Abstract
Algorithms for point target detection in hyperspectral images use the inverse covariance matrix in order to separate a detected pixel from it surrounding noise. The inverse covariance matrix can be implemented from all the pixels or from the close surroundings of the examined pixel. We compare the different methods and conclude which method brings the best results.
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