Self-Adaptive Low-Rank and Sparse Decomposition for Hyperspectral Anomaly Detection

Hyperspectral anomaly detection is a widely used technique for exploring target of interest in hyperspectral images (HSIs). In recent years, the low-rank and sparse-decomposition-based anomaly detection model has attracted extensive attention. However, these models suffer from two main problems. First, it is difficult for them to completely separate the low-rank background and the sparse anomaly. Moreover, the extracted sparse component is inevitably contaminated by noise. Second, the incorporation of various constraints increases the cost of selecting the optimal parameters. To solve the two key problems, we propose a self-adaptive low-rank and sparse decomposition (SLaSD) method for hyperspectral anomaly detection in this article. The proposed method decomposes the sparse (anomaly) part of the HSI through a novel self-adaptive alternating direction method (S-ADM). The noise of the sparse part is suppressed through a dual strategy of integrating guided filter and the difference between the S-ADM-derived sparse features of pixels. The performance of the proposed method is evaluated by comparing with ten state-of-the-art methods using six real HSIs. It is shown that the proposed SLaSD method can produce more accurate detection results than the ten benchmark methods.