## 4 Experimental Results
### Comparison with Other Methods
This reduces the phenomenon of misclassification and enhances the model's ability to handle complex scenes. The fourth row shows the cloud map segmentation result on farmland. The combination of the multi-scale CAM module and the multi-scale feature enhancement module allows the image to retain the detailed information of different scale targets in edge detail-rich segmentation tasks. This demonstrates the model's robustness in cross-platform data. The visualization results of the above comparative experiments show that our method demonstrates strong generalization ability in both daytime and nighttime images. It can accurately segment clouds under various lighting conditions and with complex backgrounds. At the same time, it has strong anti-noise capabilities and can generate smoother and more coherent segmentation masks. It can effectively preserve the fine morphological features of clouds, including thin cirrus clouds and small cumulus clouds, and accurately capture their complex structures and shape changes. By integrating the multi-scale CAM module and the multi-scale CAM attention U-Net, our method achieves more accurate and detailed cloud segmentation results. In terms of edge accuracy, anti-noise ability, and morphological feature retention, it outperforms other state-of-the-art methods in various imaging conditions. The multi-scale CAM module enhances the model's ability to focus on relevant features, while the attention U-Net improves boundary sensitivity and morphological recognition ability. These innovations make our method more reliable and interpretable in the cloud detection task. Figure 8: Qualitative examples of cloud segmentation on the TCDD dataset. Figure 9: Qualitative examples of cloud segmentation on the HRC_WHU dataset. The results in Tables 1 and 2 show that the proposed method has the highest recall rates in daytime, nighttime, and all-day image segmentation, which are 92.02%, 91.72% and 91.99%, respectively, indicating that it can better detect cloud pixels. The proposed method has the highest F1 scores in daytime, nighttime, and all-day image segmentation, which are 92.22%, 88.69%, and 91.85% respectively, indicating that it has achieved the best balance between the accuracy and recall rate of cloud segmentation. The proposed method has the lowest error rates in daytime, nighttime, and all-day image segmentation, which are 6.38%, 9.21%, and 6.67% respectively, indicating that its segmentation results are the most accurate. The proposed method has the highest MIoU values in daytime, nighttime, and all-day image segmentation, which are 86.72%, 80.39%, and 86.05%, respectively, indicating that its segmentation results exhibit the highest overlap degree with the real labels and the highest segmentation accuracy. The parameter count of the proposed method is 93.23M. Although it is slightly higher than that of some other methods, its performance improvement is significant, indicating that it has higher efficiency and accuracy in the task of cloud image segmentation. The ROC curve (Figure 10) demonstrates the performance of different algorithms in the task of cloud image segmentation. It can be seen from the figure that the ROC curve of the proposed method (ours) is closest to the upper left corner, indicating that it has the highest true positive rate (TPR) and the lowest false positive rate (FPR) in the cloud image segmentation task, and its performance is superior to the other seven algorithms. The AUC value (area under the curve) is an important indicator for measuring the performance of an