## 3 Experiments
### Comparative Experiments
but also significantly bolsters the accuracy of segmentation, leading to more refined and accurate delineation of objects in the segmentation task. However, PVT did not recognize the cloud shadow of the white frame in Figure 10d, indicating that it is still insufficient in local expression. Although CvT takes into account some characteristics of CNN, the segmentation prediction inside the white box in Figure 10e still has a high error, because the addition of a convolution operation alone cannot fully exploit the advantages of CNN in local expression. Although Swin Transformer and CSWin Transformer further optimize the problem of large computational effort in image segmentation and detection tasks of the Transformer architecture, the segmentation effects in the white boxes in Figure 10f,g show that their ability to recognize clouds and cloud shadow boundaries and discrete and subtle cloud shadows is still limited. However, the model proposed in this study incorporates a dual-branch structure integrating CNN and Transformer. This design endows the model with the merits of both local and global feature representation capabilities. Consequently, as demonstrated in Figure 10h, its segmentation performance outshines that of previous models, presenting more accurate and detailed segmentation results. Since the experimental model in this study adopts a dual-branch parallel network structure of CNN and Transformer, we need to consider the parameters and computation of the model due to the limitations of computer equipment. From the data in Table 8, it can be seen that CSWin Transformer can solve the problem of excessive storage of Transformer in image segmentation and take into account the segmentation accuracy, so we selected CSWin-T as the benchmark Transformer backbone of the model in this study. Figure 11 show the segmentation effects of CSWin-T, ResNet-50, and the two-stem model proposed in this study on cloud remote sensing images. The segmentation effect in the white frame in Figure 11c shows that CSWin-T is not accurate enough in terms of cloud-shadow boundary, while Figure 11d shows that ResNet-50 is prone to pixel-level misjudgment, especially in the part framed by the white frame. This is due to the fact that ResNet-50 focuses too much on local expression and cannot grasp the segmentation effect from the overall target, so it is easily disturbed by the remote sensing background. As can be seen from Figure 11e, the model in this study performs better than CSWin-T in edge and detail processing, and better than ResNet-50 in global segmentation. This finding serves as compelling evidence that  
Figure 10: Segmentation prediction graph compared to the model of the Transformer architecture: (**a**) original image; (**b**) labels; (**c**) Segmenter; (**d**) PVT; (**e**) CvT; (**f**) Swin Transformer; (**g**) CSWin Transformer; (**h**) our model. the model presented in this study has effectively harnessed the complementary strengths of the CNN's prowess in local feature representation and the Transformer's proficiency in global feature extraction. By seamlessly integrating these two distinct yet powerful capabilities, the model has managed to strike an optimal balance between capturing fine-grained local details and comprehending the overarching context. Consequently, in the challenging realm of remote-sensing segmentation of clouds, the model has demonstrated remarkable performance. It not only accurately delineates the boundaries of clouds and their corresponding shadows but also discerns subtle variations within these elements, resulting in highly precise and reliable segmentation outcomes. In the above, we compare the semantic segmentation methods based on the Transformer framework and evaluate their model performance and segmentation performance in cloud detection.