## 3 Experiments
### Generalization Experiments  
In order to assess the generalization ability of the model proposed in this study, this subsection conducts comparative experiments on two publicly accessible datasets: the HRC_WHU dataset and the SPARCS dataset. Table 10 shows the experimental outcomes of various network models on the HRC_WHU dataset. Evidently, from the table, the model of this study outperforms other models in terms of recognition accuracy for the single-class cloud segmentation task. Impressively, its Mean Intersection over Union (MIoU) value reached as high as 95.30, demonstrating its superior performance and remarkable effectiveness in precisely segmenting clouds within this specific dataset. Figure 12: Segmentation prediction maps of different types of networks in complex remote sensing backgrounds: (**a**) original image; (**b**) labels; (**c**) CSWin-T; (**d**) OCRNet; (**e**) Conformer; (**f**) our model. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **PA (\%)** & **mP (\%)** & **mR (\%)** & **F1 (\%)** & **MIoU (\%)** & **FWIoU (\%)** \\ \hline SegNet & 95.58 & 94.32 & 94.79 & 94.55 & 89.74 & 91.60 \\ \hline U-Net & 96.46 & 95.51 & 95.84 & 95.67 & 91.74 & 93.20 \\ \hline FCN8s & 96.55 & 95.76 & 95.74 & 95.75 & 91.89 & 93.36 \\ \hline CMT & 96.63 & 95.93 & 95.86 & 95.89 & 92.14 & 93.51 \\ \hline Mobile-Former & 96.83 & 96.28 & 95.94 & 96.12 & 92.55 & 93.86 \\ \hline DeepLabv3+ & 96.85 & 96.09 & 96.18 & 96.14 & 92.59 & 93.91 \\ \hline PSPNet & 96.96 & 96.25 & 96.30 & 96.28 & 92.85 & 94.12 \\ \hline HRNet & 97.09 & 96.16 & 96.74 & 96.45 & 93.17 & 94.38 \\ \hline Conformer & 97.15 & 96.46 & 96.64 & 96.55 & 93.35 & 94.48 \\ \hline OCRNet & 97.30 & 96.63 & 96.80 & 96.71 & 93.66 & 94.76 \\ \hline Ours & 97.51 & 96.98 & 96.86 & 96.97 & 94.13 & 95.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Experimental results are compared with CNN-based and CNN+Transformer segmentation networks on Cloud and Cloud Shadow datasets. Figure 13 shows the comparative segmentation prediction plots of PSPNet, CSWin, Conformer, and the models in this study in five remote sensing backgrounds: town, vegetation, wasteland, snow, and water. Among them, the three networks compared with the models in this study are the models with the highest segmentation accuracy among the three types of networks, CNN, Transformer, and CNN+Transformer. The first and second lines of Figure 13 show that the cloud boundary recognition accuracy of this model is higher than that of the other three models. In the third and fifth rows of Figure 13, the other three models missed the smaller clouds, and our model accurately identified them. The fourth row of Figure 13 vividly reveals that in scenarios where there is a high likelihood of confusion between clouds and snow, this model significantly outperforms other models in terms of segmentation performance. It showcases remarkable resilience and accuracy, adeptly distinguishing between clouds and snow even in such challenging and ambiguous situations. This superior performance highlights the model's robustness and effectiveness in handling complex and difficult segmentation tasks, setting it apart from its counterparts. In order to evaluate the recognition performance of the method presented in this study for more intricate segmentation tasks, we carried out comparative experiments using the SPARCS dataset. The SPARCS dataset is a five-class classification dataset characterized by its diverse and complex remote sensing backgrounds. This richness in backgrounds provides an ideal scenario for thoroughly assessing the model's robustness and generalization capabilities. Table 11 illustrates the experimental outcomes of various network models when applied to the SPARCS dataset. The results of these experiments serve as further evidence of the outstanding performance of the model introduced in this study in the domain of remote sensing image segmentation tasks.