Joint Sparse Tensor Representation for the Target Detection of Polarized Hyperspectral Images

Abstract

Polarized hyperspectral images (PHSIs) possess multidimensional information, including space, spectrum, and polarization, and in the past decades, target detection and recognition for PHSIs have attracted more and more attention. However, most target detection methods of PHSIs are based on the Stokes vector, and derived from the target detection of HSIs, which mainly take advantage of the spectral information and ignore the continuous variability of polarized dimension, being similar to spectrum. Hence, in order to take full advantage of the multidimensional information of PHSIs, we combine tensor decomposition and joint sparse representation, and propose a joint sparse tensor representation (JSTR) method for the target detection of PHSI, which can remove the redundancy and noise, and also realize the joint utilization of spectral, polarized, and spatial information. And the experiments on the PHSI data have validated the practicability and effectiveness of JSTR for the target detection of PHSIs.