基于深度学习的空谱遥感图像融合综述
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胡建文, 汪泽平, 胡佩
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A review of pansharpening methods based on deep learning
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HU Jianwen, WANG Zeping, HU Pei
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表4 不同损失函数性能比较①
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Tab.4 Performance comparison of different loss function
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| 融合网络 | 类别 | 损失函数 | Q | SAM | ERGAS | SCC | QN | QNR | | PNN | 空间损失 | MSE | 0.931 1 | 5.089 0 | 2.979 8 | 0.930 6 | 0.929 5 | 0.913 0 | | MAE | 0.929 1 | 5.133 6 | 3.033 7 | 0.928 7 | 0.927 6 | 0.895 6 | | SSIM | 0.821 6 | 15.413 1 | 9.500 0 | 0.923 6 | 0.675 7 | 0.814 5 | | MSE+SSIM | 0.938 1 | 5.131 1 | 2.984 0 | 0.931 0 | 0.934 6 | 0.932 9 | | MAE+SSIM | 0.937 2 | 5.066 7 | 2.964 4 | 0.930 9 | 0.934 8 | 0.926 6 | | 光谱损失 | SAM | 0.566 2 | 4.712 8 | 8.928 9 | 0.503 4 | 0.539 0 | 0.641 0 | | 空谱损失 | MSE+SAM | 0.878 7 | 5.088 3 | 3.594 5 | 0.916 6 | 0.877 6 | 0.867 1 | | MAE+SAM | 0.928 1 | 5.061 6 | 3.035 3 | 0.928 3 | 0.927 1 | 0.900 7 | | MSE+SAM+SSIM | 0.937 7 | 5.015 0 | 2.977 7 | 0.931 4 | 0.933 9 | 0.928 2 | | MAE+SAM+SSIM | 0.934 1 | 5.146 0 | 3.020 2 | 0.925 5 | 0.931 5 | 0.923 4 |
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