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3 图像融合算法性能比较①
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Tab.3 Comparison of pansharpening methods
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| 算法 | Q | SAM | ERGAS | SCC | QN | QNR | | GS | 0.816 8 | 6.711 1 | 4.580 6 | 0.827 3 | 0.820 7 | 0.867 7 | | BDSD | 0.853 8 | 8.081 8 | 4.733 2 | 0.830 7 | 0.858 3 | 0.896 5 | | GLP | 0.866 9 | 6.432 2 | 4.125 0 | 0.849 9 | 0.872 1 | 0.800 4 | | PNN | 0.931 1 | 5.089 0 | 2.979 8 | 0.930 6 | 0.929 5 | 0.913 0 | | PanNet | 0.938 2 | 4.847 9 | 2.851 7 | 0.935 1 | 0.935 9 | 0.929 0 | | DCCNP | 0.935 1 | 5.076 3 | 2.951 5 | 0.935 8 | 0.933 6 | 0.915 2 | | RSIFNN | 0.934 7 | 5.132 3 | 2.917 3 | 0.930 1 | 0.933 4 | 0.928 8 | | TFNet | 0.941 6 | 4.613 7 | 2.774 3 | 0.943 1 | 0.939 9 | 0.918 6 | | MSDCNN | 0.935 1 | 4.939 9 | 2.891 9 | 0.936 3 | 0.933 2 | 0.928 7 | | NLRNet | 0.943 3 | 4.360 2 | 2.929 6 | 0.948 3 | 0.939 9 | 0.943 3 | | MDCNN | 0.941 6 | 4.365 5 | 2.706 3 | 0.947 9 | 0.940 1 | 0.948 8 | | SDS | 0.948 3 | 4.348 1 | 2.596 9 | 0.951 7 | 0.946 4 | 0.955 6 |
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