DMSA-Unet: a novel method combining dual-channel features and spatial attention modeling for remote sensing object classification

Abstract The significant advancement of modern remote sensing technologies enables the collection of multispectral, hyperspectral, and spatio-temporal data. In earth observation, accurate delineation of features such as shorelines and intertidal zones is crucial for quantitative measurements, involving area estimation and long-term environmental monitoring. However, semantic segmentation (SS) of optical remote sensing images remains challenging because of high intra-class variability, subtle inter-class differences, and complex backgrounds. Thus, an enhanced SS method based on an optimized U-Net architecture was established in our study to address these issues. Specifically, a hybrid feature extraction module was introduced in the encoder to strengthen shallow feature representation. Meanwhile, a multiscale spatial attention module was embedded in the skip connections to adaptively capture spatial dependencies across scales and improve feature fusion. Experiments on the WHDLD and Potsdam datasets demonstrate the effectiveness of the method. This method achieved mIoU scores of 61.33% and 77.47%, with pixel accuracies of 88.02% and 86.72%. Furthermore, the results on the Waterseg dataset confirm its robustness and relevance for accurate geospatial measurements in diverse environments.