## 2 Material and Methods
### The Network Structure of the Deep Learning Model  
Clouds in satellite imagery have amorphous, unclear shapes that differ significantly from typical objects. These characteristics make cloud detection difficult and highlight the need for pixel-wise labeling, which is widely used in image segmentation [21; 23]. Deep learning has significantly advanced cloud detection networks. Researchers have moved from early CNN-based segmentation methods to more sophisticated architectures, including ResNet-based and U-Net-based models, which leverage large parameter sets to effectively handle the complex nature of clouds [27; 28]. Among these, advanced models like CPNet and DeepLabV3+ have shown notable performance [29; 30]. CPNet, also known as Cloud-Net, builds on the U-Net framework and integrates ResNet to improve gradient flow and feature extraction. It uses an encoder-decoder architecture specifically designed for cloud detection [31].  
Figure 4: The selected sample images of the channel combination of the KOMPSAT-3 and 3A multispectral images for cloud masks with deep learning.  
Figure 3: Data augmentation techniques were applied with the following parameters: horizontal flip (50% probability); vertical flip (50% probability); Shift, Scale, and Rotate (50% probability); Random Brightness (50% probability); and contrast adjustment (30% probability).  
In contrast, researchers widely use DeepLabV3+ because of its strong performance in image segmentation. It originally employs Xception as its encoder instead of ResNet, thereby reinforcing the concept that channel and spatial correlations can be considered separately [32]. However, this experiment showed that the Xception module was less effective at capturing the amorphous and variable nature of cloud pixels, leading to the adoption of a deeper ResNet101 module as the encoder. ResNet101 utilizes residual learning through skip connections and a bottleneck architecture that applies 1 \(\times\) 1, 3 \(\times\) 3, and again 1\(\times\)1 convolutions to effectively regulate the number of input channels, thereby reducing the parameter count and enhancing both computational efficiency and stability.  
DeepLabV3+ employs an Atrous Spatial Pyramid Pooling (ASPP) module to effectively extract multi-scale features while preserving spatial resolution [30].  
\[\mathrm{y_{r}[i,j]}=\sum\mathrm{m}\sum\mathrm{n}\mathrm{x[i+r\cdot m,j+r\cdot n ]\cdot w_{r}[m,n]} \tag{1}\]  
where there are the following:  
* \(y_{r}[i,j]\): the output value at coordinate \((i,j)\) after applying atrous convolution;
* \(x[i+r\cdot m,j+r\cdot n]\): the pixel value in the input feature map, sampled at intervals defined by the atrous rate \(r\);
* \(w_{r}[m,n]\): the weights of the convolutional filter (kernel);
* \(r\): the atrous rate, which determines the spacing between the sampled pixels;
* \(m,n\): the indices of the kernel dimensions.  
For instance, setting \(r=1,2,4\) progressively expands the receptive field, enabling the network to capture both fine details and broader contextual information.  
The ASPP module utilizes multiple parallel atrous convolutions with different dilation rates to capture features at various resolutions. In addition, a global context is captured via average pooling followed by a 1\(\times\)1 convolution as IN (2).  
\[y_{pool}=Conv_{1\times 1}(AvgPool(x)) \tag{2}\]  
The model generates the final ASPP output by concatenating the outputs from atrous convolutions with different rates (e.g., \(r=1,6,12,18\)) along with \(y_{pool}\) and processing the result through another 1\(\times\)1 convolution:  
\[y_{ASPP}=Conv_{1\times 1}\Big{(}Concat\Big{(}y_{1},y_{6},y_{12},y_{18},y_{pool} \Big{)}\Big{)} \tag{3}\]  
DeepLabV3+ ResNet101's encoder-decoder structure preserves positional information, which is crucial for semantic segmentation tasks. By aggregating multi-scale features, ASPP enables the network to interpret the image from diverse perspectives, making it particularly effective for detecting clouds of varying sizes and shapes in satellite imagery.