Adaptive matched subspace detectors for hyperspectral imaging applications

Real-time detection and identification of man-made objects or materials ("targets") from airborne platforms using hyperspectral sensors are of great interest for civilian and military applications. Over the past several years, different algorithms for the detection of targets with known spectral signature have been developed. Most of these algorithms have been reviewed by Manolakis, Shaw and Keshava (see Algorithms for Multispectral and Hyperspectral Imagery, Orlando, FL, April 2000, SPIE) within a unified theoretical and notational framework. In this paper we study adaptive matched subspace detection algorithms for low probability, single-pixel or subpixel targets. These algorithms explore the linear mixing model to both specify the desired target and characterize the interfering background. The derived algorithms are theoretically and experimentally evaluated with regard to two desirable properties: capacity to operate in constant false alarm rate (CFAR) mode and target "visibility" enhancement. Furthermore, an approach is presented for taking into account target variability, when present, to improve detection.