基于深度森林的遥感图像变化检测模型
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葛利华, 王鹏, 张燕琴, 赵双林
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Deep forest-based model for detecting changes in remote sensing images
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GE Lihua, WANG Peng, ZHANG Yanqin, ZHAO Shuanglin
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表1 2个数据集上各方法的对比
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Tab.1 Comparison of the two test sets
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| 方法 | LEVIR-CD | SYSU-CD | | 准确率 | 召回率 | F1 | 准确率 | 召回率 | F1 | | FC-EF | 0.922 | 0.773 | 0.841 | 0.721 | 0.902 | 0.801 | | FC-EF_DF | 0.942 | 0.966 | 0.954 | 0.804 | 0.827 | 0.815 | | FC-Siam-conc | 0.910 | 0.850 | 0.879 | 0.695 | 0.831 | 0.757 | | FC-Siam-conc_DF | 0.927 | 0.935 | 0.930 | 0.783 | 0.853 | 0.817 | | FC-Siam-diff | 0.914 | 0.861 | 0.887 | 0.991 | 0.632 | 0.772 | | FC-Siam-diff_DF | 0.931 | 0.952 | 0.941 | 0.988 | 0.805 | 0.887 | | SNUnet | 0.864 | 0.947 | 0.904 | 0.689 | 0.718 | 0.703 | | SNUnet_DF | 0.939 | 0.933 | 0.936 | 0.855 | 0.926 | 0.889 | | DTCDSCN | 0.883 | 0.904 | 0.893 | 0.810 | 0.857 | 0.833 | | DTCDSCN_DF | 0.918 | 0.887 | 0.902 | 0.807 | 0.869 | 0.837 | | STANet | 0.918 | 0.833 | 0.873 | 0.792 | 0.838 | 0.814 | | STANet_DF | 0.924 | 0.867 | 0.894 | 0.903 | 0.806 | 0.852 |
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