Fig. 1. Overall network structure
Fig. 2. Detail injection model
Fig. 3. Multi-scale CNN and channel attention module
Fig. 4. Structure of feature reconstruction network
Fig. 5. Fusion result of WorldView-4 simulation dataset
Fig. 6. Residual graph of WorldView-4 simulation dataset
Fig. 7. Fusion result of QuickBird simulation dataset
Fig. 8. Residual graph of QuickBird simulation dataset
Fig. 9. Fusion result of WorldView-2 simulation dataset
Fig. 10. Residual graph of WorldView-2 simulation dataset
Fig. 11. Fusion result of WorldView-4 real dataset
Fig. 12. Three different window attention unit structures
1 | for i in epochs | 第i个epoch,最大epoch个数设为200 | 2 | | for j in batches | 第j个batch | 3 | | Select 32 patches of PAN images; | 选取PAN数据集的32张图像; | 4 | Select 32 patches of LRMS images; | 选取LRMS数据集的32张图像; | 5 | Select 32 patches of HRMS images; | 选取HRMS数据集的32张图像; | 6 | Produce the output ; | 计算模型生成的融合图像; | 7 | Calculate the loss ; | 计算融合图像和参考图像的损失函数; | 8 | Update parameters by AdamOptimizer; | 根据,利用Adam优化器更新模型的参数; | 9 | end | | 10 | end | |
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Table 0. [in Chinese]
| Training dataset | Testing dataset(reduced resolution) | Testing dataset(full resolution) |
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Number | Size | Number | Size | Number | Size |
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WV4 | LRMS | 22 000 | 16×16×4 | 50 | 64×64×4 | 50 | 256×256×4 | PAN | 22 000 | 64×64×1 | 50 | 256×256×1 | 50 | 1 024×1 024×1 | HRMS | 22 000 | 64×64×4 | 50 | 256×256×4 | - | - | QB | LRMS | 22 000 | 16×16×4 | 50 | 64×64×4 | 50 | 256×256×4 | PAN | 22 000 | 64×64×1 | 50 | 256×256×1 | 50 | 1 024×1 024×1 | HRMS | 22 000 | 64×64×4 | 50 | 256×256×4 | - | - | WV2 | LRMS | 22 000 | 16×16×8 | 50 | 64×64×8 | 50 | 256×256×8 | PAN | 22 000 | 64×64×1 | 50 | 256×256×1 | 50 | 1 024×1 024×1 | HRMS | 22 000 | 64×64×8 | 50 | 256×256×8 | - | - |
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Table 1. Specific information about the dataset
| WV4 | QB | WV2 |
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Method | ERGAS↓ | SAM↓ | PSNR↑ | SCC↑ | ERGAS↓ | SAM↓ | PSNR↑ | SCC↑ | ERGAS↓ | SAM↓ | PSNR↑ | SCC↑ |
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MTF-GLP | 6.340 | 5.772 | 23.524 | 0.914 | 2.698 | 2.334 | 37.271 | 0.857 | 6.338 | 7.699 | 26.891 | 0.878 | Wavelet | 6.425 | 6.460 | 23.401 | 0.864 | 4.316 | 2.981 | 32.160 | 0.660 | 6.703 | 8.435 | 26.096 | 0.845 | PCA | 6.505 | 7.337 | 23.326 | 0.878 | 2.981 | 3.162 | 36.584 | 0.792 | 7.881 | 8.842 | 25.081 | 0.828 | IHS | 5.661 | 5.394 | 24.486 | 0.902 | 2.826 | 2.573 | 36.048 | 0.723 | 6.454 | 7.780 | 26.628 | 0.876 | MSDCNN | 2.811 | 3.232 | 30.590 | 0.973 | 1.359 | 1.468 | 43.334 | 0.953 | 4.036 | 5.145 | 30.938 | 0.944 | FusionNet | 2.910 | 3.190 | 30.280 | 0.972 | 1.270 | 1.369 | 43.856 | 0.959 | 3.845 | 5.050 | 31.217 | 0.948 | Panformer | 2.820 | 3.170 | 30.677 | 0.975 | 1.251 | 1.362 | 44.077 | 0.961 | 3.888 | 5.013 | 31.229 | 0.948 | LAGConv | 2.693 | 3.110 | 30.956 | 0.976 | 1.272 | 1.406 | 43.813 | 0.958 | 3.878 | 5.070 | 31.140 | 0.947 | TFNet | 2.585 | 3.115 | 31.390 | 0.978 | 1.238 | 1.344 | 44.154 | 0.961 | 3.795 | 5.003 | 31.397 | 0.950 | MSCANet | 2.275 | 2.831 | 32.478 | 0.982 | 1.233 | 1.310 | 44.202 | 0.962 | 3.665 | 4.869 | 31.691 | 0.953 |
