Visual State Space Model for Efficient Semantic Segmentation of Remote Sensing Images

The proposed RS3Mamba model introduces a novel dual-branch architecture that incorporates a Visual State Space (VSS) auxiliary branch to provide additional global information, complementing the convolution-based main branch. A collaborative completion module is further introduced to effectively fuse the features from the two branches, enhancing the representation learning for remote sensing images.