Hyperspectral Image Classification Based on Hierarchical Guidance Filtering and Nearest Regularized Subspace

Table 1. Experimental parameters setting
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Table 1. Experimental parameters setting
g r T λ
Indian Pines 0.01 2 8 0.05
Salinas 0.000 5 2 18 0.01
GRSS_DFC_2013 0.000 1 1 7 0.03

Table 2. Classification performance comparison (Indian Pines)

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Table 2. Classification performance comparison (Indian Pines)

Class	Train	Test	NRS^[10]	Gabor-NRS^[19]	JCR2^[12]	HiFi-We^[17]	EPF-G-g^[16]	HGF-NRS
Alfalfa	24	22	95.45	100.0	16.20	100.0	100.0	100.0
Corn-N	90	1338	78.68	90.15	95.83	93.39	94.22	97.93
Corn-M	80	750	76.57	93.42	99.44	93.60	96.66	95.05
Corn	68	169	47.16	95.48	95.83	100.0	78.97	98.83
Grass-P	71	412	90.87	97.54	99.50	98.78	97.53	100.0
Grass-T	74	656	97.62	98.18	97.57	99.69	99.24	99.70
Grass-P-M	14	14	100.0	100.0	100.0	92.85	100.0	100.0
Hay-W	70	408	99.03	100.0	99.51	100.0	100.0	100.0
Oats	10	10	81.82	100.0	100.0	100.0	100.0	100.0
Soybean-N	79	893	75.95	98.03	97.79	92.37	85.07	98.07
Soybean-M	109	2346	80.48	92.52	95.75	97.62	95.84	98.81
Soybean-C	69	524	83.16	94.14	98.53	98.09	94.45	99.05
Wheat	68	137	99.28	100.0	100.0	100.0	100.0	97.86
Woods	85	1180	95.63	100.0	100.0	99.06	98.62	100.0
Buildings-G-T-D	68	318	69.95	85.99	95.56	100.0	85.31	100.0
Stone-S-T	46	47	97.87	86.79	82.35	100.0	92.16	94.0
OA	-	-	82.85	94.58	96.14	96.87	94.57	98.63
AA	-	-	85.59	95.77	92.12	97.84	94.88	98.71
Kappa	-	-	80.27	93.74	95.55	96.39	93.75	98.43

Table 3. Classification performance comparison (Salinas)

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Table 3. Classification performance comparison (Salinas)

Class	Train	Test	NRS^[10]	Gabor-NRS^[19]	JCR2^[12]	HiFi-We^[17]	EPF-G-g^[16]	HGF-NRS
Brocoli-G-W-1	30	1979	99.95	100.0	100.0	99.24	100.0	100.0
Brocoli-G-W-2	30	3696	99.81	99.11	99.70	99.86	99.95	99.92
Fallow	30	1946	95.15	96.38	100.0	99.79	94.95	100.0
Fallow-R-P	30	1364	97.63	95.29	96.51	98.97	97.36	96.06
Fallow-S	30	2648	99.77	99.06	99.58	99.16	99.89	100.0
Stubble	30	3929	99.95	100.0	100.0	99.23	99.92	100.0
Celery	30	3549	99.30	99.66	100.0	99.04	100.0	99.94
Grapes-U	30	11241	81.48	94.50	92.30	82.93	90.56	99.94
Soil-V-D	30	6173	99.44	98.88	99.80	99.96	99.13	99.97
Corn-S-G-W	30	3248	90.82	95.79	97.99	89.28	91.44	99.16
Lettuce-R-4	30	1038	94.86	88.19	99.81	100.0	94.70	100.0
Lettuce-R-5	30	1897	98.91	99.95	100.0	100.0	100.0	100.0
Lettuce-R-6	30	886	99.66	99.44	100.0	98.87	100.0	100.0
Lettuce-R-7	30	1040	98.04	90.43	98.38	96.82	98.47	93.88
Vinyard-U	30	7238	58.04	79.50	80.98	88.31	77.03	95.98
Vinyard-V-T	30	1777	95.81	98.88	100.0	99.38	99.33	99.83
OA	-	-	88.11	94.48	95.36	93.86	93.67	99.13
AA	-	-	94.29	95.94	97.82	96.93	96.42	99.04
Kappa	-	-	86.81	93.87	94.83	93.17	92.96	99.03

Table 4. Classification performance comparison (GRSS_DFC_2013)

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Table 4. Classification performance comparison (GRSS_DFC_2013)

Class	Train	Test	NRS^[10]	Gabor-NRS^[19]	JCR2^[12]	HiFi-We^[17]	EPF-G-g^[16]	HGF-NRS
Healthy grass	99	1152	99.20	99.65	97.04	92.10	99.03	99.91
Stressed grass	95	1159	98.29	97.22	99.91	95.85	96.71	99.91
Synthetic grass	96	601	100.0	100.0	100.0	98.83	100.0	100.0
Trees	94	1150	97.87	98.83	99.74	88.08	99.48	100.0
Soil	93	1149	97.60	99.91	100.0	99.65	98.63	100.0
Water	91	234	100.0	87.64	89.65	97.86	100.0	100.0
Residential	98	1170	84.82	93.19	93.66	82.64	97.04	97.73
Commercial	95	1149	91.59	96.82	97.40	62.83	97.37	99.56
Road	96	1156	77.34	87.53	93.58	88.06	96.84	97.22
Highway	95	1132	95.96	98.57	99.55	96.11	97.66	100.0
Railway	90	1145	93.23	97.82	98.06	89.95	93.59	99.13
Parking lot 1	96	1137	93.05	99.27	100.0	86.10	94.59	100.0
Parking lot 2	92	377	57.96	93.70	94.68	93.37	87.61	99.47
Tennis court	90	338	99.40	100.0	94.41	100.0	95.48	100.0
Running track	93	567	100.0	96.76	100.0	100.0	100.0	100.0
OA	-	-	91.86	96.68	97.67	89.70	97.10	99.42
AA	-	-	92.42	96.46	97.18	91.43	96.94	99.53
Kappa	-	-	91.19	96.41	97.48	88.85	96.86	99.37

