## 3 Experiments
### Generalization Experiments
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **PA (\%)** & **mP (\%)** & **mR (\%)** & **F1 (\%)** & **MIoU (\%)** & **FWiIoU (\%)** \\ \hline Segmenter & 95.21 & 93.52 & 94.70 & 94.10 & 88.94 & 90.96 \\ \hline SegNet & 94.73 & 94.63 & 94.54 & 94.58 & 89.74 & 89.99 \\ \hline PVT & 96.53 & 95.47 & 95.97 & 95.71 & 91.82 & 93.34 \\ \hline U-Net & 95.94 & 95.86 & 95.81 & 95.84 & 92.01 & 92.20 \\ \hline HRNet & 95.97 & 95.87 & 95.86 & 95.87 & 92.07 & 92.27 \\ \hline CvT & 96.68 & 95.94 & 95.87 & 95.91 & 92.18 & 93.59 \\ \hline CMT & 96.73 & 96.01 & 95.76 & 95.99 & 92.31 & 93.70 \\ \hline Mobile-Former & 97.16 & 96.56 & 96.47 & 96.52 & 93.29 & 94.49 \\ \hline FCN8s & 96.87 & 96.75 & 96.84 & 96.80 & 93.80 & 93.94 \\ \hline Swin & 97.45 & 96.97 & 96.85 & 96.91 & 94.03 & 95.05 \\ \hline DeepLabv3+ & 97.04 & 96.91 & 97.02 & 96.97 & 94.11 & 94.26 \\ \hline CSWin & 97.10 & 97.13 & 96.90 & 97.01 & 94.20 & 94.34 \\ \hline Conformer & 97.19 & 97.09 & 97.16 & 97.12 & 94.41 & 94.54 \\ \hline OCRNet & 97.24 & 97.14 & 97.19 & 97.16 & 94.49 & 94.63 \\ \hline PSPNet & 97.25 & 97.16 & 97.21 & 97.18 & 94.53 & 94.65 \\ \hline Our & 97.65 & 97.55 & 97.63 & 97.58 & 95.30 & 95.41 \\ \hline \hline \end{tabular}
\end{table} 
Table 10: Experimental results with different types of segmentation networks on HRC_WHU datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **PA (\%)** & **mP (\%)** & **mR (\%)** & **F1 (\%)** & **MIoU (\%)** & **FwioU (\%)** \\ \hline SegNet & 92.19 & 89.46 & 87.64 & 88.48 & 80.07 & 85.80 \\ \hline Segmenter & 92.70 & 91.72 & 89.82 & 90.68 & 83.17 & 86.46 \\ \hline PVT & 94.30 & 92.79 & 92.18 & 92.96 & 86.96 & 89.32 \\ \hline PSPNet & 95.56 & 94.02 & 92.81 & 93.39 & 87.84 & 91.61 \\ \hline U-Net & 95.48 & 93.92 & 92.92 & 93.40 & 87.91 & 91.47 \\ \hline FCN8s & 95.60 & 94.27 & 92.68 & 93.44 & 87.94 & 91.69 \\ \hline CvT & 95.77 & 94.33 & 93.39 & 93.77 & 88.36 & 92.78 \\ \hline CMT & 95.82 & 94.63 & 93.11 & 93.84 & 88.63 & 92.06 \\ \hline Mobile-Former & 96.13 & 94.48 & 94.25 & 94.36 & 89.51 & 92.66 \\ \hline Swin & 96.25 & 94.51 & 94.60 & 94.55 & 89.83 & 92.89 \\ \hline DeepLabv3+ & 96.43 & 95.21 & 94.16 & 94.67 & 90.05 & 93.18 \\ \hline CSWin & 96.45 & 94.93 & 94.72 & 94.82 & 90.30 & 93.24 \\ \hline Conformer & 96.55 & 95.04 & 94.84 & 94.93 & 90.50 & 93.41 \\ \hline HRNet & 96.60 & 95.15 & 94.90 & 95.03 & 90.67 & 93.51 \\ \hline OCRNet & 96.79 & 95.66 & 94.76 & 95.20 & 90.97 & 93.85 \\ \hline
**Our** & **96.91** & **95.81** & **95.03** & **95.45** & **91.35** & **94.10** \\ \hline \hline \end{tabular}
\end{table} 
Table 11: Experimental results compared with different types of segmentation networks on the SPARCS dataset. Figure 13: Segmentation prediction graphs of different types of networks on the HRC_WHU dataset: (**a**) original graph; (**b**) labels; (**c**) CSWin-T; (**d**) PSPNet; (**e**) Conformer; (**f**) our model. Figure 14 shows the prediction results of the model in this study for five types of segmentation tasks: clouds, cloud shadows, water, snow, and background. In the first row of the cloud and snow scene in Figure 14, the model in this study can accurately restore the complex and changeable boundaries. At the same time, in the scene of water segmentation in the second row of Figure 14, the model in this study can still have high segmentation accuracy in the face of small water areas and slender tributaries. In summary, the model in this study still achieves good segmentation results on other remote sensing datasets, which proves that the model in this study has good robustness.