Laser & Optoelectronics Progress, Volume. 58, Issue 24, 2428008(2021)
Hyperspectral Semi-Supervised Classification Algorithm Based on Improved Ladder Network
Figures & Tables(14)
Fig. 6. Model of semi-supervised classification algorithm based on improved ladder network
Fig. 8. True color image and ground truth map of Pavia University dataset. (a) True color image; (b) ground truth map
Fig. 9. True color image and ground truth map of Indian Pines dataset. (a) True color image; (b) ground truth map
Fig. 10. Classification results of different algorithms on Pavia University dataset. (a) True color image; (b) feature label map; (c) M3D-DCNN algorithm; (d) 3D-CNN-LSTM algorithm; (e) SS-CNN algorithm; (f) S4CNN algorithm; (g) 3D-CNN-LSTM-LN algorithm
Fig. 11. Classification results of different algorithms on Indian Pines dataset. (a) True color image; (b) feature label map; (c) M3D-DCNN algorithm; (d) 3D-CNN-LSTM algorithm; (e) SS-CNN algorithm; (f) S4CNN algorithm; (g) 3D-CNN-LSTM-LN algorithm
Table 1. Network structure of encoder in 3D-CNN-LSTM-LN model
View tableTable 1. Network structure of encoder in 3D-CNN-LSTM-LN model
Layer Kernel size Input size Output size Number of convolution kernels Activation function Input -- -- 11×11×30 -- -- Conv-1(Conv 3D) 3×3×1 11×11×30 9×9×30 4 -- BN-1 -- 9×9×30 9×9×30 -- ReLU Pooling-1(MaxPooling) 1×1×2 9×9×30 9×9×15 -- -- Conv-2(Conv 3D) 3×3×1 9×9×15 7×7×15 8 -- BN-2 -- 7×7×15 7×7×15 -- ReLU Pooling-2(MaxPooling) 1×1×2 7×7×15 7×7×7 -- -- Conv-3(Conv 3D) 3×3×1 7×7×7 5×5×7 16 -- BN-3 -- 5×5×7 5×5×7 -- ReLU Pooling-3(MaxPooling) 1×1×2 5×5×7 5×5×3 -- -- Linear-1(flatten) -- 5×5×3 1×75 -- -- RNN-1(Bi-LSTM) -- 1200×1 400×1 -- Tanh
Table 2. Classification accuracy of different algorithms on Pavia University dataset unit: %
View tableTable 2. Classification accuracy of different algorithms on Pavia University dataset unit: %
No. Class M3D-DCNN 3D-CNN-LSTM SS-CNN S4CNN 3D-CNN-LSTM-LN 1 Asphalt 93.25 93.03 94.98 96.94 99.21 2 Meadow 98.14 96.99 96.84 99.11 99.63 3 Gravel 76.83 75.13 89.16 86.97 96.22 4 Tree 97.78 91.45 93.50 98.33 98.94 5 Metal sheet 100.00 96.63 99.33 99.78 99.66 6 Bare soil 92.87 90.31 92.28 97.40 99.18 7 Bitumen 80.56 86.97 83.21 91.76 97.79 8 Brick 83.14 83.95 90.92 93.50 98.13 9 Shadow 94.25 88.52 96.84 97.00 98.55 OA -- 93.73 92.46 94.46 97.06 99.03 AA -- 90.62 89.13 92.96 95.64 98.59 Kappa -- 91.70 89.90 92.80 96.20 98.80
Table 3. Classification accuracy of different algorithms on Indian Pines dataset unit: %
View tableTable 3. Classification accuracy of different algorithms on Indian Pines dataset unit: %
No. Class M3D-DCNN 3D-CNN-LSTM SS-CNN S4CNN 3D-CNN-LSTM-LN 1 Alfalfa 39.12 77.65 81.91 97.23 96.85 2 Corn-notill 77.95 82.45 83.24 91.77 96.93 3 Corn-mintill 88.67 82.36 74.34 89.34 93.97 4 Corn 87.02 66.75 79.67 96.38 97.15 5 Grass-pasture 95.43 93.17 97.65 92.93 95.49 6 Grass-tree 98.78 95.36 88.96 99.36 98.17 7 Grass-pasture-mowed 70.31 88.95 75.02 97.72 62.13 8 Hay-windrowed 95.33 97.34 89.61 98.33 98.88 9 Oat 53.35 56.02 82.47 72.03 57.15 10 Soybean-notill 81.63 90.14 82.35 92.19 97.31 11 Soybean-mintill 86.27 88.46 82.63 92.84 98.19 12 Soybean-clean 80.68 74.28 83.28 83.96 92.56 13 Wheat 99.45 97.67 81.36 97.95 99.70 14 Wood 94.59 98.32 87.64 97.71 99.65 15 Building-grass-tree-drive 76.95 90.24 80.27 89.72 95.12 16 Stone-steel-tower 95.27 46.83 97.79 90.98 93.35 OA -- 87.20 88.83 89.56 93.34 97.04 AA -- 82.51 83.22 84.26 82.40 92.03 Kappa -- 85.30 87.20 88.80 92.30 96.60
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Shihao Guan, Guang Yang, Shan Lu, Chunbai Jin, Hao Li, Zhaohong Xu. Hyperspectral Semi-Supervised Classification Algorithm Based on Improved Ladder Network[J]. Laser & Optoelectronics Progress, 2021, 58(24): 2428008
Paper Information
Category: Remote Sensing and Sensors
Received: Oct. 29, 2020
Accepted: Dec. 27, 2020
Published Online: Dec. 3, 2021
The Author Email: Guang Yang (yg2599@126.com)
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