## 4 Experiments and Discussion
### Ablation Study
** Table 2 shows the segmentation results for the test images of the Potsdam dataset. In the first row, the results were obtained from implementing ResU-Net, and these results were used as the baseline results for comparison. The second row shows the results for our proposed model without any of our designed attention modules (no LCSA, no MSA, no 16-LCSA). We compared the results in the first and second rows, and it can be seen that the F1-scores for the five categories (impervious surfaces, buildings, low vegetation, trees, and cars) were all much higher than ResU-Net. The m-F1-score was 80.3, higher than the ResU-Net m-F1-score (63.6), and the mIoU was 67.1, also better than ResU-Net (46.7). This means that one can just use our MSCSA-Net without attention modules and achieve much better results than ResU-Net with the Potsdam dataset. Then, we compared our model using any two of the attention modules. 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 also much better than the results with ResU-Net. Among these three methods, MSCSA-Net without the 16-LCSA module also had the best performance and its m-F1-score could reach 82.4 and the mIoU could reach 70.0. This demonstrates that we can only adopt two of our \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multirow{2}{*}{**Params**} & \multirow{2}{*}{\begin{tabular}{c} **Impervious** \\ **(M)** \\ \end{tabular} } & \multirow{2}{*}{\
\begin{tabular}{c} **Buildings** \\ **Surfaces** \\ **(F1)** \\ \end{tabular} } & \multirow{2}{*}{
\begin{tabular}{c} **Low Vegetation** \\ **(F1)** \\ \end{tabular} } & \multirow{2}{*}{**Trees (F1)**} & \multirow{2}{*}{**Cars (F1)**} & \multirow{2}{*}{**m-F1**} & \multirow{2}{*}{**mIoU**} \\ \hline ResU-net (baseline) [16] & 39.1 & 70.8 & 67.3 & 53.7 & 48.6 & 22.9 & 63.6 & 46.7 \\ MSCSA-Net without attention & 41.0 & 84.5 & 86.6 & 69.9 & 70.9 & 68.7 & 80.3 & 67.1 \\ MSCSA-Net without LCSA & 45.3 & 81.9 & 87.4 & 65.4 & 77.5 & 25.1 & 80.3 & 67.0 \\ MSCSA-Net without MSA & 83.3 & 80.3 & 84.4 & 65.4 & 67.2 & 61.0 & 76.7 & 62.2 \\ MSCSA-Net without 16-LCSA & 87.2 & 85.7 & 89.2 & 71.9 & 73.4 & 77.0 & 82.4 & 70.0 \\ MSCSA-Net (\(d=4\)) & 87.4 & **87.0** & 89.1 & 73.3 & **75.2** & **80.8** & 83.2 & 71.2 \\ MSCSA-Net (\(d=1\)) & 91.7 & 86.9 & **90.0** & **74.3** & 74.2 & 80.6 & **83.7** & **71.9** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation study with the Potsdam 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: ISPRS Vaihingen dataset: training and validation curve maps. designed attention modules, the LCSA and MSA modules, to achieve a higher m-F1-score and mIoU. The sixth and seventh rows show the results for our proposed model MSCSA-Net 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 (83.2) and the second best mIoU (71.2). MSCSA-Net (\(d=1\)) had the best m-F1-score (83.7) and the best mIoU (71.9). However, MSCSA-Net (\(d=4\)) had the best F1-scores in three categories: impervious surfaces, trees, and cars. This means that using higher channel reduction rates (\(d=4\)) can help our model make better judgments about small objects, such as trees and cars. We also show the visualization results for these methods with the ISPRS Potsdam dataset. As can be seen in Figure 10, 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. Figure 10: Visualization results for the Potsdam dataset.