Acta Optica Sinica, Volume. 39, Issue 10, 1028002(2019)
Hyperspectral Image Classification Algorithm Based on Two-Channel Generative Adversarial Network
Figures & Tables(16)
Fig. 7. Salinas dataset. (a) Pseudo color composite map; (b) feature reference map
Fig. 8. Classification results of the eight algorithms on the Salinas dataset. (a) Real feature reference map; (b) 1D-CNN; (c) 1D-GAN; (d) HS-1D-GAN; (e) 2D-CNN; (f) HS-2D-GAN; (g) 3D-CNN; (h) 3D-GAN; (i) HS-TC-GAN
Fig. 9. Indian pines dataset.(a) Pseudo color composite map; (b) feature reference map
Fig. 10. Classification results of the eight algorithms on the Indian pines dataset. (a) Real feature reference map; (b) 1D-CNN; (c) 1D-GAN; (d) HS-1D-GAN; (e) 2D-CNN; (f) HS-2D-GAN; (g) 3D-CNN; (h) 3D-GAN; (i) HS-TC-GAN
Table 1. Improved one-dimensional GAN classification framework
View tableTable 1. Improved one-dimensional GAN classification framework
Networks Layer Operation Kernel size BN Stride Padding Activation function Generator 1 Deconv 1×1×1024 No 1 0 ReLU 2 Deconv 1×1×128× a Yes 1 0 ReLU 3 Reshape - No - - No 4 Deconv 4×1×256 Yes 2 1 ReLU 5 Deconv 4×1×64 Yes 2 1 ReLU 6 Deconv 1×1×1 No 1 0 Tanh 7 Full 1× n ncNo - - No Discriminator 1 Conv 3×1×32 No 1 1 LeakyReLU 2 Conv 3×1×64 No 1 0 LeakyReLU 3 Conv 3×1×128 No 2 1 LeakyReLU 4 Conv 3×1×256 No 1 0 LeakyReLU 5 Conv 3×1×128 No 1 0 LeakyReLU 6 Conv 3×1×32 No 2 1 LeakyReLU 7 Reshape - No - - No 8 Conv 1×1×1024 No 1 0 No 9 Softmax 1024× n nclassNo - - No Sigmoid 1024×2 No - - No
Table 2. Improved two-dimensional GAN classification framework
View tableTable 2. Improved two-dimensional GAN classification framework
Networks Layer Operation Kernel size BN Stride Padding Activation function Generator 1 Deconv 1×1×1024 No 1 0 ReLU 2 Reshape - No - - No 3 Deconv 4×4×128 Yes 2 1 ReLU 4 Deconv 4×4×256 Yes 2 1 ReLU 5 Deconv 4×4×128 Yes 2 1 ReLU 6 Deconv 4×4×3 No 2 1 Tanh Discriminator 1 Conv 3×3×32 No 2 1 LeakyReLU 2 Conv 3×3×64 No 2 1 LeakyReLU 3 Conv 3×3×128 No 2 1 LeakyReLU 4 Conv 3×3×64 No 2 1 LeakyReLU 5 Reshape - No - - No 6 Conv 1×1×1024 No 1 0 No 7 Softmax 1024× n nclassNo - - No Sigmoid 1024×2 No - - No
Table 3. [in Chinese]
View tableTable 3. [in Chinese]
No. Color Class Sample number 1 
Brocoli_green_weeds_1 1977 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 11213 9 
Soil_vinyard_develop 6197 10 
Corn_senesced_green_weeds 3249 11 
Lettuce_romaine_4wk 1058 12 
Lettuce_romaine_5wk 1908 13 
Lettuce_romaine_6wk 909 14 
Lettuce_romaine_7wk 1061 15 
Vinyard_untrained 7164 16 
Vinyard_vertical_trellis 1737 Total 53785
Table 4. Comparison of classification performances of eight algorithms on Salinas dataset
View tableTable 4. Comparison of classification performances of eight algorithms on Salinas dataset
Index 1D-CNN 1D-GAN HS-1D-GAN 2D-CNN HS-2D-GAN 3D-CNN 3D-GAN HS-TC-GAN OA /% 86.12 86.88 90.22 87.84 97.15 92.04 93.38 99.67 AA /% 89.63 92.24 94.05 89.86 96.96 94.54 95.20 99.45 Kappa /% 84.48 85.39 89.10 86.44 96.82 91.13 92.63 99.63 Train time /s 8.96 19.67 120.99 94.61 195.60 211.90 350.49 385.27 Test time /s 0.51 0.54 2.87 3.95 2.43 4.00 3.48 5.11 Total time /s 9.47 20.21 123.86 98.56 198.03 215.90 353.97 390.38
Table 5. [in Chinese]
View tableTable 5. [in Chinese]
No. Color Class Sample number 1 
Alfalfa 46 2 
Corn-notill 1428 3 
Corn-min 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 386 16 
Stone-Steel-Towers 93 Total 10249
Table 6. Comparison of classification performances of eight algorithms on Indian pines dataset
View tableTable 6. Comparison of classification performances of eight algorithms on Indian pines dataset
Index 1D-CNN 1D-GAN HS-1D-GAN 2D-CNN HS-2D-GAN 3D-CNN 3D-GAN HS-TC-GAN OA /% 62.55 63.92 68.90 92.38 94.36 92.50 93.34 99.74 AA /% 53.29 55.94 60.28 84.87 91.12 89.78 86.80 97.52 Kappa /% 56.51 58.41 64.29 91.29 93.57 91.44 92.39 99.70 Train time /s 9.06 19.46 122.06 92.36 194.33 201.48 333.63 386.05 Test time /s 0.13 0.13 1.96 0.84 0.55 0.93 0.72 2.07 Total time /s 9.19 19.59 124.02 93.20 194.88 202.41 334.39 388.12
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Xiaojun Bi, Zeyu Zhou. Hyperspectral Image Classification Algorithm Based on Two-Channel Generative Adversarial Network[J]. Acta Optica Sinica, 2019, 39(10): 1028002
Paper Information
Category: Remote Sensing and Sensors
Received: Feb. 22, 2019
Accepted: Jun. 21, 2019
Published Online: Oct. 17, 2019
The Author Email: Zhou Zeyu (zhouzeyu100@hrbeu.edu.cn)
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