An automatic target detection system that uses hyperspectral (HS) imagery is proposed. HS images contain both spatial and spectral response information that provides detailed descriptions of an object. These new, to our knowledge, sensor data are useful in automatic target recognition applications. To provide discrimination information from the HS images and to select features that generalize well, we describe a new, to our knowledge, high-dimensional generalized discriminant feature-extraction algorithm and compare its performance with that of other feature-reduction methods for two HS target detection applications (mine and vehicle detection) by using a nearest-neighbor classifier. We also advance an approach to simultaneously optimize both spatial and spectral responses.

© 2004 Optical Society of America

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