An Iterative GLRT for Hyperspectral Target Detection Based on Spectral Similarity and Spatial Connectivity Characteristics

Abstract

Recently, a generalized likelihood ratio test (GLRT)-based multipixel target detector for hyperspectral imagery was proposed. With joint exploitation of the pixels occupied by a target of interest, the detection performance was significantly improved. However, it still faces a pixel selection problem in practice. In this paper, we address the pixel selection problem for the multipixel target detector in practice. First, we propose an adaptive target pixel selection method based on spectral similarity and spatial connectivity characteristics. Second, we propose a method to collect the pixels spatially closest to the target pixels as the training background pixels so that their residual background component share the same statistical characteristics with high probability. To exclude potential target pixels in the collected training background pixels, an iterative version of the GLRT-based multipixel target detector is proposed. It is easy to set the key parameters of the proposed method, which is attractive in practice. Experimental results on four real hyperspectral datasets show that the proposed method outperforms its counterparts in terms of detection performance.