Target Detection for Polarized Hyperspectral Images Based on Tensor Decomposition

Abstract

In the past decades, with the development of polarized hyperspectral imaging system, target detection and recognition for polarized hyperspectral images (PHSIs) have attracted more and more attention. Most conventional target detection algorithms of PHSI are based on the Stokes vector, which mainly take advantage of the spectral information, and ignore the continuous variability of polarized dimension, being similar to spectrum. In fact, the PHSI include multidimensional features of polarization, spectrum, space, and radiation, and these provide more discriminable information about target and background than traditional spectrum or intensity ones. Hence, tensor, which can keep the complete information of PHSI, is introduced to represent such high-dimensional data. In this letter, the tensor canonical polyadic (CP) decomposition is adopted to extract the spectral and polarized features. There are two ways to realize the target detection through CP decomposition. One is to construct a fourth-order tensor matched filtering (FTMF), and FTMF is applied to the original data directly without extracting the Stokes vector. The other is to reconstruct the PHSI into a new 3-D data, then matched filter algorithm of hyperspectral image is applied to this data to detect the targets, for short CPMF. The experimental results show that the proposed methods achieve the joint utilization of spectrum and polarization and are more suitable and effective for the target detection of the PHSI.