## 4 Conclusions  
This paper presents a novel dual-branch encoding feature aggregation network specifically designed for cloud and cloud shadow detection in remote sensing images. By integrating convolutional neural networks (CNNs) and Transformer architectures through two innovative modules--the Dual-branch Lightweight Aggregation Module (DLAM) and the Dual-branch Attention Aggregation Module (DAAM)--the proposed method effectively overcomes the limitations of existing single-backbone and naive fusion approaches. DLAM efficiently fuses shallow local and global features with low computational cost, while DAAM leverages bidirectional self-attention to facilitate deep feature interactions, significantly enhancing the model's ability to distinguish complex cloud and cloud shadow patterns. Extensive experiments across multiple high-quality datasets demonstrate that the proposed model achieves superior segmentation accuracy and robustness compared to state-of-the-art CNN-based, Transformer-based, and hybrid networks. The ablation studies validate the effectiveness of the two-stage fusion strategy and highlight the importance of the adaptive weighting mechanisms and auxiliary supervision branches in improving training stability and generalization. Furthermore, the analysis of L2 regularization parameters provides practical insights into optimizing model convergence and performance, underscoring the methodological rigor of the proposed approach. Importantly, this work advances the field by offering a scalable and computationally efficient framework that balances the performance of the proposed method.  
Figure 16: Model complexity vs. mIoU comparison on the SPARCS dataset. (**a**) The result of MIoU for different method’s parameters; (**b**) The result of MIoU for different method’s FLOPs.  
Figure 15: Model complexity vs. MIoU comparison on the HRC_WHU dataset. (**a**) The result of MIoU for different method’s parameters; (**b**) The result of MIoU for different method’s FLOPs.  
ances local detail preservation with global context modeling, addressing a critical challenge in remote sensing cloud detection.  
In future research, we plan to integrate the refined cloud and shadow masks generated by our model into downstream applications such as land cover classification, vegetation monitoring, and environmental change detection. This integration will enable a comprehensive assessment of the practical benefits and limitations of the proposed method in real-world remote sensing workflows.  
Conceptualization, N.S., H.J. and M.X.; methodology, N.S., H.J. and H.L.; software, H.J. and N.S.; validation, H.J. and H.L.; formal analysis, H.J.; investigation, H.J. and N.S.; resources, M.X.; data curation, H.J.; writing--original draft preparation, H.J. and N.S.; writing--review and editing, H.L.; visualization, M.X.; supervision, M.X.; project administration, M.X.; and funding acquisition, M.X. All authors have read and agreed to the published version of the manuscript.  
This work is supported by the undergraduate Innovative program (202411030) and the National Natural Science Foundation of PR China (42075130).  
Not applicable.  
The data and the code of this study are available from [[https://github.com/RenHongjin6/DFANet](https://github.com/RenHongjin6/DFANet)]([https://github.com/RenHongjin6/DFANet](https://github.com/RenHongjin6/DFANet)) accessed on 30 May 2025.  
The authors declare no conflicts of interest.