Yuhao Zhang

Semantic segmentation of high-resolution remote sensing (HRRS) imagery is a fundamental task in Earth observation but remains challenging due to severe scale variations and complex boundary structures. Although State Space Models (SSMs), particularly Mamba, have shown strong potential for efficient global modeling with linear complexity, existing methods are still limited in capturing multi-scale context and fine-grained spatial details. To address these issues, we propose a novel framework named WASP-Mamba for HRRS image segmentation. A Mamba-based spatial pyramid pooling module is introduced at the encoder bottleneck to enhance multi-scale feature aggregation and improve robustness to scale variations. In addition, a wavelet-inspired Mamba decoder is designed to decouple and reconstruct low-frequency semantic information and high-frequency details, mitigating boundary degradation caused by conventional upsampling. Extensive experiments on the LoveDA, ISPRS Vaihingen and ISPRS Potsdam datasets demonstrate that the proposed method achieves 53.90%, 84.32% and 87.06% mIoU, respectively. Compared with recent state-of-the-art methods, WASP-Mamba achieves superior segmentation performance.