CL-Mamba: Continual Learning Mamba Sub-Networks for Robust Cross-Domain High Efficient Hyperspectral Anomaly Detection

Bio-inspired continual learning draws inspiration from biological neural systems that continuously acquire new knowledge while retaining previously learned information. In particular, the Drosophila mushroom body employs parallel and compartmentalized neural pathways to encode complementary information and reduce interference across experiences. Inspired by this mechanism, we investigate whether a collaborative multi-learner architecture can benefit hyperspectral anomaly detection (HAD) in continuously changing scenes, where models must adapt to new background distributions while avoiding catastrophic forgetting and excessive storage of historical samples. Therefore, we propose CL-SSMamba, a bio-inspired continual learning framework built upon multiple Spatial-Spectral Mamba (SSMamba) subnetworks for open-scenario hyperspectral anomaly detection (OHAD). CL-SSMamba employs parallel SSMamba learners to collaboratively reconstruct the hyperspectral background while retaining only model parameters from previous tasks, thereby reducing storage overhead and alleviating catastrophic forgetting. Each learner integrates Global Spatial Mamba (GSM) and Local Spectral Mamba (LSM) to capture long-range spatial dependencies and cross-band spectral correlations, together with orthogonal spectral correlation enhancement to suppress redundant responses. To alleviate background–anomaly imbalance, Simple Linear Iterative Clustering (SLIC) and Local Density Peak Clustering (LDPC) are introduced to enhance spatially coherent background structures and improve background–anomaly separation. In addition, reconstruction, ensemble cooperation, and continual-learning losses are jointly optimized to promote inter-learner consistency and preserve historical knowledge. Experiments on the Abu-Beach-Urban and HAD100 datasets demonstrate that CL-SSMamba effectively adapts to sequential hyperspectral scenes without replaying historical samples, providing a robust solution for OHAD in continuously changing environments.