Hyperspectral image processing for automatic target detection applications

This article presents an overview of the theoretical and practical issues associated with the development, analysis, and application of detection algorithms to exploit hyperspectral imaging data. We focus on techniques that exploit spectral information exclusively to make decisions regarding the type of each pixel - target or nontarget - on a pixel-by-pixel basis in an image. First we describe the fundamental structure of the hyperspectral data and explain how these data influence the signal models used for the development and theoretical analysis of detection algorithms. Next we discuss the approach used to derive detection algorithms, the performance metrics necessary for the evaluation of these algorithms, and a taxonomy that presents the various algorithms in a systematic manner. We derive the basic algorithms in each family, explain how they work, and provide results for their theoretical performance. We conclude with empirical results that use hyperspectral imaging data from the HYDICE and Hyperion sensors to illustrate the operation and performance of various detectors.