105296	487	5	the	the	DET
esrj-105296	487	6	performance	performance	NOUN
esrj-105296	487	7	assessment	assessment	NOUN
esrj-105296	487	8	of	of	ADP
esrj-105296	487	9	various	various	ADJ
esrj-105296	487	10	optimizers	optimizer	NOUN
esrj-105296	487	11	on	on	ADP
esrj-105296	487	12	convolutional	convolutional	ADJ
esrj-105296	487	13	neural	neural	ADJ
esrj-105296	487	14	network	network	NOUN
esrj-105296	487	15	.	.	PUNCT
esrj-105296	488	1	3rd	3rd	ADJ
esrj-105296	488	2	international	international	ADJ
esrj-105296	488	3	conference	conference	NOUN
esrj-105296	488	4	on	on	ADP
esrj-105296	488	5	trends	trend	NOUN
esrj-105296	488	6	in	in	ADP
esrj-105296	488	7	electronics	electronic	NOUN
esrj-105296	488	8	and	and	CCONJ
esrj-105296	488	9	informatics	informatic	NOUN
esrj-105296	488	10	(	(	PUNCT
esrj-105296	488	11	icoei	icoei	NOUN
esrj-105296	488	12	)	)	PUNCT
esrj-105296	488	13	.	.	PUNCT
esrj-105296	489	1	https://doi.org/10.1109/icoei.2019.8862686	https://doi.org/10.1109/icoei.2019.8862686	PROPN
esrj-105296	489	2	vapnik	vapnik	NOUN
esrj-105296	489	3	,	,	PUNCT
esrj-105296	489	4	v.	v.	PROPN
esrj-105296	489	5	(	(	PUNCT
esrj-105296	489	6	1995	1995	NUM
esrj-105296	489	7	)	)	PUNCT
esrj-105296	489	8	.	.	PUNCT
esrj-105296	490	1	the	the	DET
esrj-105296	490	2	nature	nature	NOUN
esrj-105296	490	3	of	of	ADP
esrj-105296	490	4	statistical	statistical	ADJ
esrj-105296	490	5	learning	learning	NOUN
esrj-105296	490	6	theory	theory	NOUN
esrj-105296	490	7	.	.	PUNCT
esrj-105296	491	1	springer	springer	PROPN
esrj-105296	491	2	verlag	verlag	PROPN
esrj-105296	491	3	.	.	PUNCT
esrj-105296	492	1	https://doi.org/10.1007/978-1-4757-3264-1	https://doi.org/10.1007/978-1-4757-3264-1	PROPN
esrj-105296	492	2	wang	wang	PROPN
esrj-105296	492	3	,	,	PUNCT
esrj-105296	492	4	y.	y.	PROPN
esrj-105296	492	5	,	,	PUNCT
esrj-105296	492	6	li	li	PROPN
esrj-105296	492	7	,	,	PUNCT
esrj-105296	492	8	y.	y.	PROPN
esrj-105296	492	9	,	,	PUNCT
esrj-105296	492	10	song	song	NOUN
esrj-105296	492	11	,	,	PUNCT
esrj-105296	492	12	y.	y.	PROPN
esrj-105296	492	13	,	,	PUNCT
esrj-105296	492	14	&	&	CCONJ
esrj-105296	492	15	rong	rong	PROPN
esrj-105296	492	16	,	,	PUNCT
esrj-105296	492	17	x.	x.	NOUN
esrj-105296	492	18	(	(	PUNCT
esrj-105296	492	19	2020	2020	NUM
esrj-105296	492	20	)	)	PUNCT
esrj-105296	492	21	.	.	PUNCT
esrj-105296	493	1	the	the	DET
esrj-105296	493	2	influence	influence	NOUN
esrj-105296	493	3	of	of	ADP
esrj-105296	493	4	the	the	DET
esrj-105296	493	5	activation	activation	NOUN
esrj-105296	493	6	function	function	NOUN
esrj-105296	493	7	in	in	ADP
esrj-105296	493	8	a	a	DET
esrj-105296	493	9	convolution	convolution	NOUN
esrj-105296	493	10	neural	neural	ADJ
esrj-105296	493	11	network	network	NOUN
esrj-105296	493	12	model	model	NOUN
esrj-105296	493	13	of	of	ADP
esrj-105296	493	14	facial	facial	ADJ
esrj-105296	493	15	expression	expression	NOUN
esrj-105296	493	16	recognition	recognition	NOUN
esrj-105296	493	17	.	.	PUNCT
esrj-105296	494	1	applied	apply	VERB
esrj-105296	494	2	sciences	science	NOUN
esrj-105296	494	3	,	,	PUNCT
esrj-105296	494	4	10(5	10(5	NUM
esrj-105296	494	5	)	)	PUNCT
esrj-105296	494	6	,	,	PUNCT
esrj-105296	494	7	1897	1897	NUM
esrj-105296	494	8	.	.	PUNCT
