Relational Structure-Aware Mamba Network for Semantic Segmentation of Remote Sensing Images

Accurate semantic segmentation of high-resolution remote sensing images (RSIs) requires strong structural awareness and precise boundary delineation, which remain challenging due to complex spatial patterns and semantic ambiguities. To address these issues, we propose ReSMamba, a relational structure-aware segmentation framework based on linear state space modeling. Specifically, we introduce the Structure-Aware Topology Mamba Block (STMamba), which incorporates topological priors into the state space to capture global context and maintain directional consistency across spatial dimensions. A Structure-Aware Semantic Assembly Module (SASA) is further developed, leveraging graph-based feature reconstruction and class-guided semantic prototypes to mitigate feature distortion and reduce inter-class confusion. Additionally, we design a Directional Connectivity Prediction Module (DCPM) to explicitly model and enhance pixel-level connectivity in multiple directions, significantly improving boundary continuity and the recovery of fine structural details. Extensive experiments demonstrate that ReSMamba consistently outperforms state-of-the-art methods, achieving mIoUs of 79.74$\%$, 79.10$\%$, and 60.79$\%$on the ISPRS Potsdam, ISPRS Vaihingen, and LoveDA datasets, respectively. Ablation studies further validate the unique effectiveness and complementarity of each proposed component. These results highlight the strong structural modeling capabilities and practical value of ReSMamba for intelligent remote sensing image interpretation. The code and segmentation results will be released athttps://github.com/darkseid-arch/ReSMamba.