## 4 Experimental Results
### Ablation Experiments  
To enhance feature representation in cloud segmentation tasks, we strategically integrate multi-scale saliency attention across diverse architectural paradigms. In pure convolutional networks (U-Net, Att-UNet, and UNeXt), multi-scale saliency attention is embedded within the bottleneck layers to establish non-local dependencies for advanced semantic features while enhancing the focus on the target region. For transformer-based \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & \multicolumn{4}{c}{**TCDD**} & \multicolumn{4}{c}{**HRC\_WHU**} \\ \hline
**Method** & **Recall \(\uparrow\)** & **F1 Score \(\uparrow\)** & **Error Rate \(\downarrow\)** & **MioU \(\uparrow\)** & **Recall \(\uparrow\)** & **F1 Score \(\uparrow\)** & **Error Rate \(\downarrow\)** & **MioU \(\uparrow\)** \\ \hline U-Net & 87.29\% & 83.22\% & 9.30\% & 75.81\% & 88.59\% & 91.36\% & 6.06\% & 84.36\% \\ Att-UNet & 85.57\% & 81.80\% & 8.75\% & 77.15\% & 89.65\% & 91.90\% & 5.71\% & 85.34\% \\ Swin-UNet & 85.96\% & 82.87\% & 8.96\% & 75.12\% & 90.93\% & 91.81\% & 5.64\% & 85.32\% \\ UNeXt & 85.15\% & 80.96\% & 8.66\% & 75.17\% & 89.38\% & 89.92\% & 6.85\% & 82.08\% \\ MA-SegCloud & 86.72\% & 80.74\% & 11.80\% & 73.41\% & 90.49\% & 91.95\% & 5.68\% & 85.34\% \\ Laplacian & 83.33\% & 80.14\% & 9.60\% & 73.77\% & 83.39\% & 84.98\% & 9.98\% & 75.23\% \\ CSWin-UNet & 83.95\% & 80.69\% & 8.48\% & 74.27\% & 89.12\% & 92.23\% & 5.23\% & 85.88\% \\ Rolling-Unet & 87.41\% & 82.67\% & 9.30\% & 74.93\% & 88.26\% & 90.38\% & 6.73\% & 82.91\% \\ Ours & **88.20\%** & **83.71\%** & **7.38\%** & **82.69\%** & **92.12\%** & **92.60\%** & **5.15\%** & **86.12\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Performance comparison between our algorithm and the other eight cloud image segmentation algorithms on the TCDD and HRC_WHU datasets. Bold indicates the best option. Figure 10: ROC curve of our method and seven other algorithms. frameworks (e.g., Laplacian), we replace the attention module in the bottleneck layer of the original model with multi-scale CAM-guided attention, enabling the model to prioritize semantically salient regions during token mixing. The unified design leverages CAM-derived spatial priors to refine both local (convolutional) and global (transformer) feature aggregation, addressing limitations of fixed-receptive-field operations in heterogeneous cloud morphology segmentation. Comparative analysis of Figures 11 and 12 reveals distinct performance characteristics across architectures. Att-UNet demonstrates compromised edge precision in nocturnal scenes (error rate: 12.65% vs. daytime 8.17%), particularly for stratocumulus formations, while maintaining acceptable daytime performance for dense clouds. The Laplace method achieves superior daytime accuracy (89.37% recall) but struggles with nocturnal thin cirrus detection (76.69% recall), where its physical assumptions break down. U-Net variants exhibit systematic limitations across all conditions, with 15.53% error rates in nighttime scenarios due to inadequate small-scale feature retention. Notably, CAM integration universally enhances performance--improving Att-UNet's nocturnal recall by 4.63% and reducing Laplace's boundary errors by 0.51%, though computational overhead increases proportionally. The proposed architecture uniquely maintains consistent accuracy across illumination conditions (day-night recall variance: 0.3% vs. competitors' 8-12%), demonstrating particular advantages in cumulus-sparse regions and twilight transitions. The segmentation experiment results in Figure 11 show that our method performs best in nighttime image segmentation. It can accurately segment the main regions of clouds and capture the details and edge information of clouds well, and the segmentation results at cumulus and stratocumulus clouds are especially smooth and accurate. The results (Figure 12) highlight improvements in thin cloud segmentation and boundary sensitivity compared to baseline and CAM-enhanced variants (e.g., Att-UNet+CAM, U-Net+CAM).