# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 3 Experiments
### Results
#### 3.4.1 Results for the Pavia Dataset  
Urban buildings make up the majority of Pavia's characteristics; thus, we compare the pan-sharpening results mostly in terms of the architectural aspect. To demonstrate the advancement and novelty of this study, we choose PCA, GS, HIS, PNN, TFNET, MSDCNN, and SRPPNN models as comparison algorithms, respectively. The reconstruction outcomes for the various models are shown in Figure 6. To demonstrate the model's influence on reconstruction, the ground truth picture, the up-sampled MS image, and the fused image are contrasted in the visual resolution. The visual analysis shows that although each of the three machine learning techniques enhance the spatial resolution of MS pictures, they all cause some degree of spectral information loss. The PCA algorithm is the worst, resulting in extreme discoloration of the reconstructed picture; the GS and IHS algorithms have comparable visual consequences, both of which result in certain portions of the fused image being too bright. In contrast, both deep learning methods effectively preserve the  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Indicators**} \\ \cline{2-5}  & **CC** & **SAM** & **RMSE** & **ERGAS** \\ \hline PCA [24] & 0.800 & 2.717 & 5.92 & 7.74 \\ GS [21] & 0.958 & 3.251 & 9.08 & 13.55 \\ his [22] & 0.754 & 1.839 & 6.19 & 8.17 \\ PNN [23] & 0.961 & 1.814 & 1.278 & 2.25 \\ TFNET [24] & 0.962 & 1.727 & 1.226 & 2.12 \\ MSDCNN [25] & 0.941 & 2.350 & 1.949 & 2.88 \\ SRPPNN [26] & 0.960 & 1.819 & 1.322 & 2.16 \\ ours & **0.977** & **1.483** & **1.074** & **1.93** \\ \hline \multicolumn{5}{l}{Higher values of CC, and lower values of SAM, RMSE, and ERGAS indicate good performance. The units of RMSE are \(10^{-2}\).} \\ \end{tabular}
\end{table}
Table 4: Quantitative evaluation of the Botswana dataset.  
\begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Models**} & \multicolumn{4}{c}{**Indicators**} \\ \cline{2-5}  & **CC** & **SAM** & **RMSE** & **ERGAS** \\ \hline PCA [24] & 0.548 & 26.513 & 8.28 & 18.52 \\ GS [21] & 0.658 & 26.178 & 6.71 & 13.41 \\ HFS [22] & 0.672 & 26.566 & 6.75 & 13.64 \\ PNN [23] & 0.970 & 2.778 & 1.523 & 4.41 \\ TFNET [24] & 0.978 & 2.252 & 1.224 & 4.11 \\ MSDCNN [25] & 0.964 & 3.094 & 1.704 & 5.41 \\ SRPPNN [26] & 0.977 & 2.341 & 1.292 & 3.98 \\ ours & **0.981** & **2.219** & **1.223** & **3.85** \\ \hline \hline \end{tabular}
\end{table}
Table 5: Quantitative evaluation of the Chikusei dataset.  
original colors in the MS image. The MSDCNN algorithm's reconstructed pictures are on the bright side, indicating a significant loss of spectral information; TFNET, SRPPNN, and PNN's reconstructed images are blurry, while effectively keeping spectral information. As can be seen from the enlarged figure, our proposed algorithm reduces the loss of spectral information while ensuring spatial resolution.  
In terms of objective evaluation, a quantitative of the performance of different models on the Pavia dataset is presented in this paper, and the results are shown in Table 3. Data analysis reveals that deep learning-based pan-sharpening algorithms outperform conventional pan-sharpening methods. When the processing outcomes of the four deep learning algorithms are compared, the four deep learning methods obtain comparable results in CC metrics. The proposed model performs best compared with the other models. Specifically, the fused images produced by the Swin-MRDB method are significantly higher in SAM, RMSE, and ERGAS than other models, proving that the model can better retain spatial and spectral information. Compared with the best-performing comparative model in the objective evaluation index, the proposed model achieves an 8.4% improvement in SAM, a 13.3% improvement in RMSE, and a 15.3% improvement in ERGAS.