## 2 Methodology  
This study innovatively adopts a dual-branch encoding architecture, constructing separate pathways based on ResNet and CSWin Transformer for local and global feature extraction, respectively. This fully leverages the advantages of both network types and overcomes the limitations of single-backbone models in capturing local details and global context. Unlike previous works that often employ serial or simple fusion strategies, the proposed dual-branch feature aggregation modules--namely the Dual Lightweight Aggregation Module (DLAM) and the Dual Attention Aggregation Module (DAAM)--are designed with differentiated structures for different encoding stages to maintain the relative independence of branch features while enabling efficient and refined fusion. Specifically, the DLAM module adaptively balances local and global information in shallow features through a lightweight weighting mechanism, reducing computational complexity and making it suitable for processing high-resolution inputs. The DAAM module employs a bidirectional self-attention mechanism to facilitate complementarity between deep features, enhancing feature richness and discriminative power. This staged and modular fusion strategy innovatively addresses the performance bottleneck caused by excessive coupling in prior fusion methods, improving sensitivity to complex cloud and cloud shadow patterns. Compared to existing approaches such as Conformer and Mobile-Former, this design demonstrates notable advantages in fusion strategy and network architecture. Moreover, the model incorporates multi-auxiliary supervision branches that effectively mitigate training difficulties in deep networks, ensuring stable and balanced feature learning. This provides strong technical support for cloud detection in remote sensing images. As is vividly depicted in Figure 1, the comprehensive framework of the model, which plays a crucial role in achieving its designed functions, is compartmentalized into an encoding stage, during which initial data processing and feature extraction take place, and a decoding stage,where the processed information is transformed back into a form that can be utilized for the final output. The coding part includes a CNN backbone, a Transformer backbone, and a dual-branch aggregation module. Similar to Transformer framework, CNNs have four stages, each with an output feature map resolution that drops to half that of the previous stage, while increasing the number of channels to twice that of the previous stage. The disparities in features of Convolutional Neural Networks (CNNs) and Transformers across various stages, the model is equipped with two dual-branch aggregation modules, namely the Dual-branch Lightweight Aggregation Module (DLAM) and the Dual-branch Attention Aggregation Module (DAAM). Both aggregation modules are used to more efficiently fuse feature information from two different backbones. The first two coding stages of the project involve the use of dual lightweight modules to fuse the features of Transformer and CNN. Before feature fusion, the module assigns its respective fusion weights to the output features of two different backbones to achieve the purpose of effective aggregation. A dual-branch attention aggregation module is utilized to integrate the feature information from the dual backbones during the final two encoding stages. Prior to the feature integration process, this module makes use of the self-attention mechanism to amalgamate the output features of the CNN and the Transformer. By doing so, it capitalizes on the strong local representation capabilities of the CNN and the powerful global representation capabilities of the Transformer. During the decoding phase, we embark on a step-by-step process to restore the resolution of the feature maps. At the same time, we engage in the seamless integration of the aggregated features from the dual branches across multiple hierarchical levels.