## 3 Experiments and Results
### Experiments Analysis
based:TransUNet and 1.32% higher than the CNN-based:U-Net; the IoU ratio of TransUNet is 5.32% higher and 2.35% higher than U-Net. The qualitative and quantitative analysis reveals that MSACN demonstrates excellent performance and yields satisfactory extraction results in complex scenes characterized by significant variations in cloud distribution and content. The pre-trained weights of ResNet50 are used for transfer learning, and the model undergoes improvement through fine-tuning so that the model can swiftly adapt and converge in the domain of cloud extraction from satellite imagery. In addition to the numerical results in the table above, for the case of cloud coverage under complex backgrounds, three remote sensing cloud coverage images with different characteristics are selected from the ZY-3 satellite images for analysis. These images have different degrees of cloud cover and background, and the experimental results of MSACN are demonstrated in Figures 7-9. The figures below illustrate the raw satellite image, and the outputs of TransUNet, SwinUNet, U-Net, DeeplabV3+, MSACN-small, MSACN and Ground Truth, respectively. Upon visual inspection of the results, the result of MSACN is closer to the labeled image, where white represents cloud pixels. \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Image** & **Method** & **Accuracy** & **IoU** & **Precision** & **Recall** & **F1** \\ \hline \multirow{6}{*}{Image1} & TransUNet & 0.9171 & 0.9033 & 0.6792 & 0.9024 & 0.7751 \\  & SwinUNet & 0.9306 & 0.9217 & 0.8159 & 0.7256 & 0.7681 \\  & U-Net & 0.9508 & 0.8159 & 0.8867 & 0.7905 & 0.8358 \\  & DeeplabV3+ & 0.9504 & 0.9400 & 0.7993 & **0.9172** & 0.8542 \\  & MSACN-small & 0.9387 & 0.9297 & 0.8026 & 0.8129 & 0.8077 \\  & MSACN & **0.9641** & **0.9580** & **0.9210** & 0.8617 & **0.8904** \\ \hline \multirow{6}{*}{Image2} & TransUNet & 0.9246 & 0.8968 & 0.8637 & 0.8914 & 0.8773 \\  & SwinUNet & 0.9319 & 0.9088 & 0.9321 & 0.8357 & 0.8812 \\  & U-Net & 0.9401 & 0.9197 & 0.9575 & 0.8392 & 0.8944 \\  & DeeplabV3+ & 0.9505 & 0.9322 & 0.9392 & 0.8944 & 0.9162 \\  & MSACN-small & 0.9487 & 0.9297 & 0.9328 & 0.8949 & 0.9135 \\  & MSACN & **0.9594** & **0.9441** & **0.9575** & **0.9061** & **0.9311** \\ \hline \multirow{6}{*}{Image3} & TransUNet & 0.8675 & 0.7517 & 0.9068 & 0.8147 & 0.8582 \\  & SwinUNet & 0.8880 & 0.7763 & 0.9223 & 0.8307 & 0.8741 \\ \cline{1-1}  & U-Net & 0.8764 & 0.7574 & 0.9267 & 0.8057 & 0.8619 \\ \cline{1-1}  & DeeplabV3+ & 0.8995 & 0.7925 & 0.8851 & **0.9390** & 0.9113 \\ \cline{1-1}  & MSACN-small & 0.7070 & 0.7851 & 0.9128 & 0.8488 & 0.8796 \\ \cline{1-1}  & MSACN & **0.9238** & **0.8446** & **0.9334** & 0.9274 & **0.9304** \\ \hline \end{tabular}
\end{table} 
Table 3: Comparison of various verification indicators between MSACN and each model on the same cloud dataset. Figure 7: Experimental outputs of Cloud Extraction Results using Different models in Image1 (local enlarged in the red rectangle). **(a)** Raw image; **(b)** TransUNet; **(c)** SwinUNet; **(d)** U-Net; **(e)** DeeplabV3+; **(f)** MSACN-small; **(g)** MSACN; **(h)** Ground Truth. As can be seen in Figure 7, clouds cover a snowy mountain. The reflection between clouds and snow is very similar, and the extraction of clouds in satellite images is more susceptible to interference from such high-reflectivity ground objects such as snow or ice. Figure 7h shows the results of MSACN. Compared with the Transformer-based methods in Figure 7b,c and the CNN-based approaches in Figure 7d,e, MSACN is shown in Figure 7g. In the red rectangle area, MSACN can clearly distinguish clouds from the complex background containing snow. From this point of view, MSACN has excellent cloud detection capabilities and can resist the interference of snow factors in complex environments. From the data indicators of image1 in Table 3, we can see that the accuracy of MSACN is 4.7% higher than TransUNet and 1.33% higher than U-Net; the F1 of MSACN is 12.23% higher than SwinUNet with Swin Transformer as the backbone, and higher than U-Net is 5.46% higher.