Hyperspectral Anomaly Detection via Locally Enhanced Low-Rank Prior
Shaoyu Wang, Xinyu Wang, Yanfei Zhong, Liangpei Zhang
Abstract
Anomaly detection is an active area of research in hyperspectral information processing. Recently, low-rank representation has been applied in hyperspectral anomaly detection. However, the existing low-rank-based methods either involve a complicated dictionary construction process or the anomaly-background separation which is not sufficient. In this article, to solve these problems, a novel hyperspectral anomaly detection method based on a locally enhanced low-rank prior (LELRP-AD) is proposed. This article is inspired by the observation that, in local homogeneous regions, the background signals hold an enhanced low-rank property while the anomalies exhibit spatial sparsity. Based on this observation, the background pixels can be low-rank reconstructed by a set of basis background signals, whereas anomalies can be represented as sparse residuals. First, image segmentation is performed to enhance the homogeneity of the background, in which a Potts-based image segmentation algorithm is adopted with postprocessing, thus avoiding the need for a complicated spectral dictionary for the representation of the background. Furthermore, the original hyperspectral data matrix is augmented with extracted background endmembers for the low-rank and sparse matrix decomposition, to further achieve anomaly-background separation. The experimental results obtained on four real hyperspectral data sets demonstrate the merit and viability of the proposed method compared with the current state-of-the-art methods.