## 4 Discussion  
In this section, in order to assess the adaptability of MSACN, we further discuss the results of training and validation from other satellites of two different sensors, Landsat OLI 8 and GF-1. From the results, we try to discuss the relationship between the resolution of satellite images and the depth of CNN. For the test image collected from Lansat OLI 8, we present the comparative experimental results using different methods, shown in Table 5. Overall, the results suggest that MSACN-small stands out in achieving a balance between accuracy, IoU, and precision-recall trade-offs. In contemplating the observed results, it is plausible that the 30m resolution of Landsat OLI 8 imagery might be a contributing factor. Lower resolutions often necessitate less complex neural networks to achieve satisfactory results. Additionally, as depicted in Table 5, for lower-resolution images, various cloud extraction methods exhibit minimal discrepancies in their outcomes. MSACN-small achieved the highest accuracy (97.18%), closely followed by TransUNet (97.16%). MSACN-small exhibits the highest IoU (94.50%), indicating superior spatial overlap between predicted and ground truth cloud images. TransUNet and DeeplabV3+ also show competitive IoU values, reflecting their strong segmentation performance. Meanwhile, MSACN-small consistently performs well, balancing precision and recall effectively. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Backbone** & **Heads** & **Accuracy** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline \multirow{3}{*}{VGG16} & 4 & 0.9724 & 0.9317 & 0.9679 & 0.9612 & 0.9645 \\  & 8 & 0.9684 & 0.9267 & 0.9643 & 0.9592 & 0.9617 \\ \cline{1-1}  & 4 & 0.9667 & 0.9283 & 0.9693 & 0.9569 & 0.9626 \\ \cline{1-1}  & 8 & **0.9857** & **0.9510** & **0.9761** & **0.9737** & **0.9748** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Accuracy evaluation of different network structure complexities and different backbones. In Table 6, we present the comparative experimental results for the GF1-HWU dataset, evaluating various cloud-extracting methods. Across all evaluated metrics, the cloud extraction methods exhibit consistently high performance. MSACN-small stands out with the highest scores, indicating robust performance in terms of accuracy, IoU, and precision-recall balance. In the GF-1 dataset, both TransUNet and SwinUNet exhibit oscillating results with comparatively lower IoU scores of 94.84% and 93.84%, respectively. Contrarily, conventional CNN methods like U-Net and DeeplabV3+ exhibit more stable and robust outcomes in the given context. The MSACN series methods consistently showcase outstanding performance across different datasets. This dual accomplishment not only affirms the effectiveness of the method but also indicates a certain level of robustness in its methodology. To assess the performance of MSACN at different learning rates and the linear relationship between the learning rate and the image resolution, we try to use different learning rates for experimental comparison. In many experiments on the 38-cloud dataset, by comparing the model indicators in the table, it can be seen that the accuracy of the large learning rate is higher. In particular, the lr has a great effect on the Transform-based model. In the CNN-based model, MSACN-small, which removes the low-level convolution-pooling block, can explore the relationship between network structure and image resolution. In the GF-WHU dataset, regardless of the lr, the accuracy of MSACN-small is 0.29% higher than that of U-Net. It can be seen that the GF-WHU dataset works better in the shallow network. The 38-cloud dataset is affected by the hyper-parameter and learning rate, and the shallow network MSACN-small only performs well in large learning rates. This is all due to the fact that the 38-cloud dataset and the GF-WHU dataset have the characteristics of spatial