Stochastic target detection for hyperspectral data

There has been considerable interest in recent years in the recognition and identification of known materials and objects by using airborne hyperspectral sensors. Hyperspectral sensors provide the spectral signature for every pixel, which can be compared to the signature of a material of interest. In this paper a signature recognition algorithm is developed based on the Generalized Likelihood Ratio Test (GLRT) approach. Om starting model for target and clutter assumes that the target signature replaces the background and does not add to it. The recognition algorithm is developed using this model, and then applied to hyperspectral data to illustrate the performance.