## 4 Experiments and Discussion
### Ablation Study  
We conducted ablation studies on the ISPRS Vaihingen and Potsdam datasets to demonstrate the rationality and effectiveness of each module of our proposed MSCSA-Net. The listed results for each category are shown in terms of the F1-score. **Analysis of results for ISPRS Vaihingen. ** Table 1 lists the segmentation results for the test set with the Vaihingen dataset. In the first row, the results were obtained by using ResU-Net, and these results were used as the baseline results. The second row shows the results of our proposed model without any of our designed attention modules (no LCSA, no MSA, no 16-LCSA). Compared with the results shown in the first row, one can see that the F1-scores for five categories (improvious surfaces, buildings, low vegetation, trees, and cars) were all higher than ResU-Net. The m-F1-score and mIoU were also better than ResU-Net. This means we could simply use our MSCSA-Net without attention modules to achieve better results than ResU-Net. Then, we adopted any two of the attention modules in our proposed model. The third row shows the results for our proposed model without the LCSA module. The fourth row shows the results for our proposed model without the MSA module. The fifth row shows the results for our proposed model without the 16-LCSA module. These results were indeed better than for ResU-Net. Among these three methods, the MSCSA-Net without the 16-LCSA module had the best performance, and it had m-F1 = 81.9 and mIoU = 69.4. This shows that one can use two of our designed attention modules, LCSA and MSA, and achieve higher m-F1-score and mIoU. The sixth and seventh rows show the results for our proposed MSCSA-Net model with MSA channel reduction rates of \(d=4\) and \(d=1\), respectively. It can be seen that MSCSA-Net (\(d=4\)) had the second best m-F1-score (82.4) and the second best mIoU (70.1). MSCSA-Net (\(d=1\)) had the best m-F1-score (83.0) and the best mIoU (70.9). We also show the visualization results for these methods with the ISPRS Vaihingen dataset. As can be seen in Figure 8, our proposed MSCSA-Net (\(d=4\)) and MSCSA-Net (\(d=1\)) both had better segmentation results and were more similar to the ground-truth (GT) segmentation results. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{\begin{tabular}{c} **Params** \\ **(M)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Impervious** \\ **Surfaces** \\ **(F1)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Buildings** \\ **(F1)** \\ \end{tabular} } & \multicolumn{2}{c}{**Low**} & \multirow{2}{*}{**Trees (F1)**} & \multirow{2}{*}{**Cars (F1)**} & \multirow{2}{*}{**m-F1**} & \multirow{2}{*}{**mIoU**} \\  & & & & & & & & \\ \hline ResU-net (baseline) [16] & 39.1 & 72.1 & 76.0 & 53.9 & 67.3 & 39.4 & 67.1 & 50.5 \\ MSCSA-Net without attention & 41.0 & 84.5 & 86.6 & 69.9 & 70.9 & 68.7 & 80.8 & 67.8 \\ MSCSA-Net without LCSA & 45.3 & 81.9 & 87.4 & 65.4 & 77.5 & 25.1 & 77.8 & 63.6 \\ MSCSA-Net without MSA & 83.3 & 80.5 & 86.3 & 63.5 & 75.8 & 11.9 & 76.1 & 61.5 \\ MSCSA-Net without 16-LCSA & 87.2 & 85.4 & 90.1 & 71.0 & 81.6 & 60.4 & 81.9 & 69.4 \\ MSCSA-Net (\(d=4\)) & 87.4 & 85.4 & 90.2 & 72.3 & 82.5 & **62.5** & 82.4 & 70.1 \\ MSCSA-Net (\(d=1\)) & 91.7 & **85.7** & **90.5** & **74.2** & **82.7** & 61.8 & **83.0** & **70.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Ablation study with the Vaihingen dataset. The F1-score was used as the evaluation metric for each category. The overall results were evaluated using the m-F1-score and mIoU. The bold number means the best performance among all methods. Figure 9 shows the curve maps for our training and validation with the ISPRS Vaihingen dataset, where we can see that we chose the weight with the highest validation accuracy and number of epochs to apply in the test and output the result. Figure 8: Visualization results with the Vaihingen dataset. **Analysis of results for ISPRS Potsdam dataset.