基于孪生残差神经网络的GF-2影像林地变化检测

艾遒一,黄华国,郭颖,刘炳杰,陈树新,田昕

Forest Change Detection based on Siamese Neural Network with GF-2 Image: A Case of Jiande Forest Farm, Zhejiang

Qiuyi AI,Huaguo HUANG,Ying GUO,Bingjie LIU,Shuxin CHEN,Xin TIAN

表5 不同方法变化检测指标对比

Table 5 Comparison of change detection indicators of different methods

编号 方法 主干提取网络 变化类别 精确率 召回率 F1分数 1 Ours ResNet50 Class0 96.48 94.59 95.53 Class1 76.25 75.52 75.88 Class2 54.06 78.49 64.02 Macro avg 75.60 82.87 78.48 2 Ours ResNet50+CBAM Class0 96.60 95.71 96.15 Class1 75.41 81.63 78.40 Class2 66.23 67.33 66.78 Macro avg 79.41 81.59 80.44 3 Ours ResNet50+SE Class0 95.81 98.57 97.17 Class1 87.12 77.12 81.81 Class2 90.86 56.80 69.90 Macro avg 91.26 77.50 82.96 4 FC-Siam-conc — Class0 96.66 89.50 92.94 Class1 75.97 87.61 81.37 Class2 21.71 49.50 30.18 Macro avg 64.78 75.53 68.16 5 FC-Siam-diff — Class0 95.90 95.20 95.55 Class1 83.84 83.64 83.74 Class2 37.77 44.10 40.67 Macro avg 72.50 74.30 73.32