Table 5. Overall classification accuracy in varyingproportion of training samples(Indian Pines)

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Table 5. Overall classification accuracy in varyingproportion of training samples(Indian Pines)

	Proportion of training samples/%
	1	2	3	4	5
NRS^[10]	58.78(3.57)	66.71(1.93)	72.28(2.03)	74.46(1.10)	76.40(1.48)
Gabor-NRS^[19]	58.40(3.54)	71.50(2.11)	81.13(1.85)	84.75(2.73)	88.38(1.97)
JCR2^[12]	68.42(3.98)	79.45(3.79)	87.27(1.09)	89.88(1.34)	92.49(1.06)
HiFi-We^[17]	74.74(2.31)	85.66(1.98)	87.73(3.16)	91.72(1.11)	93.40(1.03)
EPF-G-g^[16]	64.34(3.69)	74.63(4.00)	83.02(2.96)	86.30(0.74)	88.54(1.42)
HGF-NRS	79.66(4.04)	89.47(1.60)	92.75(0.94)	94.19(1.29)	96.38(0.66)

Table 6. Overall classification accuracy in varying proportion of training samples(Salinas)

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Table 6. Overall classification accuracy in varying proportion of training samples(Salinas)

	Proportion of training samples/%
	0.4	0.5	0.6	0.7	0.8
NRS^[10]	85.33(1.94)	87.32(1.74)	87.18(1.02)	87.52(1.13)	87.77(1.23)
Gabor-NRS^[19]	90.20(0.97)	91.67(0.76)	92.42(1.33)	92.77(1.30)	93.61(0.56)
JCR2^[12]	91.30(1.54)	92.32(0.61)	92.90(1.34)	93.48(1.26)	94.22(1.36)
HiFi-We^[17]	91.78(1.43)	91.78(0.94)	92.40(1.32)	92.50(1.32)	93.19(1.14)
EPF-G-g^[16]	89.03(3.39)	89.23(1.77)	91.58(2.71)	91.77(2.70)	91.34(3.07)
HGF-NRS	96.28(0.79)	96.86(0.88)	98.40(0.51)	98.42(0.50)	98.93(0.43)

Table 7. Overall classification accuracy in varying proportion of training samples (GRSS_DFC_2013)

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Table 7. Overall classification accuracy in varying proportion of training samples (GRSS_DFC_2013)

	Proportion of training samples/%
	1	2	3	4	5
NRS^[10]	80.46(1.67)	86.24(1.23)	88.70(1.09)	90.22(0.56)	90.85(0.58)
Gabor-NRS^[19]	75.19(1.60)	84.76(1.35)	88.96(0.85)	91.21(0.98)	93.21(0.78)
JCR2^[12]	84.92(1.33)	91.15(0.95)	93.85(1.08)	95.41(0.94)	96.52(0.69)
HiFi-We^[17]	80.43(2.62)	85.22(1.71)	86.71(1.66)	88.08(1.03)	88.72(0.63)
EPF-G-g^[16]	78.08(2.75)	87.00(1.60)	90.87(2.08)	93.11(0.66)	93.74(0.61)
HGF-NRS	86.14(2.13)	93.00(1.66)	95.18(1.03)	96.71(0.74)	97.61(0.74)

Table 8. Computing time of algorithms (s)
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Table 8. Computing time of algorithms (s)
NRS^[10] Gabor-NRS^[19] JCR2^[12] HiFi-We^[17] EPF-G-g^[16] HGF-NRS
Indian Pines 48.3 33.10 49.5 589.5 7.9 53.4
Salinas 136.6 91.32 140.2 647.26 13.7 179.9
GRSS_DFC_2013 90.9 86.6 103.6 1 208.4 108.5 270.4

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Dong-dong XU, De-qiang CHENG, Liang-liang CHEN, Qi-qi KOU, Shou-feng TANG. Hyperspectral Image Classification Based on Hierarchical Guidance Filtering and Nearest Regularized Subspace[J]. Acta Photonica Sinica, 2020, 49(4): 0410004

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Paper Information

Category: Image Processing

Received: Dec. 3, 2019

Accepted: Feb. 14, 2020

Published Online: Apr. 24, 2020

The Author Email: CHENG De-qiang (chengdq@cumt.edu.cn)

DOI:10.3788/gzxb20204904.0410004

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Experimental parameters setting

Table 1. Experimental parameters setting

Table 2. Classification performance comparison (Indian Pines)

Table 2. Classification performance comparison (Indian Pines)

Table 3. Classification performance comparison (Salinas)

Table 3. Classification performance comparison (Salinas)

Table 4. Classification performance comparison (GRSS_DFC_2013)

Table 4. Classification performance comparison (GRSS_DFC_2013)

Table 5. Overall classification accuracy in varyingproportion of training samples(Indian Pines)

Table 5. Overall classification accuracy in varyingproportion of training samples(Indian Pines)

Table 6. Overall classification accuracy in varying proportion of training samples(Salinas)

Table 6. Overall classification accuracy in varying proportion of training samples(Salinas)

Table 7. Overall classification accuracy in varying proportion of training samples (GRSS_DFC_2013)

Table 7. Overall classification accuracy in varying proportion of training samples (GRSS_DFC_2013)

Table 8. Computing time of algorithms (s)

Table 8. Computing time of algorithms (s)