Hyperspectral Image Classification Based on Residual Generative Adversarial Network

Fig. 6. Structure for residual block of discriminator. (a) Block structure with convolution residuals; (b) Block structure without convolution residuals

Download full size

Fig. 7. Network structure of discriminator

Download full size

Fig. 8. Pavia University dataset. (a) Pseudo color image; (b) ground datum map

Download full size

Fig. 9. Salinas dataset. (a) Pseudo color image; (b) ground datum map

Download full size

Fig. 10. Indian Pines dataset. (a) Pseudo color image; (b) ground datum map

Download full size

Fig. 11. Hyperspectral image classification result map of Indian Pines dataset. (a) CAE-SVM; (b) 2DCNN; (c) 3DCNN; (d) ResNet;(e) proposed method

Download full size

Fig. 12. Hyperspectral image classification result map of Pavia University dataset. (a) CAE-SVM; (b) 2DCNN; (c) 3DCNN; (d) ResNet;(e) proposed method

Download full size

Fig. 13. Hyperspectral image classification result map of Salinas dataset. (a) CAE-SVM; (b) 2DCNN; (c) 3DCNN; (d) ResNet;(e) proposed method

Download full size

Table 1. Categories and number of Pavia University hyperspectral dataset
View table
Table 1. Categories and number of Pavia University hyperspectral dataset
Class Name Color Sample
1 Asphalt 6631
2 Meadows 18649
3 Gravel 2099
4 Trees 3064
5 Painted metal sheets 1345
6 Bare Soil 5029
7 Bitumen 1330
8 Self-Blocking Bricks 3682
9 Shadows 947

Table 2. Categories and number of Salinas hyperspectral dataset

View table

Table 2. Categories and number of Salinas hyperspectral dataset

Class	Name	Sample
1	Brocoli_green_weeds_1	2009
2	Brocoli_green_weeds_2	3726
3	Fallow	1976
4	Fallow_rough_plow	1394
5	Fallow_smooth	2678
6	Stubble	3959
7	Celery	3579
8	Grapes_untrained	11271
9	Soil_vinyard_develop	6203
10	Corn_senesced_green_weeds	3278
11	Lettuce_romaine_4wk	1068
12	Lettuce_romaine_5wk	1927
13	Lettuce_romaine_6wk	916
14	Lettuce_romaine_7wk	1070
15	Vinyard_untrained	7268
16	Vinyard_vertical_trellis	1807

Table 3. Categories and number of Indian Pines dataset

View table

Table 3. Categories and number of Indian Pines dataset

Class	Name	Sample
1	Alfalfa	46
2	Corn-notill	1428
3	Corn-mintill	830
4	Corn	237
5	Grass-pasture	483
6	Grass-trees	730
7	Grass-pasture-mowed	28
8	Hay-windrowed	478
9	Oats	20
10	Soybean-notill	972
11	Soybean-mintill	2455
12	Soybean-clean	593
13	Wheat	205
14	Woods	1265
15	Buildings-Grass-Trees-Drives	386
16	Stone-Steel-Towers	93

Table 4. Comparison of classification results of ablation experiments

View table

Table 4. Comparison of classification results of ablation experiments

Index	GAN						GAN with residual discriminator			Proposed method
Index	IP	PU	SA	IP	PU	SA	IP	PU	SA	IP	PU	SA
OA	95.85	96.59	96.57	96.52	97.11	97.53	97.21	97.34	97.93	98.84	99.00	99.09
AA	94.10	94.42	98.02	98.50	95.77	98.69	96.38	96.25	98.77	98.48	98.74	99.45
K	95.27	95.47	96.18	96.03	96.16	97.25	96.82	96.46	97.69	98.69	98.67	98.98

Table 5. Comparison of classification accuracy of Indian Pines hyperspectral dataset
View table
Table 5. Comparison of classification accuracy of Indian Pines hyperspectral dataset
Index CAE-SVM 2DCNN 3DCNN ResNet Proposed method
OA 76.81 85.93 93.85 97.05 98.84
AA 75.52 87.23 94.13 96.78 98.48
K 75.28 84.30 92.77 96.63 98.69

Table 6. Comparison of classification accuracy of Pavia University hyperspectral dataset
View table
Table 6. Comparison of classification accuracy of Pavia University hyperspectral dataset
Index CAE-SVM 2DCNN 3DCNN ResNet Proposed
OA 84.28 90.81 95.69 97.24 99.00
AA 70.85 86.25 94.23 96.39 98.74
K 78.67 87.71 94.29 96.35 98.67

Table 7. Comparison of classification accuracy of Salinas hyperspectral dataset
View table
Table 7. Comparison of classification accuracy of Salinas hyperspectral dataset
Index CAE-SVM 2DCNN 3DCNN ResNet Proposed
OA 84.34 91.22 95.41 97.20 99.09
AA 79.97 93.02 96.07 98.31 99.45
K 82.52 90.22 94.15 96.91 98.98

Tools

Get Citation

Copy Citation Text

Ming Chen, Xiangyun Xi, Yang Wang. Hyperspectral Image Classification Based on Residual Generative Adversarial Network[J]. Laser & Optoelectronics Progress, 2022, 59(22): 2210008

Download Citation

EndNote(RIS)BibTex Plain Text

Set citation alerts for article

Save article for my favorites