基于DeepLabv3+语义分割模型的GF-2影像城市绿地提取

刘文雅, 岳安志, 季珏, 师卫华, 邓孺孺, 梁业恒, 熊龙海

Urban green space extraction from GF-2 remote sensing image based on DeepLabv3+ semantic segmentation model

Wenya LIU, Anzhi YUE, Jue JI, Weihua SHI, Ruru DENG, Yeheng LIANG, Longhai XIONG

表4 语义分割网络结果精度对比

Tab.4 The precision results comparison of semantic segmentation networks

指标 方法 切片1 切片2 切片3 切片4 切片5 切片6 平均值 Precision/% PspNet 74.13 72.81 82.62 83.78 73.85 72.36 76.59 SegNet 76.34 70.09 81.94 85.79 78.66 78.69 78.59 U-Net 85.83 77.58 78.42 81.42 75.62 78.67 79.59 DeepLabv2 74.09 81.41 81.03 86.19 72.91 74.30 78.32 DeepLabv3+ 88.63 86.54 88.20 86.49 81.23 86.99 86.35 Recall/% PspNet 81.62 71.18 83.63 75.21 72.14 75.52 76.55 SegNet 80.83 85.19 78.70 73.68 74.20 85.67 79.71 U-Net 80.31 86.99 78.10 73.30 72.55 85.41 79.45 DeepLabv2 78.31 81.52 88.82 81.49 74.80 79.81 80.79 DeepLabv3+ 83.42 87.11 86.96 78.73 88.08 86.26 85.09 F值 PspNet 0.77 0.72 0.83 0.79 0.73 0.74 0.77 SegNet 0.79 0.77 0.80 0.79 0.76 0.82 0.79 U-Net 0.83 0.82 0.78 0.77 0.75 0.80 0.79 DeepLabv2 0.76 0.81 0.85 0.84 0.75 0.76 0.79 DeepLabv3+ 0.86 0.87 0.88 0.82 0.85 0.87 0.86 OA/% PspNet 87.13 84.79 89.30 84.09 79.79 87.25 85.39 SegNet 87.86 85.95 87.82 84.42 85.80 89.02 86.81 U-Net 90.96 89.53 86.32 82.44 84.05 88.73 87.01 DeepLabv2 86.53 89.81 89.92 87.24 81.13 87.79 87.07 DeepLabv3+ 92.51 92.74 92.23 86.43 89.92 92.28 91.02