Hyperspectral Image Classification Method Based on Targets Constraint and Spectral-Spatial Iteration

Chunyan Yu; Meng Zhao; Meiping Song; Sen Li; Yulei Wang

doi:10.3788/AOS201838.0628003

Journals >Acta Optica Sinica >Volume 38 >Issue 6 >Page 0628003 > Article

Acta Optica Sinica
Vol. 38, Issue 6, 0628003 (2018)

Hyperspectral Image Classification Method Based on Targets Constraint and Spectral-Spatial Iteration

Chunyan Yu¹, Meng Zhao¹, Meiping Song^1、2、*, Sen Li¹, and Yulei Wang^1、2、3

Author Affiliations

¹ Information Science and Technology College, Dalian Maritime University, Dalian, Liaoning 116026, China

² State Key Laboratory of Integrated Services Networks, Xi'an, Shannxi 710071, China

³ Key Laboratory of Spectral Imaging Technology, Chinese Academy of Sciences, Xi'an, Shannxi 710071, China

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DOI: 10.3788/AOS201838.0628003 Cite this Article Set citation alerts

Chunyan Yu, Meng Zhao, Meiping Song, Sen Li, Yulei Wang. Hyperspectral Image Classification Method Based on Targets Constraint and Spectral-Spatial Iteration[J]. Acta Optica Sinica, 2018, 38(6): 0628003 Copy Citation Text

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Fig. 1. Flow chart of classification method

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Fig. 2. Comparison of feedback stack methods

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Fig. 3. Image of Purdue. (a) False color image; (b) image of ground truth

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Fig. 4. Classification results of Purdue data by MTCC-1 method. (a) Iteration for 1 time; (b) iteration for 2 times; (c) iteration for 5 times; (d) final result

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Fig. 5. Classification results of Purdue data by MTCC-2 method. (a) Iteration for 1 time; (b) iteration for 2 times; (c) iteration for 5 times; (d) final result

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Fig. 6. Image of Salinas Valley. (a) False color image; (b) image of ground truth

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Fig. 7. Classification results of Salinas data by MTCC-1 method. (a) Iteration for 1 time; (b) iteration for 2 times; (c) iteration for 5 times; (d) final result

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Fig. 8. Classification results of Salinas data by MTCC-2 method. (a) Iteration for 1 time; (b) iteration for 2 times; (c) iteration for 5 times; (d) final result

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Fig. 9. Image of Pavia University. (a) False color image; (b) image of ground truth

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Fig. 10. Classification results of Pavia data by MTCC-1 method. (a) Iteration for 1 time; (b) iteration for 2 times; (c) iteration for 5 times; (d) final result

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Fig. 11. Classification results of Pavia data by MTCC-2 method. (a) Iteration for 1 time; (b) iteration for 2 times; (c) iteration for 5 times; (d) final result

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Classification result	Real result
Classification result	C₁	C₂	…	C_n	BKG
C₁	n₁₁	n₁₂	…	n₁_n	n_1B
C₂	n₂₁	n₂₂	…	n₂_n	n_2B
︙	︙	︙	︙	︙	︙
C_n	n_n₁	n_n₂	…	n_nn	n_n_B
BKG	n_B1	n_B2	…	n_B_n	n_BB

Table 1. Confusion matrix of C-class classification results

Label	Class name	Sample
Class 1	Alfalfa	46
Class 2	Corn-notill	1428
Class 3	Corn-min	830
Class 4	Corn	237
Class 5	Grass/pasture	483
Class 6	Grass/trees	730
Class 7	Grass/pasture-mowed	28
Class 8	Hay-windrowed	478
Class 9	Oats	20
Class 10	Soybeans-notill	972
Class 11	Soybeans-min	2455
Class 12	Soybeans-clean	593
Class 13	Wheat	205
Class 14	Woods	1265
Class 15	Bldg-grass green-drives	386
Class 16	Stone-steel towers	93
BKG	Background	10776

Table 2. Category name and pixel number of Purdue image

Class	MTCC-1		MTCC-2		EPF-B-g		EPF-B-c		EPF-G-g		EPF-G-c
Class	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre
1	91.30	97.67	95.65	95.65	100.00	57.50	97.83	57.69	97.83	58.44	100.00	60.53
2	97.76	94.58	97.69	91.96	85.01	67.03	84.94	66.91	85.22	67.09	84.45	65.76
3	99.40	93.96	98.80	92.34	93.13	74.40	94.10	73.54	92.41	73.12	92.41	74.11
4	100.00	99.16	100.00	99.58	99.16	64.74	99.16	64.56	99.16	60.26	99.16	59.95
5	97.10	94.94	93.79	95.77	93.58	39.65	93.37	41.45	94.00	42.23	93.58	43.55
6	97.40	95.05	98.63	91.25	100.00	49.06	99.73	48.92	99.73	46.82	100.00	48.70
7	100.00	100.00	100.00	100.00	96.43	62.79	96.43	65.85	96.43	56.25	96.43	56.25
8	99.58	99.37	99.58	100.00	100.00	69.88	100.00	70.40	100.00	71.77	100.00	71.34
9	100.00	64.52	100.00	62.50	95.00	38.78	100.00	42.55	100.00	55.56	95.00	63.33
10	97.53	93.03	97.53	90.46	82.30	62.70	82.82	61.97	81.79	62.21	82.51	62.51
11	98.78	95.62	98.37	94.19	95.23	76.71	95.64	76.63	94.46	77.02	94.70	77.45
12	96.96	94.73	96.80	97.95	98.82	62.47	98.65	63.38	98.48	61.67	98.65	61.13
13	98.54	98.54	97.07	98.51	99.02	79.61	99.02	77.19	99.51	76.69	99.51	73.12
14	97.55	97.70	95.57	96.95	98.26	31.91	98.50	31.58	98.10	31.90	98.50	31.97
15	97.15	98.17	99.74	98.72	96.63	7.85	96.89	7.94	94.30	7.67	99.48	7.99
16	96.77	95.74	96.77	96.77	96.77	54.88	98.92	57.86	100.00	52.25	100.00	52.54
A_O	98.09		97.70		94.83		95.33		94.99		94.60
P	96.84		96.00		46.23		46.47		46.31		46.12

