Selective Prototype Aggregation for Remote Sensing Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) in remote sensing imagery remains challenging due to complex scenes, large image sizes, frequent multi-class coexistence, and significant intra-class variation. These factors often lead to severe overfitting to base classes and hinder generalization to novel classes. To address these issues, we propose a Mamba-Enhanced Transformer framework with Selective Prototype Aggregation (SPA), which simultaneously suppresses base-class interference and enhances novel-class representation. Specifically, the Dynamic Feature Aggregation (DFA) module integrates features from a base-class learner and a meta-learner, effectively enhancing inter-class distinction and mitigating feature contamination in multi-class coexistence scenarios. We further introduce the Mamba-Enhanced Transformer (MET), which combines Mamba blocks and Transformer self-attention to capture long-range dependencies and multi-scale context while maintaining low computational complexity. Mamba blocks independently extract support and query features, preventing cross-branch interference and improving robustness. By selectively aggregating support prototypes with query self-support prototypes, SPA strengthens feature discriminability and leverages query-specific contextual cues to reduce intra-class variance. Our model demonstrates strong performance across multiple datasets, achieves 50.49% mIoU on the iSAID dataset and 30.82% on the LoveDA dataset, representing improvements of 3.95% and 3.89%, respectively, over the Base model. Ablation experiments further validate the superior performance of SPA.