MCAMamba: Multilevel Cross-Modal Attention-Guided State-Space Model for Multisource Remote Sensing Image Classification

Effective fusion of multi-source remote sensing data remains a fundamental challenge for Earth observation, as CNN and Transformer models suffer from limited receptive fields and high computational complexity. While State Space Models (SSM) like Mamba show promise in sequence modeling, they face three critical challenges in multi-source remote sensing: insufficient spatial-spectral coordination, cross-modal heterogeneity, and inadequate multi-scale feature integration. To address these limitations, this paper proposes MCAMamba: a Multi-Level Cross-Modal Attention-Guided Mamba framework for joint classification of hyperspectral images (HSI) and Light Detection and Ranging (LiDAR)/Synthetic Aperture Radar (SAR) data. MCAMamba introduces a novel three-stage feature fusion pipeline: 1) The FExt-Attention module enhances spatial structure and spectral information through parallel spatial-channel attention mechanisms. 2) The SSM-Attention module achieves deep cross-modal fusion by combining attention mechanisms with SSM for parametric interaction. 3) The FFus-Attention module performs adaptive multi-scale feature integration through global context modeling and cascaded attention. This hierarchical design enables superior feature representation with enhanced computational efficiency. Experiments on four public benchmark datasets (Houston2013, Houston2018, Augsburg, and Berlin) show that MCAMamba achieves Overall Accuracy (OA) of 94.75%, 93.35%, 92.46%, and 79.18%. The code will be available at https://github.com/Dmygithub/MCAMamba.