Hyperspectral Anomaly Detection via Merging Total Variation Into Low-Rank Representation

Abstract

Anomaly detection (AD) aiming to locate targets distinct from the surrounding background spectra remains a challenging task in hyperspectral applications. The methods based on low-rank decomposition utilize the inherent low-rank characteristic of hyperspectral images (HSIs), which has attracted great interest and achieved many advances in recent years. In order to fully consider the characteristics of HSIs, more appropriate constrains need to be added to the low-rank model. However, there are too many regularizations and mutual constraints between regularizers, which would result in a reduction in detection accuracy, while an increasing number of tradeoff parameters complicates parameter tuning. To address the above problems, we propose a novel method based on merging total variation into low-rank representation (MTVLRR) for hyperspectral AD in this article, using a regularizer to reflect the low-rankness and smoothness of the background component of HSIs simultaneously, which can significantly decrease the mutual influence of regularizers and the difficulty of parameter tuning. Experimental results on both simulated and real hyperspectral datasets demonstrate that the proposed MTVLRR has an excellent AD performance in terms of detection accuracy compared with other state-of-the-art methods.