Nonparametric Target Detection with Target Strength Estimation for Hyperspectral Images

This work presents a novel target detector that combines a nonparametric approach for conditional probability density function (pdf) estimation and an adaptive estimation of the target strength of the additive model it is based on. The variable bandwidth kernel density estimator is employed for pdf estimation within the Generalized Likelihood Ratio Test (GLRT) framework and a closed-form solution is found. Experimental results featuring hyperspectral data of a real subpixel target detection scenario reveal the potential of the proposed approach.