Dual-Branch Transformer–CNN Fusion for Enhanced Cloud Segmentation in Remote Sensing Imagery

Cloud coverage and obstruction significantly affect the usability of remote sensing images, making cloud detection a key prerequisite for optical remote sensing applications. In existing cloud detection methods, using U-shaped convolutional networks alone has limitations in modeling long-range contexts, while Vision Transformers fall short in capturing local spatial features. To address these issues, this study proposes a dual-branch framework, TransCNet, which combines Transformer and CNN architectures to enhance the accuracy and effectiveness of cloud detection. TransCNet addresses this by designing dual encoder branches: a Transformer branch capturing global dependencies and a CNN branch extracting local details. A novel feature aggregation module enables the complementary fusion of multi-level features from both branches at each encoder stage, enhanced by channel attention mechanisms. To mitigate feature dilution during decoding, aggregated features compensate for information loss from sampling operations. Evaluations on 38-Cloud, SPARCS, and a high-resolution Landsat-8 dataset demonstrate TransCNet’s competitive performance across metrics, effectively balancing global semantic understanding and local edge preservation for clearer cloud boundary detection. The approach resolves key limitations in existing cloud detection frameworks through synergistic multi-branch feature integration.