Table 3. Comparison of classification evaluation results of Purdue data with different methods%

Label	Class name	Sample
Class 1	Weed 1	2009
Class 2	Weed 2	3726
Class 3	Fallow	1976
Class 4	Fallow rough plow	1394
Class 5	Fallow smooth	2678
Class 6	Stubble	3959
Class 7	Celery	3579
Class 8	Grapes untrained	11271
Class 9	Soil vineyard develop	6203
Class 10	Corn	3278
Class 11	Lettuce 4 weeks	1068
Class 12	Lettuce 5 weeks	1927
Class 13	Lettuce 6 weeks	916
Class 14	Lettuce 7 weeks	1070
Class 15	Vineyard untrained	7268
Class 16	Vineyard trellis	1807
BKG	Background	56975

Table 4. Category name and pixel number of Salinas image

Class	MTCC-1		MTCC-2		EPF-B-g		EPF-B-c		EPF-G-g		EPF-G-c
Class	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre
1	98.61	93.22	99.15	90.46	100.00	73.19	100.00	74.05	100.00	74.00	100.00	71.22
2	98.28	95.22	99.06	94.01	100.00	56.22	99.97	56.21	100.00	56.72	100.00	54.67
3	92.91	92.73	95.24	88.69	100.00	12.20	100.00	12.22	100.00	12.13	100.00	12.10
4	94.12	85.47	94.62	83.75	100.00	23.13	100.00	23.25	100.00	23.04	100.00	22.64
5	95.22	86.03	96.34	85.63	98.51	55.95	98.36	55.66	98.47	56.29	98.84	57.61
6	98.81	89.97	99.27	89.32	100.00	81.75	100.00	81.18	100.00	81.58	100.00	80.65
7	98.44	89.87	99.39	88.35	100.00	79.34	100.00	80.14	100.00	80.21	99.97	79.48
8	98.97	95.61	98.05	94.88	81.52	87.11	81.47	86.84	82.37	88.33	83.52	88.82
9	94.97	96.70	94.23	96.50	99.85	35.25	99.84	35.33	99.87	35.19	99.87	34.90
10	95.79	95.88	96.03	94.93	96.19	28.06	96.06	27.77	96.49	27.96	97.86	30.16
11	94.10	93.75	95.32	89.85	100.00	23.57	99.91	23.58	100.00	23.71	100.00	24.09
12	96.94	90.59	97.82	90.36	100.00	27.42	100.00	27.36	100.00	27.20	100.00	27.23
13	97.38	76.96	96.83	76.27	99.13	67.16	99.45	67.38	99.56	66.91	99.78	66.52
14	97.57	83.92	98.60	82.17	100.00	65.97	100.00	65.64	100.00	65.40	100.00	67.85
15	98.35	98.18	96.86	96.89	93.82	76.17	93.26	75.86	94.32	76.86	96.19	77.94
16	97.68	92.80	98.56	92.23	99.61	69.47	99.56	69.19	99.39	70.35	99.67	70.38
A_O	97.33		97.29		95.87		95.70		96.01		96.55
P	95.32		94.81		46.71		46.63		46.77		47.04

Table 5. Comparison of classification evaluation results of Salinas data with different methods%

Label	Class name	Sample
Class 1	Asphalt	6631
Class 2	Meadows	18649
Class 3	Gravel	2099
Class 4	Trees	3064
Class 5	Painted metal sheets	1345
Class 6	Bare soil	5029
Class 7	Bitumen	1330
Class 8	Self-blocking bricks	3682
Class 9	Shadows	947
BKG	Background	164624

Table 6. Category name and pixel number of Pavia image

Class	MTCC-1		MTCC-2		EPF-B-g		EPF-B-c		EPF-G-g		EPF-G-c
Class	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre	C_i_,OA	C_i_,Pre
1	83.56	32.66	85.06	23.00	97.07	18.28	97.10	18.20	96.53	18.56	96.95	18.72
2	85.05	85.17	84.63	76.47	98.10	36.51	98.09	36.48	98.13	37.10	98.16	37.42
3	80.04	42.60	76.46	33.66	91.47	34.19	91.71	34.55	91.76	35.11	91.81	35.02
4	83.86	22.37	84.93	19.18	95.04	10.03	93.99	9.93	94.35	10.21	98.14	10.69
5	99.12	61.43	98.03	60.02	100.00	42.92	100.00	44.42	100.00	41.81	100.00	44.63
6	85.41	89.91	88.24	85.92	100.00	9.69	100.00	9.74	100.00	9.73	100.00	9.72
7	87.37	70.25	85.29	66.51	100.00	39.82	100.00	39.19	100.00	40.82	100.00	41.34
8	82.03	27.09	90.52	22.79	99.02	19.19	98.72	19.23	98.91	19.28	99.51	19.31
9	80.15	24.55	73.22	19.06	100.00	9.41	100.00	9.15	100.00	7.44	100.00	7.00
A_O	84.68		85.43		98.97		98.95		98.84		99.17
P	79.13		71.44		20.41		20.41		20.39		20.45

Table 7. Comparison of classification evaluation results of Pavia data with different methods%

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