Lateral-Slice Sparse Tensor Robust Principal Component Analysis for Hyperspectral Image Classification

Abstract

This letter proposes a lateral-slice sparse tensor robust principal component analysis (LSSTRPCA) method to remove gross errors or outliers from hyperspectral images so as to promote the performance of subsequent classification. The LSSTRPCA assumes that a three-order hyperspectral tensor has a low-rank structure, and gross errors or outliers are sparsely scattered in a 2-D space (i.e., lateral-slice) of the tensor. It formulates a low-rank and sparse tensor decomposition problem into a convex problem and then implements the inexact augmented Lagrange multiplier method to solve it. The experiments on two hyperspectral data sets show that the LSSTRPCA can successfully remove outliers or gross errors and achieve higher accuracies than both the original robust principal component analysis (RPCA) and tensor robust principal component analysis (TRPCA).