改进Transformer的高光谱图像地物分类方法——以黄河三角洲为例

李薇, 樊彦国, 周培希

Improved Transformer-based hyperspectral image classification method for surface features: A case study of the Yellow River Delta

LI Wei, FAN Yanguo, ZHOU Peixi

表2 NC13数据集的不同方法的分类结果

Tab.2 Classification results of different methods for NC13 datasets (%)

类别名称 查全率 SVM 1DCNN CDCNN SSRN HybridSN ViT 本文方法 碱蓬 86.08 80.37 80.97 91.10 95.53 88.30 92.23 沥青水泥混合路面 99.96 99.77 98.64 99.37 100.00 94.05 99.73 湿地 77.10 87.17 86.65 90.04 70.77 73.38 80.37 水域 99.98 99.97 98.82 99.81 100.00 99.54 99.76 石油 90.35 93.78 91.08 99.54 95.20 94.24 99.04 芦苇 31.84 46.96 53.91 44.27 52.84 52.00 50.89 柽柳 0 38.94 60.00 50.56 17.04 46.36 57.27 车 27.36 65.49 77.74 87.72 72.93 57.36 85.17 旱田 53.67 81.13 87.64 66.06 98.27 92.00 74.97 标准反射板 0 100.00 37.65 97.37 0 89.79 51.02 柽柳芦苇混生 35.12 56.63 53.44 59.60 59.13 51.73 52.13 碱蓬芦苇混生 61.56 64.79 69.23 46.54 69.73 51.98 65.12 芦苇水域混合 45.82 49.52 49.88 65.77 58.54 68.72 65.61 OA 69.38 70.41 69.81 71.46 70.65 70.86 73.84 AA 54.53 74.19 72.74 76.75 68.46 73.80 74.87 Kappa 62.73 64.91 64.29 66.18 65.19 65.47 68.94