HYPERSPECTRAL TARGET DETECTION USING SEMI- AND NON- PARAMETRIC METHODS

In this paper we propose novel semi- and non- parametric detectors to be used with the additive target signal model within the general detection framework of the likelihood ratio test (LRT). In the semi-parametric detector, the Gaussian mixture model is employed to estimate a lower dimensional approximation of the background probability density function (PDF), whereas a multivariate kernel density estimator is employed to estimate the PDF in the multidimensional space within the non-parametric approach. Target detection experiments are carried out using the hyperspectral airborne "Viareggio 2013 trial" data set. The detectors are shown to provide promising results for the detection of the targets of interest deployed in the scene and outperform the well-known Adaptive Match Filter (AMF) detector.