esrj-105296	495	1	https://doi.org/10.3390/	https://doi.org/10.3390/	PROPN
esrj-105296	495	2	app10051897	app10051897	VERB
esrj-105296	495	3	waske	waske	PROPN
esrj-105296	495	4	,	,	PUNCT
esrj-105296	495	5	b.	b.	PROPN
esrj-105296	495	6	,	,	PUNCT
esrj-105296	495	7	benediktsson	benediktsson	PROPN
esrj-105296	495	8	,	,	PUNCT
esrj-105296	495	9	j.	j.	PROPN
esrj-105296	495	10	a.	a.	PROPN
esrj-105296	495	11	,	,	PUNCT
esrj-105296	495	12	árnason	árnason	NOUN
esrj-105296	495	13	,	,	PUNCT
esrj-105296	495	14	k.	k.	PROPN
esrj-105296	495	15	,	,	PUNCT
esrj-105296	495	16	&	&	CCONJ
esrj-105296	495	17	sveinsson	sveinsson	PROPN
esrj-105296	495	18	,	,	PUNCT
esrj-105296	495	19	j.	j.	PROPN
esrj-105296	495	20	r.	r.	PROPN
esrj-105296	495	21	(	(	PUNCT
esrj-105296	495	22	2009	2009	NUM
esrj-105296	495	23	)	)	PUNCT
esrj-105296	495	24	.	.	PUNCT
esrj-105296	496	1	mapping	mapping	NOUN
esrj-105296	496	2	of	of	ADP
esrj-105296	496	3	hyperspectral	hyperspectral	ADJ
esrj-105296	496	4	aviris	aviris	NOUN
esrj-105296	496	5	data	datum	NOUN
esrj-105296	496	6	using	use	VERB
esrj-105296	496	7	machine	machine	NOUN
esrj-105296	496	8	-	-	PUNCT
esrj-105296	496	9	learning	learn	VERB
esrj-105296	496	10	algorithms	algorithm	NOUN
esrj-105296	496	11	.	.	PUNCT
esrj-105296	497	1	canadian	canadian	ADJ
esrj-105296	497	2	journal	journal	NOUN
esrj-105296	497	3	of	of	ADP
esrj-105296	497	4	remote	remote	ADJ
esrj-105296	497	5	sensing	sensing	NOUN
esrj-105296	497	6	,	,	PUNCT
esrj-105296	497	7	35(sup1	35(sup1	NUM
esrj-105296	497	8	)	)	PUNCT
esrj-105296	497	9	,	,	PUNCT
esrj-105296	497	10	s106	s106	PROPN
esrj-105296	497	11	-	-	PUNCT
esrj-105296	497	12	s116	s116	PROPN
esrj-105296	497	13	.	.	PUNCT
esrj-105296	498	1	https://doi	https://doi	PROPN
esrj-105296	498	2	.	.	PUNCT
esrj-105296	498	3	org/10.5589	org/10.5589	PROPN
esrj-105296	498	4	/	/	SYM
esrj-105296	498	5	m09	m09	NOUN
esrj-105296	498	6	-	-	PUNCT
esrj-105296	498	7	018	018	NUM
esrj-105296	498	8	https://doi.org/10.5281/zenodo.1222202	https://doi.org/10.5281/zenodo.1222202	PROPN
esrj-105296	498	9	https://doi.org/10.5281/zenodo.1222202	https://doi.org/10.5281/zenodo.1222202	PROPN
esrj-105296	498	10	https://doi.org/10.1016/j.jag.2009.06.002	https://doi.org/10.1016/j.jag.2009.06.002	NOUN
esrj-105296	498	11	https://doi.org/10.1016/j.jag.2009.06.002	https://doi.org/10.1016/j.jag.2009.06.002	NOUN
esrj-105296	498	12	https://doi.org/10.48550/arxiv.1412.6980	https://doi.org/10.48550/arxiv.1412.6980	PROPN
esrj-105296	498	13	https://doi.org/10.48550/arxiv.1412.6980	https://doi.org/10.48550/arxiv.1412.6980	PROPN
esrj-105296	498	14	https://doi.org/10.1145/3065386	https://doi.org/10.1145/3065386	NOUN
esrj-105296	498	15	https://doi.org/10.1109/icdse.2012.6282329	https://doi.org/10.1109/icdse.2012.6282329	PROPN
esrj-105296	498	16	https://doi.org/10.1038/nature14539	https://doi.org/10.1038/nature14539	NOUN
esrj-105296	498	17	https://doi.org/10.1109/5.726791	https://doi.org/10.1109/5.726791	VERB
esrj-105296	498	18	https://doi.org/10.1002/widm.1264	https://doi.org/10.1002/widm.1264	NOUN
esrj-105296	498	19	https://doi.org/10.1002/widm.1264	https://doi.org/10.1002/widm.1264	ADJ
esrj-105296	498	20	https://doi.org/10.3390/rs10020202	https://doi.org/10.3390/rs10020202	NOUN
