# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 3 Experiments
### Ablation Experiments  
To investigate the influence of alternative loss functions on the model, the ADAM optimizer is used to train the Swin-MRDB model with \(L_{1}\) and \(L_{2}\) loss functions on the Botswana dataset. In this experiment, the CC, SAM, RMSE, and ERGAS were selected for objective evaluation of the results, and the detailed results are shown in Table 6. The comparison shows that the model based on the \(L_{1}\) loss function achieves better results, preserving the spatial details of the fused images and reducing the loss of spectral information. Compared with the \(L_{2}\) loss function, when the model is trained with the \(L_{1}\) loss function, all metrics are improved significantly, including the CC (by 9.5%), SAM (by 65%), RMSE (by 44%), and ERGAS (by 75%), so we finally choose to train the model with the \(L_{1}\) loss function.  
In addition, this section compares the effects of different multi-scale residuals and dense feature extraction branches on the model. The addition of a multi-scale feature extraction branch to the Swin transformer can effectively improve the performance of the model and help reconstruct higher-quality remote sensing images. As can be seen from Table 6, the addition of two multi-scale feature extraction branches significantly improves the objective evaluation metrics. When adding multi-scale feature extraction blocks for PAN images, the CC, SAM, RMSE, and ERGAS are improved by 1.5%, 9.2%, 13.7%, and 14.2%, respectively. When adding multi-scale feature extraction blocks for HS images, the CC, SAM, RMSE, and ERGAS are improved by 1.2%, 3.2%, 11.5%, and 14%, respectively.  
In order to evaluate the impact of the number of residual blocks in the reconstruction module on the Swin-MRDB model, this paper selects Botswana as the experimental dataset and compares the impact of different numbers of residual blocks on objective evaluation metrics. The detailed results are shown in Figure 9. The horizontal coordinate is the number of residual blocks, and it can be seen that increasing the number of residual blocks results in better performance for the Swin-MRDB model, but there is no significant change in model ability as the number of residual blocks increases. Considering that more residual fusion blocks will affect the speed of the model, we finally choose one residual block to fuse the extracted features.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Model** & \(\mathbf{L_{1}}\) & \(\mathbf{L_{2}}\) & \
\begin{tabular}{c} **RMDB-** \\ **pan** \\ \end{tabular} &
\begin{tabular}{c} **RMDB-** \\ **MS** \\ \end{tabular} & **CC** & **SAM** & **RMSE** & **ERGAS** \\ \hline Swin- & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & 0.977 & 1.483 & 1.074 & 1.93 \\ SRME & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & 0.892 & 2.461 & 1.942 & 3.38 \\ MRDB & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & 0.965 & 1.533 & 1.214 & 2.19 \\  & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & 0.962 & 1.634 & 1.245 & 2.25 \\ \hline \hline \end{tabular}  
* The units of RMSE are \(10^{-2}\).  
\end{table}
Table 6: Ablation test results.