Laser & Optoelectronics Progress, Volume. 60, Issue 12, 1228002(2023)
Hyperspectral Remote-Sensing Classification Combining Transformer and Multiscale Residual Mechanisms
Figures & Tables(19)
Fig. 10. Class activation mapping (CAM). (a) CAM of multiscale spectral enhanced residual fusion module; (b) CAM of spectral attention module
Fig. 11. OA values corresponding to different ratios of training samples. (a) Inida dataset; (b) PU dataset
Table 1. Figure categories and sample counts of India dataset
View tableTable 1. Figure categories and sample counts of India dataset
Class No. Land cover/use type Training Test 1 Alfalfa 23 23 2 Corn-notill 300 1128 3 Corn-min 300 530 4 Corn 118 119 5 Grass-pasture 241 242 6 Grass-trees 300 430 7 Grass-pasture-moved 14 14 8 Hay-windrowed 239 239 9 Oats 10 10 10 Soybean-notill 300 672 11 Soybean-mintill 300 2155 12 Soybean-clean 296 297 13 Wheat 102 103 14 Woods 300 965 15 Buildings-grass-trees-crives 193 193 16 Stone-steel-towers 46 47 Total 3082 7167
Table 2. Figure categories and sample counts of PU dataset
View tableTable 2. Figure categories and sample counts of PU dataset
Class No. Land cover/use type Training Test 1 Asphalt 663 5968 2 Meadows 1864 16785 3 Gravel 209 1890 4 Trees 306 2758 5 Painted metal sheets 134 1211 6 Bare soil 502 4527 7 Bitumen 133 1197 8 Self-blocking bricks 368 3314 9 Shadows 94 853 Total 4273 38503
Table 3. Experimental results of different dropout rates on India and PU datasets
View tableTable 3. Experimental results of different dropout rates on India and PU datasets
Dropout rate 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 OA /% （India） 99.47 95.60 99.51 97.41 98.23 99.01 98.44 96.91 OA /% （PU） 99.56 99.04 99.31 99.19 99.07 99.02 98.99 98.45
Table 4. Experimental results of different spatial sizes on India dataset
View tableTable 4. Experimental results of different spatial sizes on India dataset
Spatial dimension 9×9 11×11 13×13 15×15 17×17 19×19 21×21 OA /% 97.92 98.51 98.93 99.02 99.22 99.21 99.33 AA /% 98.10 98.68 98.95 99.05 99.11 99.16 99.36 Kappa /% 98.21 98.51 98.73 99.17 99.25 99.31 99.34 Spatial dimension 23×23 25×25 27×27 29×29 31×31 33×33 35×35 OA /% 99.47 99.51 99.39 99.35 99.31 99.26 99.21 AA /% 99.52 99.66 99.45 99.26 99.18 99.15 99.10 Kappa /% 99.42 99.44 99.32 99.21 99.05 98.72 98.69
Table 5. Classification results on India dataset
View tableTable 5. Classification results on India dataset
No. Baseline 1D-CNN 3D-CNN 3D+2D-CNN Swin-T SMSaNet 1 96.77 100.00 100.00 100.00 100.00 100.00 2 77.91 78.15 98.60 98.70 96.44 98.95 3 70.16 74.39 99.32 98.97 98.80 99.48 4 66.83 68.45 100.00 100.00 98.22 99.09 5 93.43 91.57 100.00 100.00 99.41 99.85 6 95.38 95.95 99.41 99.61 99.22 99.80 7 95.24 86.36 95.24 90.91 100.00 100.00 8 99.11 98.82 99.41 100.00 100.00 99.85 9 68.75 70.59 100.00 100.00 100.00 100.00 10 79.55 77.82 99.27 99.27 97.84 99.85 11 90.81 88.87 99.53 99.47 99.76 99.33 12 74.07 65.96 99.52 98.54 97.42 99.76 13 99.28 97.92 100.00 100.00 100.00 100.00 14 97.22 97.92 97.77 99.77 99.77 99.89 15 76.55 72.99 96.09 98.54 95.07 98.90 16 93.65 91.18 98.46 100.00 100.00 99.22 OA /% 85.21 84.21 99.01 99.31 98.63 99.51 AA /% 88.99 89.13 98.45 99.44 98.74 99.66 Kappa /% 83.29 82.17 98.87 99.22 98.44 99.44
Table 6. Classification results on PU dataset
View tableTable 6. Classification results on PU dataset
No. Baseline 1D-CNN 3D-CNN 3D+2D-CNN Swin-T SMSaNet 1 93.00 92.97 98.90 99.17 99.28 99.20 2 96.18 96.52 99.77 99.84 99.77 99.80 3 77.03 81.45 97.78 98.99 99.07 97.44 4 94.63 94.88 99.70 98.27 99.44 99.56 5 99.92 100.00 100.00 99.67 99.92 100.00 6 90.91 92.99 99.98 100.00 99.96 100.00 7 82.69 86.91 99.09 99.50 99.50 99.58 8 82.69 82.73 98.05 99.60 97.30 99.08 9 99.65 98.95 96.51 98.91 97.41 98.81 OA /% 92.66 93.40 99.32 99.54 99.38 99.56 AA /% 90.15 90.81 98.39 98.95 98.72 99.18 Kappa /% 90.26 91.23 99.10 99.39 99.18 99.41
Table 7. Ablation experiments
View tableTable 7. Ablation experiments
Method and module India PU Method Shift M S OA /% AA /% Kappa /% OA /% AA /% Kappa /% w/o shift √ √ 99.30 98.93 98.88 99.37 98.77 98.56 SMSaNet √ √ √ 99.51 99.66 99.44 99.56 99.18 99.41 w/o M √ √ 98.86 98.63 98.71 99.11 99.05 98.82 w/o S √ √ 99.21 99.16 99.13 99.28 99.09 98.87 w/o M and S √ 98.56 98.17 98.06 98.89 98.65 98.48
Table 8. Params and FLOPs for different models
View tableTable 8. Params and FLOPs for different models
Method Image size Param /M FLOPs Baseline 1×1 4.230 4.23 MFLOPs 1D-CNN 1×1 0.036 0.11 MFLOPs 3D-CNN 25×25 0.772 93.82 MFLOPs 3D+2D-CNN 25×25 5.009 152.68 MFLOPs Swin-T 25×25 27.495 89.78 MFLOPs SMSaNet 25×25 5.056 46.65 MFLOPs
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Yuhan Chen, Bo Wang, Qingyun Yan, Bingjie Huang, Tong Jia, Bin Xue. Hyperspectral Remote-Sensing Classification Combining Transformer and Multiscale Residual Mechanisms[J]. Laser & Optoelectronics Progress, 2023, 60(12): 1228002
Paper Information
Category: Remote Sensing and Sensors
Received: Mar. 8, 2022
Accepted: Jun. 13, 2022
Published Online: Jun. 1, 2023
The Author Email: Bo Wang (wangbo@nuist.edu.cn)
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