Acta Photonica Sinica, Volume. 52, Issue 12, 1210002(2023)
Spectral-spatial Attention Residual Networks for Hyperspectral Image Classification
Figures & Tables(28)
Fig. 2. The structure of the central region spectral attention mechanism
Fig. 12. The visualization result of each algorithm on the IP dataset
Fig. 13. The visualization result of each algorithm on the SA dataset
Fig. 14. The visualization result of each algorithm on the PU dataset
Fig. 15. The visualization result of each algorithm on the Houston dataset
Table 1. The number of training and testing samples on IP dataset
View tableView in ArticleTable 1. The number of training and testing samples on IP dataset
Sample category Sample name Training samples Test samples Total —— 2 045 10 249 0 Alfalfa 9 46 1 Corn-notill 285 1 428 2 Corn-mintill 166 830 3 Corn 47 237 4 Grass-pasture 96 483 5 Grass-trees 146 730 6 Grass-pasture-mowed 5 28 7 Hay-windrowed 95 478 8 Oats 4 20 9 Soybean-notill 194 972 10 Soybean-mintill 491 2 455 11 Soybean-clean 118 593 12 Wheat 41 205 13 Woods 253 1 265 14 Buildings-Grass-Trees-Drives 77 386 15 Stone-Steel-Towers 18 93
Table 2. The number of training and testing samples on SA dataset
View tableView in ArticleTable 2. The number of training and testing samples on SA dataset
Sample category Sample name Training samples Test samples Total —— 1 076 54 129 0 Brocoli_green_weeds_1 40 2 009 1 Brocoli_green_weeds_2 74 3 726 2 Fallow 39 1 976 3 Fallow_rough_plow 27 1 394 4 Fallow_smooth 53 2 678 5 Stubble 79 3 959 6 Celery 71 3 579 7 Grapes_untrained 225 11 271 8 Soil_vinyard_develop 124 6 203 9 Corn_senesced_green_weeds 65 3 278 10 Lettuce_romaine_4wk 21 1 068 11 Lettuce_romaine_5wk 38 1 927 12 Lettuce_romaine_6wk 18 916 13 Lettuce_romaine_7wk 21 1 070 14 Vinyard_untrained 145 7 268 15 Vinyard_vertical_trellis 36 1 807
Table 3. The number of training and testing samples on PU dataset
View tableView in ArticleTable 3. The number of training and testing samples on PU dataset
Sample category Sample name Training samples Test samples Total —— 423 42 776 0 Asphalt 66 6 631 1 Meadows 186 18 649 2 Gravel 20 2 099 3 Trees 30 3 064 4 Painted metal sheets 13 1 345 5 Bare Soil 50 5 029 6 Bitumen 13 1 330 7 Self-Blocking Bricks 36 3 682 8 Shadows 9 947
Table 4. The number of training and testing samples on Houston dataset
View tableView in ArticleTable 4. The number of training and testing samples on Houston dataset
Sample category Sample name Training samples Test samples Total —— 2 832 12 197 0 Healthy Grass 198 1 053 1 Stressed Grass 190 1 064 2 Synthetic Grass 192 505 3 Trees 188 1 056 4 Soil 186 1 056 5 Water 182 143 6 Residential 196 1 072 7 Commercial 191 1 053 8 Road 193 1 059 9 Highway 191 1 036 10 Railway 181 1 054 11 Parking Lot 1 192 1 041 12 Parking Lot 2 184 285 13 Tennis Court 181 247 14 Running Track 187 473
Table 5. The overall accuracy of the different size of the patch on the four datasets
View tableView in ArticleTable 5. The overall accuracy of the different size of the patch on the four datasets
