MFM-TransUNet: Multi-Scale Feature Modeling Network for Remote Sensing-Based Surface Water Extraction

Accurate extraction of surface water is of great significance for water resource management, ecological conservation, and flood disaster monitoring. However, water bodies in high-resolution remote sensing imagery are often obscured by building shadows, vegetation, complex backgrounds, or spectral confusion. Consequently, traditional semantic segmentation methods frequently struggle with global feature modeling and the preservation of boundary details. To address these challenges, this paper proposes MFM-TransUNet, a remote sensing water extraction network based on multi-scale feature modeling. The network employs an encoder–feature modeling–decoder architecture. A Ghost-SE encoder is utilized to extract multi-scale hierarchical features, while a High-Performance Sequence Modeling Module (HPSM) is introduced to aggregate local contextual information from high-level features through depthwise 1D convolutions, with a feed-forward network enhancing nonlinear feature representations along the channel dimension. During the decoding phase, a learnable weight-fusion strategy is applied to balance the contributions of features from different levels, and an Edge-enhanced Feature Attention (EFA) is incorporated to strengthen boundary-related information within the decoded features. The proposed method was evaluated in terms of segmentation accuracy, module ablation, and cross-dataset performance. Experiments on the GID dataset demonstrate that MFM-TransUNet achieves an Overall Accuracy (OA) of 96.27%, a Mean Intersection over Union (Mean IoU) of 89.24%, an F1-Score of 94.20%, and a Boundary IoU of 63.52%, showing competitive performance compared with several mainstream baseline models. Ablation studies evaluate the contributions of the key modules, while experiments on the LJ-Water and Water-CD datasets assess the model’s performance across different datasets. These results suggest that MFM-TransUNet achieves a favorable trade-off between segmentation accuracy, boundary-related performance, and computational cost, providing a potential approach for surface water extraction in complex remote sensing scenarios.