Hyperspectral Anomaly Detection via Structured Sparsity Plus Enhanced Low-Rankness

Hyperspectral anomaly detection (HAD), distinguishing anomalous pixels or subpixels from the background, has received increasing attention in recent years. Low-rank representation (LRR)-based methods have also been promoted rapidly for HAD, but they may encounter three challenges: 1) they adopted the nuclear norm as the convex approximation, yet a suboptimal solution of the rank function; 2) they overlook the structured spatial correlation of anomalous pixels; and 3) they fail to comprehensively explore the local structure details of the original background. To address these challenges, in this article, we proposed the structured sparsity plus enhanced low rankness ((SELR)-E-2) method for HAD. Specifically, our (SELR)-E-2 method adopts the weighted tensor Schatten-p norm, acting as an enhanced approximation of the rank function than the tensor nuclear norm (TNN), and the structured sparse norm to characterize the low-rank properties of the background and the sparsity of the abnormal pixels, respectively. To preserve the local structural details, the position-based Laplace regularizer is accompanied. An iterative algorithm is derived from the popular alternating direction methods of multipliers. Compared to the existing state-of-the-art HAD methods, the experimental results have demonstrated the superiority of our proposed (SELR)-E-2 method.

Automated summary

In this article, the structured sparsity plus enhanced low rankness ((SELR)-E-2) method is proposed for hyperspectral anomaly detection (HAD). It adopts a weighted tensor Schatten-p norm and a structured sparse norm to represent the low-rank properties of the background and the sparsity of abnormal pixels, respectively. A position-based Laplace regularizer is also used to preserve the local structural details.