## 5 Conclusions  
Clouds and their shadows have a significant impact on the quality and usability of optical remote sensing images, so cloud detection is a crucial research topic in this field. This paper proposes an enhanced U-Net architecture that effectively improves the segmentation effect of cloud images by combining multi-scale feature input with a multi-scale saliency attention module. The main contributions of this method are threefold: firstly, a multi-scale class activation map (CAM) is proposed, which integrates multi-scale features to capture fine details and broader contextual information; secondly, a U-Net framework based on multi-scale saliency attention is implemented, using multi-scale weighted encoders to enhance the network's ability to extract detailed features; finally, a multi-scale image feature module containing dilated convolutions is introduced to further enhance the model's ability to capture large-scale features.  
During the experimental stage, the model was rigorously trained and tested using four datasets. The experimental results show that this method performs well in the cloud image segmentation task, outperforming existing methods in key metrics such as recall rate, F1 score, error rate, and MIoU. Moreover, the model's parameter count is also reasonably explained. Compared with other models that include class activation maps (CAM), this method's superiority in cloud segmentation is further confirmed. Additionally, the analysis of ROC curves and AUC values once again demonstrates the high accuracy and stability of this method in cloud image segmentation.  
From the perspective of explainable artificial intelligence (XAI), this optimized U-Net architecture provides a transparent and interpretable method for cloud detection. By combining multi-scale CAM and multi-scale saliency attention modules, the model provides a clear basis for its predictions. These modules enable the model to effectively highlight the most influential regions in its decision-making process, thereby improving the model's reasoning process's comprehensibility for researchers and practitioners. This is in line with the goals of XAI, making artificial intelligence systems transparent, interpretable, and trustworthy. The proposed method demonstrates the potential of explainable artificial in telligence in practical optical remote sensing applications. By enhancing the interpretability of the cloud detection model, this study contributes to the broader field of explainable artificial intelligence by applying advanced technologies to practical problems and providing a model for the development of this field. The improvements in accuracy and efficiency, as well as the model's interpretability, make it a valuable tool in various scenarios requiring reliable and interpretable artificial intelligence decisions.  
Conceptualization, Q.X. and Y.C.; methodology, Q.X., Z.Z. and Y.C.; software, Q.X. and Z.Z.; validation, Q.X., Z.Z. and Y.C.; formal analysis, Q.X.; investigation, Q.X.; resources, Q.X., Z.Z. and Y.C.; data curation, Q.X. and Z.Z.; writing--original draft preparation, Q.X., Z.Z. and Y.C.; writing--review and editing, Q.X.; visualization, Q.X.; supervision, Y.C.; project administration, Q.X., G.W. and Y.C. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.