HADDNLP: Hyperspectral anomaly detection via double nonlocal priors

Abstract

Hyperspectral anomaly detection (HAD) is a promising approach that acts as an unsupervised strategy by distinguishing anomalies from the background. Low-rank representation (LRR) based methods that exploit global correlations at the image level are effective for HAD but often fail to capture long-range correlations, resulting in the loss of important structural details. To address the limitation, we develop a novel HAD via double nonlocal priors (HADDNLP) framework that preserves critical background structure. The proposed HADDNLP method first adopts the patch-wise nonlocal low-rank tensor (NLRT) modeling to explore global correlation along spectrum (GCS) and self-similarity (SS) across distant regions in hyperspectral images (HSIs), thereby preserving the structural and contextual details of the background. Then, the nonlocal means (NLM) prior is integrated to maintain spatial distribution within the HSIs, further enhancing the model’s ability to distinguish anomalies from the background. We optimize the model with an alternating minimization (AM) algorithm for NLRT estimation and an alternating direction method of multipliers (ADMM) for joint background reconstruction and anomaly detection. Experimental results on the real satellite and aerial hyperspectral datasets demonstrate that our proposed approach outperforms state-of-the-art methods in the HAD tasks.