A Generalized Likelihood Ratio Test for Detecting Land Mines Using Multispectral Images

Abstract

In this letter, we address the problem of detecting anomalies in multispectral images that are due to the presence of land mines. The proposed detection scheme follows from a generalized likelihood ratio test (GLRT) involving a mixture of certain probability density functions. More specifically, the GLRT is based on the assumption that the pixel values of a subblock within a single spectral-plane image can be modeled as a mixture of two Gaussian density functions (model distribution of background pixels) and a uniform density function (model distribution of anomalous pixels when they are present). We extend the single spectral-plane GLRT to the multiple spectral-plane case where the subblocks are from multispectral images. To assess the proposed GLRT, which we call the Gaussian-uniform GLRT (GU-GLRT) algorithm, we applied the GU-GLRT and popular RX algorithms to images from a real multispectral image sequence and used receiver operating characteristic (ROC) curves as the figure of merit. In the experiments that we conducted, the GU-GLRT outperformed the RX algorithm in the sense that the area under the ROC curve was greatest for the GU-GLRT algorithm.