## 3 Experiments  
### Datasets  
In this experiment, three datasets were used to evaluate the performance of the network algorithm. All three datasets provide labeled data down to the pixel level, and one is a high-resolution Cloud and Cloud Shadow dataset from Google Earth for comparative experiments. The other two sets are publicly available remote sensing image datasets, HRC_WHU and SPARCS, which are used for generalization experiments. Cloud and Cloud Shadow datasets: The Cloud and Cloud Shadow datasets, which were collected by our research team, focus on plain areas within China and are used to evaluate the core segmentation performance of our method. The majority of the imagery within these datasets is sourced from the QuickBird and WorldView-4 satellites. As presented in Table 1, it details the band information for the QuickBird and WorldView-4 satellite images. To guarantee the credibility of the experimental outcomes, we randomly picked 50 high-resolution remote-sensing images as the initial data. These images are situated in the plain regions of China, and their resolutions span from 0.3 m to 30 m. The selected imagery covers five remotely sensed backgrounds, including cities, farmland, vegetation, water, and wasteland. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Satellite Type** & **Band Number** & **Band Name** & \begin{tabular}{c} **Wavelength** \\ **Range (nm)** \\ \end{tabular} & \begin{tabular}{c} **Spatial** \\ **Resolution (m)** \\ \end{tabular} \\ \hline QuickBird & \begin{tabular}{c} 1 \\ 2 \\ 3 \\ 4 \\ \end{tabular} & \begin{tabular}{c} blue \\ green \\ \end{tabular} & \begin{tabular}{c} 450–520 \\ 520–600 \\ 630–690 \\ 760–900 \\ \end{tabular} & 0.61 \\ \hline WorldView-4 & \begin{tabular}{c} 1 \\ 2 \\ 3 \\ 4 \\ \end{tabular} & \
\begin{tabular}{c} blue \\ green \\ \end{tabular} &
\begin{tabular}{c} 50–510 \\ 510–580 \\ 655–690 \\ 780–920 \\ \end{tabular} & 0.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Specific parameters of satellite imagery for Cloud and Cloud Shadow datasets. Figure 6: Decode. We manually labeled each of the 50 selected images and categorized them into three categories: background, cloud, and cloud shadow. This annotation was carried out by five annotators working in the field of remote sensing imagery. Each annotator tags the image independently. In the event of a disagreement, the commentators discuss it until an agreement is reached. The decision-making process relies on full consistency, which means that the tag needs to be agreed upon by all annotators before it can be added to the label diagram. Figure 7 selects five sets of original images and their labels in different remote sensing backgrounds in the Cloud and Cloud Shadow datasets. Considering that the experiment would be limited by memory, we cropped the original image to a size of \(224\times 224\) pixels and took the RGB three channels. Ultimately, a total of 1916 images underwent cropping and filtering procedures. Out of these, 1545 images were designated for the training phase, while 371 images were earmarked for the testing phase. HRC_WHU: Each image is \(1280\times 720\) pixels in size and consists of RGB three channels with a resolution between 0.5 m and 15 m collected from various locations around the world. The researchers divided the image background into five main land types, namely snow, urban, wasteland, vegetation, and water. Figure 8 shows a set of original images and labels. The cloud areas of the label map are marked in white. Due to memory limitations, we cropped 150 high-resolution images from the HRC_WHU dataset to 3488 images with a size of \(224\times 224\) pixels. The number of training and validation sets is divided into \(4/1\) ratios, with 2791 and 697 images, respectively. SPARCS: The SPARCS dataset was developed and made available by a research group at Oregon State University. It consists of globally distributed Landsat 8 imagery from the years 2013 to 2014.