Acta Optica Sinica, Volume. 41, Issue 3, 0310001(2021)
Hyperspectral Image Classification Based on Dilated Convolutional Attention Neural Network
Figures & Tables(16)
Fig. 2. Schematic of standard convolution and dilated convolution. (a) 2D standard convolution; (b) 2D dilated convolution (r=2,2); (c) 3D standard convolution; (d) 3D dilated convolution (r=2,2,2)
Fig. 3. Multi-scale feature fusion structure. (a) Multi-scale spatial-spectral feature fusion module; (b) multi-scale spatial feature fusion module
Fig. 4. Structure diagram of attention module. (a) Spatial-spectral attention module; (b) spatial attention module
Fig. 6. False color image and ground truth of data sets. (a) PU data set; (b) SA data set
Fig. 7. Comparison of accuracy for different spatial sizes. (a) PU data set; (b) SA data set
Fig. 8. Classification maps and partial enlarged maps with different algorithms on PU data set. (a) Ground truth image; (b) 2D-CNN-MLP; (c) 3D-CNN-CRF; (d) Hybrid-CNN; (e) Dilated-3D-CNN; (f) 3D-2D-ADCNN
Fig. 9. Classification maps with different algorithms on SA data set. (a) Ground truth image; (b) 2D-CNN-MLP; (c) 3D-CNN-CRF; (d) Hybrid-CNN; (e) Dilated-3D-CNN; (f) 3D-2D-ADCNN
Fig. 10. Overall accuracy with different numbers of training samples. (a) PU data set; (b) SA data set
Table 1. Comparison of overall accuracy, training time, and test time for different convolution kernel numbers
View tableTable 1. Comparison of overall accuracy, training time, and test time for different convolution kernel numbers
Kernel number Pavia University Salinas OA /% Training time /s Test time /s OA /% Training time /s Test time /s 16 97.78 118.03 29.71 96.48 243.42 66.38 20 96.87 130.56 34.19 97.28 324.71 76.93 24 97.17 149.93 38.34 96.26 373.57 88.24 28 96.25 185.74 43.35 95.88 383.83 98.84 32 97.05 165.58 46.76 96.12 416.47 107.36
Table 2. Comparison of parameters and overall accuracy for different model architectures
View tableTable 2. Comparison of parameters and overall accuracy for different model architectures
Architecture Pavia University Salinas Parameter OA /% Parameter OA /% 3D-2D-CNN (baseline) 75596 98.11 139644 98.03 3D-2D-CNN+dilated convolution 56783 98.47 110402 98.42 3D-2D-CNN+attention 75643 98.51 139718 98.38 3D-2D-CNN+dilated convolution+attention 56857 98.73 110476 98.61
Table 3. Comparison of classification accuracy for different algorithms on PU data setunit: %
View tableTable 3. Comparison of classification accuracy for different algorithms on PU data setunit: %
Category name 2D-CNN-MLP 3D-CNN-CRF Hybrid-CNN Dilated-3D-CNN 3D-2D-ADCNN Asphalt 87.33 86.78 90.89 84.86 96.82 Meadows 90.46 86.51 96.31 87.67 99.47 Gravels 84.91 85.76 94.45 87.73 99.35 Trees 93.00 96.27 92.39 97.71 97.77 Painted-Metal-Sheets 99.72 99.90 99.78 99.75 99.92 Bare-Soil 88.46 85.18 99.06 85.94 100.00 Bitumen 94.67 93.23 99.24 89.33 99.84 Self-Blocking-Bricks 85.04 86.74 87.81 83.76 98.91 Shadows 98.94 99.11 96.87 99.82 98.57 OA 89.76 87.92 94.89 89.48 98.75 AA 91.39 91.05 95.20 89.89 98.96 Kappa 96.54 84.22 93.26 86.04 98.60
Table 4. Comparison of classification accuracy for different algorithms on SA data setunit: %
View tableTable 4. Comparison of classification accuracy for different algorithms on SA data setunit: %
Category name 2D-CNN-MLP 3D-CNN-CRF Hybrid-CNN Dilated-3D-CNN 3D-2D-ADCNN Brocoli_green_weeds_1 99.98 99.39 99.99 99.97 100.00 Brocoli_green_weeds_2 99.48 98.92 99.96 99.70 100.00 Fallow 99.27 99.59 98.57 99.08 100.00 Fallow_rough_plow 99.30 99.16 99.47 99.47 99.61 Fallow_smooth 98.44 97.44 98.65 96.86 99.80 Stubble 99.76 99.94 99.79 99.76 100.00 Celery 99.61 99.60 99.84 99.60 100.00 Grapes_untrained 79.40 76.09 88.90 70.83 94.17 Soil_vinyard_develop 99.92 99.73 98.61 99.05 100.00 Corn_senesced_green_weeds 95.61 94.52 99.18 92.72 100.00 Lettuce_romaine_4wk 99.02 99.29 99.82 98.78 100.00 Lettuce_romaine_5wk 99.99 99.88 99.01 99.92 100.00 Lettuce_romaine_6wk 99.98 99.78 99.52 99.33 100.00 Lettuce_romaine_7wk 98.86 99.01 99.41 99.38 99.89 Vinyard_untrained 81.90 79.83 94.71 81.40 99.09 Vinyard_vertical_trellis 98.05 97.14 99.81 98.36 100.00 OA 92.57 91.38 96.48 90.25 98.61 AA 96.79 96.21 98.45 95.82 99.53 Kappa 91.73 90.40 96.09 89.17 98.45
Table 5. Comparison of parameters, training time, and test time for different algorithms on PU data set
View tableTable 5. Comparison of parameters, training time, and test time for different algorithms on PU data set
Item 2D-CNN-MLP 3D-CNN-CRF Hybrid-CNN Dilated-3D-CNN 3D-2D-ADCNN Parameter 33129 155017 4844793 413801 56857 Training time /s 8.91 103.30 63.75 671.89 122.22 Test time /s 2.17 13.04 7.85 72.36 51.93
Table 6. Comparison of parameters, training time, and test time for different algorithms on SA data set
View tableTable 6. Comparison of parameters, training time, and test time for different algorithms on SA data set
Item 2D-CNN-MLP 3D-CNN-CRF Hybrid-CNN Dilated-3D-CNN 3D-2D-ADCNN Parameter 33556 192144 4845696 498912 110476 Training time /s 16.94 276.08 97.08 2122.97 327.33 Test time /s 2.70 27.69 9.70 170.50 122.61
Tools
Get Citation
Copy Citation Text
Xiangdong Zhang, Tengjun Wang, Shaojun Zhu, Yun Yang. Hyperspectral Image Classification Based on Dilated Convolutional Attention Neural Network[J]. Acta Optica Sinica, 2021, 41(3): 0310001
Paper Information
Category: Image Processing
Received: Jul. 6, 2020
Accepted: Sep. 8, 2020
Published Online: Feb. 28, 2021
The Author Email: Xiangdong Zhang (18755150056@163.com)
© Copyright 2018-2021 | Chinese Laser Press.
All Rights Reserved 沪ICP备15018463号-20