RC-Mamba: A Group-Conditional Bayesian Risk-Controlled Mamba for Multi-Source Remote Sensing Classification

Multi-source remote sensing classification integrates hyperspectral, SAR, and LiDAR observations for accurate land-cover recognition. Recent state-space models (SSMs) with multi-scale Mamba fusion have achieved competitive accuracy, yet they remain purely discriminative and lack distribution-free reliability under spectral-spatial non-stationarity, cross-modal heterogeneity, and class imbalance. To address this problem, we propose RC-Mamba, a reliability-calibrated Mamba framework for multi-source remote sensing classification. RC-Mamba couples a multi-scale Mamba backbone with three reliability modules: Group-Conditional Bayesian Risk Thresholding (G-BRT), which learns group-aware risk thresholds beyond a single global threshold; Threshold-Coupled Margin Regularization (TCMR), which injects these thresholds into discriminative training to enlarge confidence margins of critical samples; and Risk-Aware Selective Refinement (RASR), which refines only calibrated high-risk samples at test time. Experiments on Berlin, Augsburg, Houston2013, and Houston2018 show that RC-Mamba improves the reproduced MSFMamba baseline by $0.08$–$0.84$ OA points over three seeds while exposing a group-aware reliability interface for independent-reference evaluation. The audited reliability diagnostic further shows why marginal coverage alone is insufficient and motivates explicit worst-group evaluation. These results establish RC-Mamba as a risk-aware SSM framework for multi-source remote sensing imagery.