## 1 Introduction
This model, by integrating deep convolution and an extended window-based multi-head self-attention mechanism, reduces the computational burden while enhancing the modeling of long-range dependencies. [PERSON] et al. [38] introduced a Transformer-based cloud detection algorithm known as CSD-Former. Innovatively, this algorithm utilizes a hierarchical Transformer structure as its encoder. By harnessing the multi-head self-attention mechanism, the model is able to capture the long-range dependencies between pixels. As a result, it significantly boosts the global contextual comprehension of the semantic relationships between clouds and shadows, enabling a more comprehensive and in-depth understanding of these elements. With the rapid development of the aforementioned semantic segmentation algorithms, an increasing number of researchers have been applying them to the tasks of cloud segmentation. [PERSON] et al. [39] proposed MAFNet, which extracts features using a combination of ResNet and Transformer. They introduced MAFM and IDAM to enhance small object detection capabilities, while the RGM optimizes boundary segmentation, improving the accuracy of cloud segmentation. The advantages of MAFNet lie in its strengthened feature fusion, enhanced boundary details, and improved small object detection capability. However, it has high computational complexity and still has room for improvement in real-time processing and extreme illumination conditions. [PERSON] et al. [40] came up with an innovative convolutional self-attention feature fusion network built upon a U-shaped structure. This method employs a Channel-Spatial Attention Module (CSAM) to adaptively adjust the receptive field and utilizes a Feature Fusion Module (FFM) to combine upsampled and downsampled features, improving edge detail extraction. However, it still faces challenges in the misclassification of highly reflective surfaces such as snow and sand. [PERSON] et al. [41] introduced a cloud detection method based on TSMM, which effectively removes noise and enhances detection accuracy, demonstrating outstanding performance across multiple datasets. However, this method heavily relies on time-series data and may be less effective in scenarios with limited or missing temporal information. Existing studies have demonstrated that CNNs have strong advantages in local feature extraction [42; 43; 44]. However, due to CNNs' limitations in global feature recognition, their capability remains constrained. Compared to CNNs, Transformers are not restricted by the local constraints of convolution operations and can better capture the overall contextual relationships within an image, thus improving the model's ability to grasp global features. Therefore, integrating CNN and Transformer architectures can fully leverage their respective strengths while compensating for their limitations, enhancing model performance in cloud detection tasks. The main contributions of this paper are as follows:  
1. A novel dual-branch encoding architecture is proposed, combining CNN and Transformer to overcome the limitations of single-backbone models. This architecture incorporates two distinct feature aggregation strategies that effectively fuse multi-level local and global features, thereby enhancing feature representation and segmentation accuracy. 2. A Dual-Branch Lightweight Aggregation Module (DLAM) is designed for the early encoding stage. By learning adaptive fusion weights, DLAM emphasizes local features from CNN and global features from Transformer while suppressing irrelevant features, achieving efficient integration with low computational cost. 3. A Dual-Branch Attention Aggregation Module (DAAM) is developed for the late encoding stage. Leveraging a self-attention mechanism and weight allocation, DAAM enables interaction and complementarity between the dual-branch features, further improving the fusion of global and local information.