Hyperspectral Anomaly Detection Via Deep Prior Mamba Network

This paper presents a novel hyperspectral anomaly detection method based on a deep prior Mamba network (DPMN), which is primarily composed of two components: abundance generation module (AGM) and background reconstruction module (BRM). Specifically, the AGM employs convolutional techniques to extract local information and bidirectional Mamba to capture global information. The BRM then uses a background dictionary to divide the background into multiple subspaces. By combining the background dictionary with the generated abundance, we can realize accurate background reconstruction while effectively suppressing the interference of anomalies on background reconstruction. Moreover, to fully leverage the intrinsic properties of hyperspectral images (spatial smoothness and low-rankness), we incorporate a regularization term into the loss function, merging total variation (TV) into low-rank representation (LRR). This approach not only utilizes spatial smoothness and low-rankness but also reduces the number of hyperparameters. Experimental results on two real hyperspectral datasets demonstrate that the proposed method significantly outperforms other state-of-the-art (SOTA) methods in detection performance.