An unsupervised algorithm for hyperspectral image segmentation based on the Gaussian mixture model

A new algorithm for hyperspectral image segmentation based on the statistical approach is presented. The algorithm is completely unsupervised and relies only on the spectral information. The hyperspectral image is statistically characterized by means of the Gaussian Mixture Model (GMM). Preliminary results obtained on experimental data are presented and discussed.

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