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Table 2. Objective evaluation index of simulation dataset
Method | WV4 | QB | WV2 |
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| ↓ | QNR↑ | ↓ | ↓ | QNR↑ | ↓ | ↓ | QNR↑ |
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MTF-GLP | 0.065 5 | 0.050 9 | 0.887 1 | 0.095 7 | 0.150 9 | 0.768 9 | 0.094 2 | 0.065 3 | 0.847 0 | Wavelet | 0.014 1 | 0.039 8 | 0.946 7 | 0.133 5 | 0.151 4 | 0.738 2 | 0.046 9 | 0.073 1 | 0.883 6 | PCA | 0.034 8 | 0.064 7 | 0.902 8 | 0.016 4 | 0.083 9 | 0.901 1 | 0.069 5 | 0.056 8 | 0.877 6 | IHS | 0.013 3 | 0.067 0 | 0.920 6 | 0.018 0 | 0.091 9 | 0.891 8 | 0.025 9 | 0.047 2 | 0.928 2 | MSDCNN | 0.024 0 | 0.016 4 | 0.960 0 | 0.013 2 | 0.034 0 | 0.953 3 | 0.018 0 | 0.046 2 | 0.936 6 | FusionNet | 0.027 8 | 0.026 4 | 0.946 5 | 0.014 1 | 0.029 1 | 0.957 3 | 0.017 2 | 0.031 6 | 0.951 8 | Panformer | 0.040 0 | 0.018 8 | 0.942 0 | 0.015 1 | 0.037 3 | 0.948 2 | 0.020 3 | 0.031 4 | 0.948 9 | LAGConv | 0.030 6 | 0.018 9 | 0.951 1 | 0.014 9 | 0.054 1 | 0.931 8 | 0.017 7 | 0.029 5 | 0.953 4 | TFNet | 0.019 9 | 0.026 1 | 0.954 5 | 0.015 4 | 0.041 6 | 0.943 6 | 0.014 9 | 0.048 7 | 0.937 2 | MSCANet | 0.018 1 | 0.008 8 | 0.973 2 | 0.011 0 | 0.031 1 | 0.958 2 | 0.015 5 | 0.023 4 | 0.961 5 |
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Table 3. Objective evaluation index of real dataset
Model | ERGAS | SAM | PSNR↑ | SCC↑ |
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Non-injection model | 2.412 | 2.926 | 31.944 | 0.980 | Injection model | 2.268 | 2.816 | 32.488 | 0.983 |
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Table 4. Ablation result of injection model in WV4 dataset
Structure | MLP | Multi-scale CNN | Channel-attention | ERGAS | SAM | PSNR↑ | SCC↑ |
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Fig.12(a) | √ | | | 2.767 | 3.156 | 30.763 | 0.974 | Fig.12(b) | | √ | | 2.377 | 2.916 | 32.100 | 0.981 | Fig.12(c) | | √ | √ | 2.268 | 2.816 | 32.488 | 0.983 |
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Table 5. Ablation result of MSCA in WV4 dataset
MAE | Spectral loss | Spatial loss | ERGAS | SAM | PSNR↑ | SCC↑ |
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√ | | | 2.362 | 2.873 | 32.109 | 0.981 | √ | √ | | 2.343 | 2.845 | 32.211 | 0.981 | √ | | √ | 2.329 | 2.900 | 32.261 | 0.982 | √ | √ | √ | 2.268 | 2.816 | 32.488 | 0.983 |
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Table 6. Ablation result of loss function in WV4 dataset
Method | Runtime/s | Parameters |
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MTF-GLP | 0.919 | - | Wavelet | 0.095 | - | PCA | 0.122 | - | IHS | 0.105 | - | MSDCNN | 0.046 | | FusionNet | 0.053 | | Panformer | 0.197 | | LAGConv | 0.079 | | TFNet | 0.125 | | MSCANet | 0.147 | |
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Table 7. Average test time and number of parameters for all methods