A Multi-Scale Attention U-Net for Water Body Extraction Under Urban Shadow Interference

With the continuous improvement of spatial resolution in remote sensing imagery, the representation of ground object details has been significantly enhanced. However, shadows generated by complex objects such as buildings and vegetation have become increasingly prominent, severely interfering with water body spectral characteristics and posing challenges for the automatic extraction of narrow and fragmented water bodies. To address shadow interference, blurred boundaries, and multi-scale water body recognition in high-resolution remote sensing images, this paper investigates water body extraction based on a commonly used dataset in water body shadow interference research. On the basis of the U-Net architecture, this paper proposes a Multi-Scale Attention U-Net (MSA-UNet) by integrating multi-scale feature extraction and attention mechanisms. The proposed model enhances the representation of water bodies at different scales through multi-scale feature fusion, while the attention mechanism emphasizes discriminative water body features and suppresses shadow-induced interference, thereby improving segmentation accuracy under complex background conditions. Experimental results demonstrate that MSA-UNet achieves performance comparable to or superior to mainstream semantic segmentation networks, including U-Net, U2-Net, and TransUNet, in terms of Overall Accuracy (OA), F1-score, Intersection over Union (IoU), and the Kappa coefficient. These results confirm the effectiveness and robustness of the proposed method for high-precision water body extraction under shadow interference conditions.