Robust hyperspectral image segmentation based on a non-Gaussian model

Spectra collected by hyperspectral sensors over samples of the same material are not deterministic quantities. Their inherent spectral variability can be accounted for by making use of suitable statistical models. Within this framework, the Gaussian Mixture Model (GMM) is one of the most widely adopted models for modeling hyperspectral data. Unfortunately, the GMM has been shown not to be sufficiently adequate to represent the statistical behavior of real hyperspectral data, especially for the tails of the distributions. The class of elliptically contoured distributions, which accommodates longer tails, promises to better match the spectral distribution of hyperspectral data.

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