Size IP SA PU Houston 9×9 99.57 98.23 98.20 81.04 11×11 99.69 99.02 98.46 85.35 13×13 99.79 99.69 99.09 85.75 15×15 99.50 99.65 98.87 85.78 17×17 99.51 99.68 99.09 85.80 19×19 99.38 99.71 98.95 84.16
Table 6. The overall accuracy of the different network on the four datasets
View tableView in ArticleTable 6. The overall accuracy of the different network on the four datasets
Network IP SA PU Houston Basic network 97.89 98.21 98.31 83.06 Spectral attention network 99.02 98.74 98.54 84.91 Spectral-spatial attention residual network 99.79 99.69 99.09 85.75
Table 7. The overall accuracy of the different network with different training ratios on the IP datasets
View tableView in ArticleTable 7. The overall accuracy of the different network with different training ratios on the IP datasets
IP dataset scale 5% 10% 15% 20% 2D CNN 65.49 71.18 80.17 82.85 3D CNN 73.61 84.20 90.79 93.23 HybirdSN 88.91 95.44 98.12 99.49 RIAN 87.65 93.87 96.75 97.82 SSFTT 94.98 98.19 99.30 99.45 SSARN 96.09 98.56 99.43 99.79
Table 8. The overall accuracy of the different network with different training ratios on the IP datasets
View tableView in ArticleTable 8. The overall accuracy of the different network with different training ratios on the IP datasets
SA dataset scale 0.5% 1% 1.5% 2% 2D CNN 71.92 87.33 88.12 88.88 3D CNN 80.08 88.64 90.80 92.45 HybirdSN 93.27 95.55 98.17 98.44 RIAN 91.88 96.37 96.70 97.18 SSFTT 94.72 96.31 97.22 98.70 SSARN 95.02 97.89 98.71 99.69
Table 9. The overall accuracy of the different network with different training ratios on the IP datasets
View tableView in ArticleTable 9. The overall accuracy of the different network with different training ratios on the IP datasets
PU dataset scale 0.3% 0.5% 0.7% 1% 2D CNN 76.13 82.92 84.86 89.22 3D CNN 76.94 82.21 85.10 86.24 HybirdSN 85.06 93.03 94.87 97.63 RIAN 76.82 89.98 91.74 94.03 SSFTT 86.99 94.78 96.65 97.24 SSARN 93.93 97.46 98.15 99.09
Table 10. The category accuracy，OA，AA and Kappa of the different algorithms on IP dataset
View tableView in ArticleTable 10. The category accuracy，OA，AA and Kappa of the different algorithms on IP dataset
Sample category 2D CNN 3D CNN HybridSN RIAN SSFTT SSARN 0 43.48 84.78 100.00 95.62 95.65 100.00 1 75.28 91.81 99.72 96.29 99.93 99.86 2 74.22 88.67 99.76 96.63 99.40 99.28 3 59.92 91.14 94.94 94.94 97.89 100.00 4 93.17 94.62 98.14 95.03 99.38 100.00 5 98.08 99.45 100.00 99.45 99.45 100.00 6 57.14 60.71 96.43 92.86 100.00 100.00 7 93.31 100.00 100.00 99.79 100.00 100.00 8 50.00 95.00 100.00 95.00 100.00 100.00 9 76.95 90.43 98.66 97.48 99.69 99.38 10 85.17 93.36 99.71 98.45 99.02 99.76 11 62.56 82.97 99.33 96.46 98.82 99.83 12 99.02 100.00 100.00 98.54 99.51 100.00 13 95.02 97.15 100.00 99.84 100.00 100.00 14 80.83 96.37 100.00 97.41 100.00 100.00 15 78.49 93.55 100.00 98.92 98.92 98.92 AA 76.42 91.25 99.17 97.07 99.23 99.81 OA 82.85 93.23 99.49 97.82 99.45 99.79 Kappa 80.36 92.27 99.42 97.52 99.38 99.76
Table 11. The category accuracy，OA，AA and Kappa of the different algorithms on SA dataset
