## 4 Experimental Results
### Ablation Experiments
In summary, the multi-scale input module enhances the model's ability to handle the variability in cloud textures and boundaries by integrating hierarchical features from different network depths, as demonstrated by the quantitative improvements in key metrics [21]. Incorporating the multi-scale feature extraction module further enhances performance, yielding a recall of 90.55%, an F1 score of 91.63%, an error rate of 6.81%, and an MIoU of 85.87%. The module employs dilated convolutions and residual connections to expand the receptive field without excessive computational overhead, preserving edge details and suppressing noise--critical for segmenting small or semi-transparent cloud structures. In terms of XAI, this module improves the model's ability to provide detailed and noise-resistant representations of cloud features, which is essential for generating reliable and interpretable segmentation results. By leveraging residual learning principles [43], the module ensures both low-level spatial fidelity and high-level semantic abstraction, addressing the gradient decay problem in deeper networks [44]. The most significant performance gains are observed with the addition of the MSA module, achieving a recall of 91.99%, an F1 score of 91.85%, an error rate of 6.67%, and an MIoU of 86.05%. This module dynamically fuses CAM-derived saliency maps with original feature maps through learnable weights, refining attention allocation in cloud regions. The MSA module resolves the misclassification of bright non-cloud objects (e.g., urban areas) [21] by adaptively weighting multi-scale dependencies and suppressing background noise. Within the XAI framework, the MSA module significantly enhances the model's interpretability by adaptively weighting multi-scale dependencies and suppressing background noise. This allows for a clearer distinction between cloud regions and background, making the model's predictions more transparent and trustworthy. Its innovative dual-attention mechanism, which combines spatial priors from CAM with feature-layer attention scores, enhances boundary sensitivity and morphological recognition. \begin{table}
\begin{tabular}{c c c c c} \hline  & **Recall \(\uparrow\)** & **F1 Score \(\uparrow\)** & **Error Rate \(\downarrow\)** & **MIoU \(\uparrow\)** \\ \hline Baseline & 89.35\% & 89.81\% & 8.16\% & 83.11\% \\ + Multi-Scale Input & 89.62\% & 91.40\% & 7.16\% & 85.51\% \\ + Multi-Scale Saliency Attention (MSA) & 90.55\% & 91.63\% & 6.81\% & 85.87\% \\ + Multi-Scale Residual Feature & 91.99\% & 91.85\% & 6.67\% & 86.05\% \\ Aggregation (MRFA) & & & & \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation experiment results. The progressive improvements in Table 4 underscore the synergistic effect of the proposed modules. The multi-scale input lays the foundation for hierarchical feature integration, while the feature extraction module ensures detailed and noise-resistant representations. The MSA module then refines these features through context-aware attention, achieving state-of-the-art segmentation accuracy. Collectively, these modules address the three key limitations of existing CAM methods: (1) reliance on pre-fully connected layer features, (2) lack of multi-scale integration, and (3) poor performance in pixel-level segmentation tasks. By integrating these XAI techniques, our model not only improves segmentation accuracy but also provides a more transparent and interpretable decision-making process, which is crucial for the application of AI in fields requiring high reliability and accountability. The results validate our design choices and highlight the importance of combining multi-scale processing with adaptive attention mechanisms for robust cloud detection. In conclusion, the ablation study demonstrates that each module contributes uniquely to overcoming the challenges of cloud segmentation, with the MSA module playing a pivotal role in achieving superior performance.