Hyperspectral Anomaly Detection via Global and Local Joint Modeling of Background

Abstract

Anomaly detection is a hot topic in hyperspectral signal processing. The key point of hyperspectral anomaly detection is the modeling of the background. In this paper, we propose a novel anomaly detection method via global and local joint modeling of background. Based on the observation that the local three-dimensional patch belonging to the background in hyperspectral image (HSI) usually lies in a low dimensional manifold, we propose to reconstruct the background part of a HSI from its subsample by scalable low dimensional manifold modeling (SLDMM). Thus, the background of HSI can be well characterized in both global and local aspects. Taking into consideration that the SLDMM reconstructs the background part at a low sampling ratio, we propose a multiple random sampling reconstruction strategy to further improve the detection accuracies and robustness. The final background is generated by the mean of backgrounds reconstructed from the multiple random sampling and the anomalies are contained in the residual between the observed HSI and the mean background. Experimental results on three real datasets demonstrate that the proposed anomaly detection method outperforms other state-of-the-art hyperspectral anomaly detection methods.