## 3 Experiments
### Ablation Study
insufficient regularization, while too large a value suppresses learning capacity and slows convergence. This experiment validates the robustness of our loss formulation and justifies the default choice of \(\lambda=1\) in our method. To further verify the effectiveness of the auxiliary losses, we conducted an ablation experiment by removing all four auxiliary supervision branches and keeping only the main loss. As shown in Table 7, removing the auxiliary losses leads to a noticeable drop in both MIoU and F1 score, confirming their positive contribution to model performance. The ablation experiments systematically validate the contributions of the proposed dual-branch aggregation modules, DLAM and DAAM, to the overall model performance. Compared with directly merging features from both branches, the DLAM module, through its lightweight weighting mechanism, effectively enhances the quality of shallow feature fusion. This verifies its innovative advantage in maintaining feature independence while balancing local and global information. The DAAM module serves as the core fusion unit for deep features, leveraging a bidirectional self-attention mechanism to enable in-depth interaction between CNN and Transformer features, significantly improving the model's capability in delineating cloud and cloud shadow boundaries. This study is the first to propose and systematically evaluate a two-module staged fusion strategy, combining residual connections and concatenation operations, which substantially optimizes the expressive power of fused features and stabilizes network training. The ablation results demonstrate that this novel design significantly improves key metrics such as MIoU, confirming its practical value and theoretical significance in cloud detection. Compared with existing related fusion strategies, our method achieves performance gains while maintaining computational efficiency, showing excellent engineering applicability. In addition, a sensitivity analysis on the L2 regularization parameter was conducted to further understand its impact on model convergence and segmentation performance. Results indicate that moderate L2 regularization values (e.g., \(\lambda=0.1\) or \(1\)) strike a balance between preventing overfitting and maintaining sufficient learning capacity, leading to faster convergence and better segmentation metrics. Too small a regularization value results in under-regularization and slower convergence, whereas excessively large values suppress learning and degrade accuracy. This analysis validates the robustness of our loss formulation and supports the default choice of \(\lambda=1\) in the training process, contributing to the overall effectiveness and stability of the model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Lambda (\(\lambda\))** & **F1 Score (\%)** & **MIoU (\%)** & **Convergence Epoch** \\ \hline
0 & 96.25 & 92.90 & 30 \\
0.01 & 96.70 & 93.60 & 27 \\
0.1 & 96.90 & 93.95 & 25 \\
1 & 96.97 & 94.13 & 23 \\
10 & 96.40 & 93.30 & 32 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance under different values of \(\lambda\) on the Cloud and Cloud Shadow datasets. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Setting** & **F1 Score (\%)** & **MIoU (\%)** \\ \hline With auxiliary losses (ours) & 96.97 & 94.13 \\ Without auxiliary losses & 96.12 & 93.20 \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Effect of auxiliary losses on model performance on Cloud and Cloud Shadow dataset.