## 1 Introduction
In contrast, gradient-based methods like Fullgrad CAM, Grad-CAM, and Grad-CAM++ produce blurred activations (third and fourth columns), a known limitation attributed to their reliance on gradient computations [22]. Score-CAM (seventh column) and Layer-CAM (sixth column) show fragmented attention distributions, especially in regions with complex cloud textures. These artifacts arise from their dependence on forward-pass scoring or layer-specific activations, which may overlook multi-scale dependencies [27]. For HiResCAM/LayerCAM, while preserving high-resolution details, they fail to integrate global context effectively. While deep learning has demonstrated promising results in ground-based cloud segmentation, significant challenges remain in practical implementations. Current class activation mapping (CAM) techniques exhibit three critical limitations when applied to cloud segmentation tasks. These deficiencies are particularly problematic for astronomical applications, where precise cloud coverage estimation is crucial. U-Net architecture, widely adopted for semantic segmentation tasks, also faces challenges when applied to cloud detection. While its encoder-decoder structure with skip connections effectively captures multi-scale features, standard U-Net variants struggle with fine boundary delineation of semi-transparent clouds and suffer from false positives in heterogeneous landscapes [30]. Recent analyses indicate that these limitations stem from the uniform treatment of features across scales, without adaptive weighting of contextually relevant regions [31]. To address these challenges and improve the interpretability of cloud segmentation models, we propose an enhanced U-Net architecture incorporating multi-scale feature inputs and a novel multi-scale CAM attention mechanism. This approach not only aims to improve the accuracy of cloud segmentation but also seeks to enhance the transparency and interpretability of the model's decision-making process, making it more reliable and trustworthy for operational cloud detection systems. Our principal contributions include the following:* Multi-scale CAM: Multi-scale features are processed through channel cascading and score-based weighting to generate post hoc visualizations that enhance interpretability while maintaining segmentation accuracy. * Multi-scale CAM Attention U-Net: Incorporates a mask attention module that synergistically combines pretrained CAM saliency maps with original feature maps through adaptive weight fusion. This innovation enhances boundary sensitivity and morphological recognition while maintaining feature representation integrity. * Multi-scale Feature Enhancement: Augments the model's receptive field through dilated convolutions in residual separation modules, significantly improving large-scale feature extraction capability while reducing the semantic gap between skip connections and upsampled features. By integrating these XAI techniques into the cloud segmentation framework, we aim to provide a more interpretable and reliable solution for ground-based cloud detection, which is essential for advancing research and applications in astronomical observations and solar forecasting.