esrj-105296	498	21	https://doi.org/10.3390/rs10020202	https://doi.org/10.3390/rs10020202	NOUN
esrj-105296	498	22	https://doi.org/10.1117/1.jbo.19.1.010901	https://doi.org/10.1117/1.jbo.19.1.010901	NOUN
esrj-105296	498	23	https://doi.org/10.1109/icalip.2018.8455251	https://doi.org/10.1109/icalip.2018.8455251	PROPN
esrj-105296	498	24	https://doi.org/10.1109/tgrs.2004.831865	https://doi.org/10.1109/tgrs.2004.831865	PROPN
esrj-105296	499	1	https://doi.org/10.1109/tgrs.2004.831865	https://doi.org/10.1109/tgrs.2004.831865	PROPN
esrj-105296	499	2	https://doi.org/10.3390/rs13163055	https://doi.org/10.3390/rs13163055	X
esrj-105296	499	3	https://doi.org/10.48550/arxiv.1908.08681	https://doi.org/10.48550/arxiv.1908.08681	PROPN
esrj-105296	499	4	https://doi.org/10.48550/arxiv.1908.08681	https://doi.org/10.48550/arxiv.1908.08681	PROPN
esrj-105296	500	1	https://doi.org/10.1016/j.isprsjprs.2010.11.001	https://doi.org/10.1016/j.isprsjprs.2010.11.001	NOUN
esrj-105296	501	1	https://doi.org/10.1080/01431160412331269698	https://doi.org/10.1080/01431160412331269698	ADJ
esrj-105296	501	2	https://doi.org/10.1080/01431160412331269698	https://doi.org/10.1080/01431160412331269698	NOUN
esrj-105296	502	1	https://doi.org/10.1080/01431160512331314083	https://doi.org/10.1080/01431160512331314083	X
esrj-105296	502	2	https://doi.org/10.1007/978-1-4939-2836-1	https://doi.org/10.1007/978-1-4939-2836-1	PROPN
esrj-105296	502	3	https://doi.org/10.48550/arxiv.1201.0490	https://doi.org/10.48550/arxiv.1201.0490	PROPN
esrj-105296	502	4	https://doi.org/10.1016/j.isprsjprs.2011.11.002	https://doi.org/10.1016/j.isprsjprs.2011.11.002	NOUN
esrj-105296	502	5	https://doi.org/10.1016/j.isprsjprs.2011.11.002	https://doi.org/10.1016/j.isprsjprs.2011.11.002	NOUN
esrj-105296	502	6	https://doi.org/10.1109/lgrs.2019.2918719	https://doi.org/10.1109/lgrs.2019.2918719	PROPN
esrj-105296	502	7	https://doi.org/10.1080/10106049.2018.1516248	https://doi.org/10.1080/10106049.2018.1516248	VERB
esrj-105296	502	8	https://doi.org/10.1080/10106049.2018.1516248	https://doi.org/10.1080/10106049.2018.1516248	PROPN
esrj-105296	503	1	https://doi.org/10.1007/s11042-023-17546-5	https://doi.org/10.1007/s11042-023-17546-5	NUM
esrj-105296	503	2	https://doi.org/10.1109/jstars.2020.3026724	https://doi.org/10.1109/jstars.2020.3026724	PROPN
esrj-105296	503	3	https://doi.org/10.1109/jstars.2020.3026724	https://doi.org/10.1109/jstars.2020.3026724	PROPN
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esrj-105296	509	6	wavelet	wavelet	NOUN
esrj-105296	509	7	active	active	ADJ
esrj-105296	509	8	media	medium	NOUN
esrj-105296	509	9	technology	technology	NOUN
esrj-105296	509	10	and	and	CCONJ
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esrj-105296	510	12	c.	c.	PROPN
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esrj-105296	510	20	j.	j.	PROPN
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esrj-105296	510	22	&	&	CCONJ
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esrj-105296	511	30	with	with	ADP
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esrj-105296	513	4	_	_	PUNCT
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