The GMRF detector for hyperspectral imagery: An efficient fully-adaptive maximum likelihood detector

Hyperspectral sensors collect hundreds of narrow and contiguously spaced spectral bands of data organized in the so called hyperspectral cube. The hyperspectral imagery provides fully registered spatial and high resolution spectral information that is invaluable in discriminating between man-made objects and natural clutter backgrounds, since the objects and clutter have unique spectral signatures that are captured by the data. This comes at a cost. The high volume of data in the hyperspectral cube and the associated processing that is required, has precluded the development of computationally practical Maximum-Likelihood (ML) detectors of man-made anomalies in clutter.; This thesis solves this problem. We derive the Gauss-Markov random field (GMRF) detector, a computationally efficient ML anomaly detector that fully adapts to the unknown statistics of the clutter, and fully exploits the spatial and spectral correlation of the hyperspectral imagery. We test extensively our clutter adaptive GMRF detector with real imagery from several hyperspectral sensors. Our results show that the GMRF detector is significantly simpler computationally and noticeably improves the detection performance over the benchmark anomaly detection algorithm. Our approach avoids the costly step of inverting the large sample covariance matrix of the clutter. We parameterize directly the inverse of the clutter covariance and develop several alternative methods to match this inverse to the actual clutter statistics.