DSRNet: Diagonal Subsampling Reconstruction Network for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection seeks to locate pixels in a scene exhibiting substantial spectral discrepancies from the surrounding background pixels, holding essential applications in both civilian and military domains. However, in real-world scenarios, the inherent properties of hyperspectral imaging, the irregular forms of anomalous targets, and the lack of prior information pose significant challenges for anomaly detection methodologies. To address these issues, we first explore a novel reconstruction-based modeling approach for hyperspectral anomaly detection, offering a rational motivation for the modeling approach and a detailed exposition of its effective implementation. Furthermore, we propose a diagonal subsampling reconstruction network (DSRNet) for anomaly detection of hyperspectral data. Specifically, DSRNet consists of a paired training data generation algorithm using subsampling and a self-supervision training process enforced with a reconstruction consistency constraint. The training input is derived by a randomly diagonal averaging subsampler, where training pairs are derived from the same original hyperspectral data. Extensive experiments on four public datasets demonstrate the superiority of our DSRNet compared over several state-of-the-art baselines, with an average AUC score increase of 0.0156.