## 4 Discussion
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**lr** & **Method** & **Accuracy** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{\(1\times 10^{-3}\)} & TransUNet & 0.9734 & 0.9484 & 0.9870 & 0.9734 & 0.9865 \\  & SwinUNet & 0.9730 & 0.9384 & 0.9662 & 0.9697 & 0.9678 \\  & U-Net & 0.9878 & 0.9757 & 0.9881 & 0.9874 & 0.9877 \\  & DeeplabV3+ & 0.9885 & 0.9759 & 0.9878 & 0.9878 & 0.9879 \\  & MSACN-small & **0.9907** & **0.9794** & **0.9896** & **0.9896** & **0.9896** \\  & MSACN & 0.9889 & 0.9749 & 0.9884 & 0.9861 & 0.9873 \\ \hline \multirow{8}{*}{\(7\times 10^{-5}\)} & TransUNet & 0.9824 & 0.9656 & 0.9809 & 0.9824 & 0.9811 \\  & SwinUNet & 0.9778 & 0.9491 & 0.9724 & 0.9751 & 0.9737 \\ \cline{1-1}  & U-Net & 0.9662 & 0.9335 & 0.9649 & 0.9664 & 0.9656 \\ \cline{1-1}  & DeeplabV3+ & 0.9886 & 0.9752 & 0.9879 & 0.9870 & 0.9837 \\ \cline{1-1}  & MSACN-small & **0.9907** & **0.9810** & **0.9897** & **0.9913** & **0.9904** \\ \cline{1-1} \cline{2-6}  & MSACN & 0.9872 & 0.9742 & 0.9864 & 0.9874 & 0.9869 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparative experimental results of GF-HWU dataset with different lr (learning rate). \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**lr** & **Method** & **Accuracy** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline \multirow{8}{*}{\(1\times 10^{-3}\)} & TransUNet & 0.9716 & 0.9429 & 0.9712 & 0.9700 & 0.9706 \\  & SwinUNet & 0.9518 & 0.9045 & 0.9508 & 0.9489 & 0.9494 \\  & U-Net & 0.9688 & 0.9384 & 0.9595 & 0.9665 & 0.9628 \\  & DeeplabV3+ & 0.9697 & 0.9393 & 0.9719 & 0.9661 & 0.9743 \\  & MSACN-small & **0.9718** & **0.9450** & **0.9735** & **0.9707** & **0.9717** \\  & MSACN & 0.9635 & 0.9268 & 0.9678 & 0.9575 & 0.9619 \\ \hline \multirow{8}{*}{\(7\times 10^{-5}\)} & TransUNet & **0.9699** & 0.9381 & 0.9685 & 0.9680 & 0.9681 \\  & SwinUNet & 0.9453 & 0.8937 & 0.9450 & 0.9427 & 0.9434 \\ \cline{1-1}  & U-Net & 0.9670 & 0.9353 & 0.9674 & 0.9666 & 0.9666 \\ \cline{1-1}  & DeeplabV3+ & 0.9696 & **0.9409** & **0.9702** & 0.9695 & **0.9703** \\ \cline{1-1}  & MSACN-small & 0.9641 & 0.9278 & 0.9648 & 0.9604 & 0.9625 \\ \cline{1-1}  & MSACN & 0.9691 & 0.9391 & 0.9687 & **0.9684** & 0.9686 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparative experimental results of 38-cloud dataset with different lr (learning rate). resolution images. The lower-resolution satellite images have fewer details, and the shallow network makes it easier to capture the texture features in the image. The 38-cloud where the low-resolution images are located is more susceptible to the impact of the learning rate, so it shows better results at a larger learning rate. Both Tables 5 and 6 illustrate that the optimal models in all cases are based on CNN, so the Transformer may not be able to make full use of its self-attention when processing these images due to the low-resolution remote sensing image datasets with less-detailed mechanism and sequence modeling capabilities. In contrast, CNNs excel at extracting image features and capturing local and global context. The following conclusions can be depicted from the experimental data:  
* For high-resolution complex scenes, there is a certain relationship between model accuracy and attention module;
* The size of lr: (a) When the data are low-resolution images, a large learning rate works better. (b) The learning rate has a greater impact on the fusion attention module, and a small learning rate is better.