AutoSAM: Auto-Prompting Mamba-Based Vision Foundation Model for Multimodal Remote Sensing Semantic Segmentation

Vision foundation models such as the Segment Anything Model (SAM) have advanced remote sensing (RS) tasks. However, extending SAM to multimodal remote sensing semantic segmentation faces two key challenges: 1) SAM is tailored for unimodal inputs and lacks RS-specific knowledge, hindering effective spatial modeling and cross-modal feature integration; 2) SAM depends on externally provided prompts (e.g., points or boxes), limiting its scalability and practicality in multimodal scenarios. To address these issues, we present AutoSAM, an end-to-end auto-prompting Mamba-based vision foundation model framework tailored for multimodal remote sensing semantic segmentation. Specifically, we introduce a CrossMamba Block (CMB) in the feature extraction stage to replace the conventional multi-head self-attention mechanism, where the core Reverse Interactive Scanning Adaptor-SS2D Module (RISASM) promotes semantic interaction and alleviates modality discrepancies. Additionally, a Multimodal Scale-Aware Fusion Module (MSAFM) is incorporated to enhance scale-aware fusion and suppress irrelevant features through cascaded residual interactions. Furthermore, we propose a plug-and-play Multimodal Mixture-of-Class-Expert Auto-Prompting Module (MMoEAPM), which enables the generation of pseudo-mask prompts for the original prompt encoder without additional training overhead, thereby supporting efficient auto-prompting. Extensive experiments and ablation studies on four benchmark multimodal remote sensing datasets demonstrate that AutoSAM consistently achieves state-of-the-art performance across diverse modality combinations. The code is available at https://github.com/NWPUFranklee/AutoSAM.git.