A GLRT-Based Multi-Pixel Target Detector in Hyperspectral Imagery

Abstract

In hyperspectral imagery, target detection algorithms are usually based on the spectral signature information. Due to the advance of the spatial resolution of hyperspectral sensors, the ground sample distance may be much smaller than the size of targets. As a result, targets often occupy multiple consecutive pixels, which are referred to as multi-pixel targets. In this paper, we investigate the target detection problem for multi-pixel targets in hyperspectral imagery, when the target spectral signature is known. Jointly exploiting the pixels occupied by a target of interest, we propose a multi-pixel target detector resorting to the generalized likelihood ratio test criterion. Closed-form expressions for the probabilities of the false alarm and detection are derived, which are verified using Monte Carlo simulations. Experimental results on four real hyperspectral datasets show that the proposed detector outperforms its counterparts.