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Supervised classification of hyperspectral images is a challenging task due to the relatively low ratio between the number of training samples and the number of spectral channels. Subspace-based classification methods deal with this difficulty by assuming that feature vectors lie in a low-dimensional subspace. Based on the fact that a class in a hyperspectral image may be composed of a number of different...
In the field of subpixel target detection in hyperspectral images, there is well-documented current interest for identifying preferred background covariance matrix estimates, to be used in the formation of matched-filter detectors. In this work, for the first time, we study the case of local background covariance matrix estimation from SLIC-superpixel based coherent regions. Interestingly, our experiments...
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