改进的残差式3D-CNN和近邻注意力的高光谱遥感图像分类
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潘增滢, 吴瑞姣, 林易丰, 翁谦, 林嘉雯
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Hyperspectral remote sensing image classification using improved residual 3D-CNN and neighborhood attention
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PAN Zengying, WU Ruijiao, LIN Yifeng, WENG Qian, LIN Jiawen
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表3 不同方法在HO集上的分类结果
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Tab.3 Classification results of different methods on the HO dataset
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| 类别 | 1D-CNN | 2D-CNN | SSRN | HybridSN | A2S2K | morphFormer | LSGA | IR3NAN | | 健康的草地 | 93.90 | 94.65 | 93.36 | 91.83 | 96.37 | 94.77 | 94.85 | 96.83 | | 受压力的草地 | 94.81 | 97.63 | 96.54 | 95.50 | 99.13 | 97.95 | 98.85 | 99.21 | | 人造草皮 | 95.39 | 95.44 | 92.63 | 97.98 | 99.24 | 99.31 | 99.63 | 99.89 | | 树木 | 94.03 | 98.60 | 99.87 | 95.53 | 99.93 | 97.52 | 97.66 | 99.93 | | 土壤 | 95.86 | 97.37 | 99.61 | 100.00 | 100.00 | 100.00 | 100.00 | 100.00 | | 水 | 68.01 | 67.21 | 78.16 | 85.37 | 84.04 | 83.75 | 96.25 | 95.44 | | 住宅区 | 80.29 | 95.12 | 93.66 | 94.52 | 98.24 | 98.68 | 98.94 | 99.05 | | 商业区 | 78.10 | 84.39 | 91.09 | 91.14 | 94.93 | 94.52 | 94.16 | 94.94 | | 道路 | 73.29 | 89.06 | 83.47 | 93.73 | 98.55 | 97.80 | 97.79 | 98.11 | | 高速公路 | 71.85 | 87.05 | 88.26 | 98.77 | 99.35 | 99.47 | 99.86 | 99.97 | | 铁路 | 63.66 | 92.69 | 90.84 | 99.85 | 99.78 | 98.32 | 99.95 | 100.00 | | 停车场1 | 75.47 | 92.18 | 88.17 | 96.51 | 97.84 | 96.82 | 96.77 | 99.12 | | 停车场2 | 23.19 | 88.01 | 80.87 | 89.78 | 91.36 | 89.21 | 92.62 | 92.29 | | 网球场 | 89.44 | 98.91 | 94.84 | 73.33 | 99.96 | 99.89 | 100.00 | 100.00 | | 跑道 | 94.79 | 100.00 | 99.34 | 97.99 | 100.00 | 99.89 | 100.00 | 100.00 | | OA/% | 81.11 | 92.91 | 92.14 | 95.19 | 98.21 | 97.45 | 97.96 | 98.64 | | AA/% | 79.47 | 91.89 | 91.38 | 93.46 | 97.25 | 96.53 | 97.82 | 98.32 | | Kappa | 0.794 7 | 0.923 0 | 0.914 5 | 0.947 7 | 0.980 6 | 0.972 3 | 0.977 8 | 0.985 2 |
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