View tableView in ArticleTable 11. The category accuracy，OA，AA and Kappa of the different algorithms on SA dataset
Sample category 2D CNN 3D CNN HybridSN RIAN SSFTT SSARN 0 98.41 96.67 100.00 99.85 100.00 100.00 1 97.26 99.60 100.00 99.76 100.00 100.00 2 94.33 99.44 100.00 99.54 99.80 100.00 3 98.57 98.42 99.50 98.28 98.14 98.64 4 97.87 97.80 98.02 98.47 97.42 99.96 5 98.36 100.00 100.00 99.92 100.00 100.00 6 97.26 97.09 100.00 99.89 100.00 99.97 7 79.3 86.86 96.73 92.91 97.32 99.35 8 99.19 97.11 100.00 98.94 99.90 100.00 9 79.44 92.04 98.87 99.18 96.71 98.93 10 92.13 91.67 100.00 96.44 98.60 99.81 11 99.95 99.48 99.90 99.95 99.90 100.00 12 95.63 99.78 99.56 98.91 100.00 100.00 13 95.70 97.01 98.88 97.66 99.63 99.63 14 70.20 77.45 95.18 94.30 97.84 99.52 15 93.97 93.69 99.45 97.12 99.28 100.00 AA 92.96 95.26 99.13 98.20 99.03 99.74 OA 88.88 92.45 98.44 97.18 98.70 99.69 Kappa 87.61 91.59 98.26 96.86 98.55 99.65
Table 12. Category accuracy，OA，AA and Kappa of the different algorithms on PU dataset
View tableView in ArticleTable 12. Category accuracy，OA，AA and Kappa of the different algorithms on PU dataset
Sample category 2D CNN 3D CNN HybridSN RIAN SSFTT SSARN 0 92.34 83.40 97.62 93.83 96.00 100.00 1 97.42 99.36 99.83 98.05 99.84 99.98 2 59.27 48.26 83.99 67.37 92.85 98.71 3 73.60 90.57 97.55 95.72 97.49 94.13 4 98.44 84.54 100.00 99.85 100.00 99.85 5 76.91 66.81 97.95 92.56 98.11 100.00 6 79.85 70.23 97.89 75.79 81.88 94.81 7 87.78 74.71 93.70 96.44 92.18 97.58 8 93.77 91.13 94.72 85.64 96.30 97.99 AA 84.37 78.78 95.92 89.47 94.96 98.12 OA 89.22 86.24 97.63 94.03 97.24 99.09 Kappa 85.47 81.46 96.85 92.08 96.34 98.79
Table 13. The category accuracy，OA，AA and Kappa of the different algorithms on Houston dataset
View tableView in ArticleTable 13. The category accuracy，OA，AA and Kappa of the different algorithms on Houston dataset
Sample category 2D CNN 3D CNN HybridSN RIAN SSFTT SSARN 0 82.72 82.53 72.93 81.29 82.53 82.62 1 84.21 82.05 81.96 58.55 84.77 85.15 2 97.82 92.28 85.15 88.32 85.94 100.00 3 91.29 91.57 72.25 80.21 92.99 91.67 4 98.20 99.24 98.49 83.62 99.72 100.00 5 94.41 92.31 77.62 60.84 94.41 95.80 6 75.75 75.19 66.33 66.51 83.86 86.38 7 66.95 56.51 73.31 45.11 65.43 88.60 8 73.47 66.95 50.05 58.83 74.41 81.87 9 44.79 50.77 100.00 20.17 51.74 47.88 10 78.18 73.91 86.34 35.48 74.38 81.50 11 77.91 72.33 80.31 51.30 78.48 93.47 12 84.21 81.75 65.61 45.61 89.83 85.26 13 98.79 96.76 100.00 76.52 99.19 100.00 14 100.00 82.45 100.00 86.68 94.93 100.00 AA 83.25 79.77 80.69 62.60 83.51 88.01 OA 79.92 76.95 79.41 60.66 80.66 85.75 Kappa 78.37 75.18 77.64 57.58 79.10 84.57
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Feifei WANG, Huijie ZHAO, Na LI, Siyuan LI, Yu CAI. Spectral-spatial Attention Residual Networks for Hyperspectral Image Classification[J]. Acta Photonica Sinica, 2023, 52(12): 1210002
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Received: Apr. 18, 2023
Accepted: May. 25, 2023
Published Online: Feb. 19, 2024
The Author Email: Na LI (lina_17@buaa.edu.cn)
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