## 3 Experiments
### Ablation Study
experimental results are shown in Table 5. The third and fifth rows show that the DRAM with the residual structure has a 0.55% higher MIoU than the DLAM without the residual structure. The experimental data proves the effectiveness of the residual structure on DLAM, which is mainly due to the fact that the residual connection can accelerate the convergence of the network and improve the training effect of the model. Above this study, Figure 4b shows the internal structure of a DAAM. Specifically, DAAM has two branches: the upper branch integrates local features from CNN into the output feature map of the Transformer, and the lower branch integrates the global features from the Transformer into the output feature map of the CNN. The data in the first three rows of Table 5 show that the MIoU of DAAM models with only lower and upper branches are 93.44% and 93.61%, respectively, which are lower than those of DAAM models with both upper and lower branches. This suggests that CNN and Transformer can improve the performance of the model by complementing each other's feature expressions. Once the fused features of the upper and lower branches are acquired, there exist two distinct approaches, namely \"Concat\" and \"Add\", for further integrating these features. The data in the third and fourth rows vividly illustrate the performance of the models utilizing these two fusion methods. Notably, the Mean Intersection over Union (MIoU) value of the model adopting the \"Add\" method is 0.3 units lower than that of the model implementing the \"Concat\" method. This clear disparity strongly indicates that within the DAAM architecture, the \"Concat\" fusion approach demonstrates a superior performance compared to the \"Add\" fusion approach. The underlying cause of this performance divergence can be attributed to the fundamental differences in how these two operations handle feature integration. Specifically, the \"Concat\" operation combines the fused features of the CNN and Transformer along the channel dimension. This strategic concatenation effectively safeguards and retains the original characteristic information of both components, allowing for a more coherent and intact representation of the features. On the contrary, the \"Add\" operation merges the two sets of features through element-wise addition. This process, while seemingly straightforward, has the potential to muddle and obscure the distinctiveness between local and global feature information, thereby resulting in a less optimal performance in terms of segmentation accuracy, as reflected by the MIoU metric. Based on the above, we finally determined the optimal structure settings of DLAM and DAAM through the ablation experiments of different internal structures. To assess the influence of the L2 regularization strength \(\lambda\) on model performance and convergence, we conducted a sensitivity analysis on the Cloud and Cloud Shadow datasets. We tested five values of \(\lambda\): \(\{0,0.01,0.1,1,10\}\) and recorded final F1 scores, Mean Intersection over Union (MIoU), and convergence epochs. As shown in Table 6, a moderate value of \(\lambda\) (e.g., 0.1 or 1) leads to better convergence speed and improved segmentation performance. In contrast, too small a \(\lambda\) results in \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**DLAM** & & **DAAM** & & **MIoU (\%)** \\ \hline \begin{tabular}{c} **Residual** \\ **Connections** \\ \end{tabular} & **Upper Branch** & \
\begin{tabular}{c} **Lower** \\ **Branches** \\ \end{tabular} &
\begin{tabular}{c} **Fusion** \\ **Methods** \\ \end{tabular} & \\ \hline \(\checkmark\) & & \(\checkmark\) & Concat & 93.44 \\ \hline \(\checkmark\) & \(\checkmark\) & & Concat & 93.61 \\ \hline \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & Concat & 94.13 \\ \hline \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & Add & 93.28 \\ \hline \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & Concat & 93.58 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Ablation results of DLAM and DAAM with different internal structure settings.