改进的残差式3D-CNN和近邻注意力的高光谱遥感图像分类

潘增滢, 吴瑞姣, 林易丰, 翁谦, 林嘉雯

Hyperspectral remote sensing image classification using improved residual 3D-CNN and neighborhood attention

PAN Zengying, WU Ruijiao, LIN Yifeng, WENG Qian, LIN Jiawen

表1 不同方法在IP数据集上的分类结果

Tab.1 Classification Results of Different Methods on IP Datasets

类别 1D-CNN 2D-CNN SSRN HybridSN A2S2K morphFormer LSGA IR3NAN 苜蓿 22.93 55.61 72.93 79.76 79.27 89.51 88.29 97.56 未翻耕的玉米地 59.95 78.06 84.92 95.35 98.67 97.11 98.79 98.96 翻耕过的玉米地 51.02 82.53 89.21 93.36 98.33 96.49 95.92 98.89 玉米地 38.78 82.35 91.60 88.69 97.56 95.82 97.32 98.50 牧草区 73.72 95.52 96.74 92.34 98.92 96.46 98.32 98.69 草地与树木 86.51 97.84 99.47 97.69 99.27 99.74 99.89 99.82 已收割的牧草区 45.60 42.80 66.40 64.80 92.40 99.20 100.00 99.60 风干的草料 94.26 99.49 98.09 97.19 99.81 99.79 99.88 99.93 燕麦 18.89 65.56 9.44 94.44 99.44 84.44 97.78 98.33 未翻耕的大豆田 48.00 76.99 86.82 96.49 98.89 98.74 99.22 99.05 翻耕过的大豆田 67.89 84.51 97.24 95.71 99.38 98.45 99.50 99.67 已清理的大豆田 43.73 82.73 98.45 91.46 99.29 95.13 98.65 99.16 小麦 93.35 99.19 99.68 98.11 100.00 99.73 99.78 100.00 树林 92.99 98.13 92.68 99.08 99.93 99.87 99.99 100.00 建筑物、草地、树木、车道 39.39 89.57 89.34 88.1 98.67 98.30 98.79 100.00 石、钢、塔、楼 89.76 98.81 98.57 81.79 98.10 94.76 94.88 96.79 OA/% 66.67 86.77 92.84 95.08 99.03 98.04 98.92 99.39 AA/% 60.42 83.11 85.72 90.9 97.37 96.47 97.94 99.06 Kappa 0.619 8 0.847 5 0.918 8 0.943 9 0.988 9 0.977 6 0.987 7 0.993 0