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Found 2 papers, 1 papers with code
Unmixing urban hyperspectral imagery with a Gaussian mixture model on endmember variability
Spectral libraries were built by manually identifying and extracting pure spectra from both resolution images, resulting in 3, 287 spectra at 16 m and 15, 426 spectra at 4 m. We then unmixed ROIs of each resolution using the following unmixing algorithms: the set-based algorithms MESMA and AAM, and the distribution-based algorithms GMM, NCM, and BCM.
A Gaussian mixture model representation of endmember variability in hyperspectral unmixing
We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing model) is also a GMM (and this is shown from two perspectives).
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