Mamba-Enhanced Background Suppression Diffusion Model for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection (HAD) faces a significant challenge in separating scarce, small and subtle anomalous targets from complex backgrounds. For typical HAD approaches, background suppression is realized by building statistical or representational models. In contrast, leveraging generative models to actively erase background hyperspectral signal remains underexplored. To address the gap, this paper proposes the Mamba-enhanced background suppression diffusion model (MBSDM). The core of our approach is the integration of Mamba into a diffusion-based framework. Specifically, Mamba is employed to capture the complex global spatial-spectral features of the background. These deep features then guide the reverse diffusion process to treat the background as noise and eliminate it, thereby highlighting the anomalous targets. Furthermore, to prevent anomalies from disrupting the background learning process, we propose a new loss function designed to mitigate the training bias introduced by anomalous pixels. We compare MBSDM with other methods on multiple public datasets, and the results show that our method achieves superior and robust detection performance.