## 4 Experimental Results
### Comparison with Other Methods  
This section presents a visual comparison of cloud segmentation results between the proposed method (Figure 6) and several state-of-the-art approaches, including U-Net [41], Att-UNet [42], Swin-UNet [43], UNeXt [44], MA-SegCloud [45], Laplacian [46], CSWin-UNet [47], and Rolling-Unet [48]. We evaluate performance on both daytime and nighttime images from the SWIMSEG/SWINSEG datasets, focusing on edge precision, noise robustness, and morphological detail preservation. Figure 7 offers a visual comparison of cloud segmentation outcomes from various state-of-the-art methods on daytime and nighttime images sourced from the SWIM-SEG/SWINSEG datasets. The evaluation centers on edge precision, noise resilience, and retention of morphological details. Results indicate that our proposed method surpasses other leading approaches in multiple critical aspects. It exhibits a marked advantage in defining cloud boundaries with greater precision and localization accuracy. Unlike other techniques, which can produce indistinct or incomplete edges, our method maintains clear delineation, particularly in complex cloud formations and areas of varying cloud density. The segmentation results of the TCDD dataset (as shown in Figure 8) reveal that our model demonstrates certain advantages in the case of class imbalance (first row) and rich texture (second and third rows). Specifically, in the segmentation of the sparse small cloud patches (the first row), most models failed to accurately restore the cloud area at the lower left corner of the image. The red boxes highlight regions where baseline methods struggle with texture-rich scenarios. For instance, models like U-Net and Att-UNet show fragmented or incomplete segmentation of thin cloud strips, while CSWin-UNet tends to over-smooth these detailed structures. Our multi-scale CAM attention mechanism specifically addresses this limitation by preserving multi-scale texture information through hierarchical feature fusion. Our proposed multi-scale CAM module can dynamically adjust the model's focus in the case of class imbalance through its adaptive weight fusion mechanism. This allows the model to better capture the subtle features of sparse small cloud patches, avoiding local over-segmentation and omission. From the edge segmentation results in the second and third rows, it can be seen that when the imbalance of categories and the rich edge texture occur simultaneously, the multi-scale CAM attention U-Net, with its enhanced boundary sensitivity and morphological recognition capabilities, effectively addresses the issue of \"discontinuous segmentation of thin cloud strips\" commonly found in traditional models. For example, when processing thin cloud strips, the model leverages the attention weights from the CAM module to enhance the expression of edge features. It combines shallow edge detection features with deep semantic features to determine whether the thin strip belongs to the cloud body, thereby achieving continuous and clear segmentation of thin cloud strips. Figure 6: Structures of multi-scale residual feature aggregation (MRFA) module. Figure 7: Qualitative examples of cloud segmentation on SWIMSEG and SWINSEG datasets. As can be seen from the segmentation results on the HRC_WHU high-resolution dataset (Figure 9), the first and second rows represent barren and urban scenes, respectively. In the segmentation results, we use a set of transposed rectangular boxes to display. When the edge detail textures have directional characteristics, our multi-scale feature enhancement module, which utilizes dilated convolutions in residual separation modules, effectively captures the directional edge details. This enables the model to restore the image details well in these complex scenes. The third row shows that when the segmentation target is thin clouds and the non-target area has complex, detailed structures, our multi-scale CAM module can effectively distinguish the target from the background through its feature decoupling mechanism.