PSNet: A Universal Algorithm for Multispectral Remote Sensing Image Segmentation

Semantic segmentation, a fundamental task in remote sensing, plays a crucial role in urban planning, land monitoring, and road vehicle detection. However, compared to conventional images, multispectral remote sensing images present significant challenges due to large-scale variations, multiple bands, and complex details. These challenges manifest in three major issues: low cross-scale object segmentation accuracy, confusion between band information, and difficulties in balancing local and global information. Recognizing that traditional remote sensing indices, such as the Normalized Difference Vegetation Index and the water body index, reveal unique semantic information in specific bands, this paper proposes a feature-decoupling-based pseudo-Siamese semantic segmentation architecture. To evaluate the effectiveness and robustness of the proposed algorithm, comparative experiments were conducted on the Suichang Spatial Remote Sensing Dataset and the Potsdam-S Aerial Remote Sensing Dataset. The results demonstrate that the proposed algorithm outperforms all comparison methods, with average accuracy improvements of 80.719% and 77.856% on the Suichang and Potsdam datasets, respectively.