Orthogonal Subspace Projection Using Data Sphering and Low-Rank and Sparse Matrix Decomposition for Hyperspectral Target Detection

Abstract

Orthogonal subspace projection (OSP) has been widely used in many applications for hyperspectral data exploitation. However, its performance is sensitive to its used prior target knowledge, which is significantly affected by target background (BKG). To resolve this issue, this article develops three approaches to extend OSP in improving its performance. One is data sphering which can suppress BKG via removing the first- and second-order data statistics. Another takes advantage of a recently developed low-rank and sparse matrix decomposition (LRaSMD) to separate BKG and target signal sources in two subspaces characterized by the low-rank matrix and sparse matrix, respectively, and then annihilates BKG via the low-rank matrix, referred to as BKG-annihilated OSP (BA-OSP). A third approach combines data sphering and LRaSMD to further improve OSP over target detectability and BKG suppressibility. Experiments show that implementing OSP in conjunction with data sphering and LRaSMD significantly improves OSP in target detection and BKG suppression, and also performs as well as a widely used constrained energy minimization (CEM)-based subpixel target detection.