## 2 Material and Methods
### The Performance Metrics of Cloud Detection  
To evaluate the accuracy of cloud masks generated for KOMPSAT-3 and 3A imagery, this study applied a focused set of performance metrics to the test dataset, extending beyond basic accuracy to capture the nuanced aspects of segmentation quality. We calculated the selected metrics, F1-score, and Intersection over Union (IoU), using the true-positive (TP), true-negative (TN), false-positive (FP), and false-negative (FN) values obtained from the pixel-wise comparisons. In this context, TP represents correctly identified cloud pixels,  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Configuration** \\ \hline Learning rate for Adam & 0.0003 \\ Learning rate factor for scheduling & 0.5 \\ Momentum & 0.937 \\ Weight decay & 0.0005 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Hyperparameter range used to select optimized deep learning model structure for cloud detection.  
FP indicates non-cloud pixels misclassified as clouds, TN denotes correctly identified non-cloud pixels, and FN signifies cloud pixels missed by the model.  
\[\text{Accuracy}=\frac{\text{TP}+\text{TN}}{\text{TP}+\text{TN}+\text{FP}+\text{ FN}} \tag{5}\]  
\[\text{Precision}=\frac{\text{TP}}{\text{TP}+\text{FP}} \tag{6}\]  
\[\text{Recall}=\frac{\text{TP}}{\text{TP}+\text{FN}} \tag{7}\]  
\[\text{F1 Score}=\frac{2*\text{precision}*\text{Recall}}{\text{Precision}+ \text{Recall}} \tag{8}\]  
\[\text{IoU}=\frac{\text{TP}}{\text{TP}+\text{FP}+\text{FN}} \tag{9}\]  
These metrics collectively provide a robust framework for assessing the model's performance in cloud segmentation. Accuracy, as shown in Equation (5), offers a baseline measure of the overall correctness across all pixels, while precision and recall, defined in Equations (6) and (7), respectively, break down the model's ability to correctly classify cloud pixels and avoid errors. The F1-score, derived from Equation (8), integrates precision and recall into a single metric, emphasizing the balance between detecting clouds and minimizing false positives--a critical consideration for applications like meteorological analysis where both over- and under-detection can impact outcomes. In contrast, the IoU, as presented in Equation (9), evaluates the spatial overlap between the predicted and actual cloud regions, providing a geometric perspective on segmentation accuracy that is particularly relevant for tasks requiring precise cloud boundary delineation, such as atmospheric correction in satellite imagery processing. To extend this evaluation across the entire test dataset, the mean IoU (mIoU) was introduced, calculated as the average of the individual IoU scores across all the images, as shown in Equation (10).  
\[\text{mIoU}=\frac{1}{\text{N}}\text{$\sum_{i=0}^{n}$ IoU}_{i} \tag{10}\]  
The mIoU complements the IoU by offering a holistic measure of the model's consistency and reliability, capturing its ability to maintain effective cloud detection across the diverse imaging conditions and scene complexities inherent in KOMPSAT-3 and 3A data. By incorporating the mIoU, this study ensures a comprehensive assessment that not only evaluates the localized segmentation quality but also verifies the model's generalizability.