Comparison between constrained energy minimization based approaches for hyperspectral imagery

Abstract

Constrained Energy Minimization (CEM) has been widely used for target detection in hyperspectral remote sensing imagery. It detects the desired target signal source using a unity constraint while suppressing noise and unknown signal sources by minimizing the average output power. Base on the design CEM can only detect one target source at a time. In order to simultaneously detect multiple targets in a single image, several approaches are developed, including Multiple-Target CEM (MTCEM), Sum CEM (SCEM) and Winner-Take-All CEM (WTACEM). Interestingly, the sensitivity of noise and interference seems to play a role in the detection performance. Unfortunately, this issue has not been investigated. In this paper, we take up this problem and conduct a quantitative study of the noise and interference suppression abilities of LCMV, SCEM, WTACEM for multiple-target detection.