## 3 Experiments
### Comparative Experiments  
In the comparative experiment, we evaluated and contrasted the performance of several prevalent segmentation networks that utilize the Transformer backbone on the Cloud and Cloud Shadow dataset. The outcomes of the experiment are illustrated in Table 8. Among these networks, the Schizont is a semantic segmentation network characterized by its pure Transformer architecture, and the backbone of it is Vision Transformer (ViT), which still uses the Transformer form in the decoding stage [49]. Compared with ViT, PVT (Pyramid Vision Transformer) adopts a pyramid architecture similar to that of CNN, which enables it to reduce computational complexity in image segmentation and detection tasks [50]. The Convolutional vision Transformer (CvT) adds some convolution operations on the basis of ViT, that is, convolutional token embedding before each stage and convolutional projection is added inside each self-attention module, so that the Transformer can take into account the advantages of CNN [51]. Based on PVT, Swin Transformer proposed a window attention mechanism by means of a sliding window, and the computational complexity is reduced from a quadratic relationship with the size of the input image to a linear one [36]. The CSWin Transformer, building upon the Swin Transformer, replaces the window-based self-attention mechanism with a cross-shaped window attention mechanism. Additionally, it employs convolutional operations for the embedding of image patches, which brings about certain improvements and novel features in its architecture [46]. As is evident from Table 8, the segmentation precision of clouds for the model presented in this study surpasses that of the Transformer model. Figure 10 shows the comparative segmentation of the above model, where red, green, and black represent clouds, cloud shadows, and backgrounds, respectively. As can be seen from the cloud shadows marked in the white box in Figure 10c, the segmenter segmentation is rough and the error is large, because the network of the Transformer architecture needs to be trained on a large dataset to learn the inductive bias like CNN, and our self-made Cloud and Cloud Shadow datasets of more than 2000 is not enough to train a pure Transformer model. Apart from the constraints imposed by the dataset, the segmenter's substantial quantity of parameters and computational load pose significant challenges. Consequently, we were compelled to restrict the training to merely 32 image patches. This limitation has severely hindered the network's performance in the intricate tasks of segmenting clouds, leading to suboptimal results and a noticeable gap in its ability to accurately distinguish between these elements. Figure 10d shows that the PVT segmentation performance is better than that of the segmenter, because the PVT adopts a pyramid architecture, which can produce progressive feature maps. The computational cost of the PVT model has been slashed, further accentuating the benefits of multi-scale feature representation. Simultaneously, with the reduction in computational complexity, the PVT model becomes feasible to adopt smaller image patches. As a result, this not only streamlines the computational process \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **PA (\%)** & **mP (\%)** & **mR (\%)** & **F1 (\%)** & **MIoU (\%)** & **FWIoU (\%)** \\ \hline Segmenter & 96.68 & 93.56 & 92.87 & 93.20 & 87.39 & 89.97 \\ \hline PVT & 95.54 & 94.12 & 94.88 & 94.49 & 89.63 & 91.53 \\ \hline CvT & 96.27 & 95.25 & 95.65 & 95.44 & 91.33 & 92.84 \\ \hline Swin & 96.65 & 95.98 & 95.87 & 95.93 & 92.20 & 93.55 \\ \hline CSWin & 96.81 & 96.06 & 96.17 & 96.11 & 92.55 & 93.84 \\ \hline Our & 97.51 & 96.98 & 96.86 & 96.97 & 94.13 & 95.15 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experimental results are compared with the segmentation network based on CNN and CNN+Transformer on Cloud and Cloud Shadow datasets.