Spatio-spectral anomalous change detection in hyperspectral imagery

Because each pixel of a hyperspectral image contains so much information, many (successful) algorithms treat those pixels as independent samples, despite the evident spatial structure in the imagery. One way to exploit this structure is to incorporate spatial processing into pixel-wise anomalous change detection algorithms. But if this is done in the most straightforward way, a contaminated cross-covariance is produced. A spatial processing framework is proposed that avoids this contamination and enhances the performance of anomalous change detection algorithms in hyperspectral imagery.