# STGAN: Swin Transformer-Based GAN to Achieve Remote Sensing Image Super-Resolution Reconstruction
## 4 Experiments
### Ablation Experiments  
In this section, we validate the effectiveness of the key components of the proposed method, including the analysis of RefSR, the Robustness Augmentation Module (RAM-V), the use of specific gradient losses, and the hyperparametric tuning of the weights. Figure 9: Visual comparison of MAE for different models. MAE represents the mean absolute error for each spectral band. Analysis of the effectiveness of the RefSR:  
To validate the effectiveness of the RefSR method, we conducted comparison experiments, keeping the same training strategy and network parameters. In the comparison experiments, the baseline method is constructed using only a convolutional neural network without the self-attention mechanism, while the STGAN(SISR) model removes all the reference image inputs and simulates the traditional SISR method by using only the LR images and their corresponding gradient maps for super-resolution reconstruction. The experimental results, as shown in Table 3, show that the RefSR method significantly improves the performance of super-resolution reconstruction through the synergistic effect of multiple image inputs and outperforms the SISR method and the baseline method. In addition, the gradual introduction of more depth-referenced features further enhances the reconstruction effect. Based on this, our method employs three levels of reference features to maximize the utilization of multi-image information, thus achieving better super-resolution reconstruction performance. 2 Effectiveness of RAM-V:  
In order to verify the effectiveness of RAM-V, we removed RAM-V in the feature transfer process and replaced it with direct channel-level fusion of the inputs and processed them through a convolutional neural network instead. As shown in Table 4, the use of RAM-V significantly improves the robustness of the model in different scenarios. This is because RAM-V is able to suppress the influence of irrelevant information in Ref features and focuses on the relevant regions between LR images and Ref images. Thus, RAM-V significantly improves the robustness of the model by suppressing irrelevant information and enhancing the correlation between the two. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Dataset** & **Metric** & **Baseline** & **STGAN (RefSR)** & **STGAN (SISR)** \\ \hline \multirow{8}{*}{1 st test set} & LPIPS\(\downarrow\) & 0.1589 & 0.1059 & 0.1477 \\  & PL\(\downarrow\) & 3.6247 & 4.3911 & 4.5501 \\  & PSNR\(\uparrow\) & 29.5883 & 31.4151 & 29.8390 \\  & SSIM\(\uparrow\) & 0.7678 & 0.8408 & 0.8286 \\  & RMSE\(\downarrow\) & 8.2804 & 5.5169 & 7.6866 \\  & SAM\(\downarrow\) & 0.0174 & 0.0116 & 0.0162 \\ \hline \multirow{8}{*}{2 nd test set} & LPIPS\(\downarrow\) & 0.1986 & 0.1085 & 0.1513 \\  & PL\(\downarrow\) & 3.5580 & 3.3668 & 4.6999 \\  & PSNR\(\uparrow\) & 29.1053 & 30.3556 & 29.1378 \\  & SSIM\(\uparrow\) & 0.7368 & 0.7990 & 0.7669 \\  & RMSE\(\downarrow\) & 11.6051 & 6.3602 & 8.8589 \\  & SAM\(\downarrow\) & 0.0190 & 0.0104 & 0.0145 \\ \hline \multirow{8}{*}{3 rd test set} & LPIPS\(\downarrow\) & 0.2140 & 0.1570 & 0.2119 \\  & PL\(\downarrow\) & 3.2653 & 4.0744 & 5.4991 \\ \cline{1-1}  & PSNR\(\uparrow\) & 27.5391 & 28.7841 & 27.5459 \\ \cline{1-1}  & SSIM\(\uparrow\) & 0.7010 & 0.7707 & 0.7321 \\ \cline{1-1}  & RMSE\(\downarrow\) & 11.0889 & 8.1407 & 10.9873 \\ \cline{1-1}  & SAM\(\downarrow\) & 0.0222 & 0.0163 & 0.0219 \\ \hline \multirow{8}{*}{4 th test set} & LPIPS\(\downarrow\) & 0.2175 & 0.1211 & 0.1688 \\ \cline{1-1}  & PL\(\downarrow\) & 3.6153 & 4.9014 & 6.8320 \\ \cline{1-1}  & PSNR\(\uparrow\) & 29.1843 & 30.2993 & 29.3837 \\ \cline{1-1}  & SSIM\(\uparrow\) & 0.7163 & 0.7902 & 0.7663 \\ \cline{1-1}  & RMSE\(\downarrow\) & 6.7907 & 3.7800 & 5.2689 \\ \cline{1-1}  & SAM\(\downarrow\) & 0.0119 & 0.0066 & 0.0091 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experiments on the effectiveness of the RefSR method, where STGAN (RefSR) is the model designed in this paper and STGAN (SISR) is the single-input (LR) version of STGAN.