Hyperspectral Anomaly Detection Based on Low Rank and Sparse Tensor Decomposition

Abstract

Anomaly detection has become a hot topic in hyperspectral image (HSI) processing. Both spatial and spectral features have been proven to be very important for accurate and efficient hyperspectral anomaly detection. The traditional HIS anomaly detection algorithms usually reshape HSI to a matrix, which destroy spatial or spectral structure. In this paper, we propose a novel method of hyperspectral anomaly detection based on LOW RANK AND SPARSE TENSOR DECOMPOSITION (LRASTD). Taking into consideration that HSI data can be essentially regarded as a three-order tensor. HSI is modeled as a background tensor and a sparse anomalies tensor. A tensor nuclear norm is employed to constrain the core tensor, which be designed to characterize the low dimensional structure of the core tensor. Furthermore, a novel sparse tenor norm is proposed to constrain the anomaly targets. Experiments on both simulated and real hyperspectral data sets demonstrate the efficiency and effectiveness of the proposed method.