A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing

@article{Zhou2017AGM,
  title={A Gaussian Mixture Model Representation of Endmember Variability in Hyperspectral Unmixing},
  author={Yuan Zhou and Anand Rangarajan and Paul D. Gader},
  journal={IEEE Transactions on Image Processing},
  year={2017},
  volume={27},
  pages={2242-2256}
}
Hyperspectral unmixing while considering endmember variability is usually performed by the normal compositional model, where the endmembers for each pixel are assumed to be sampled from unimodal Gaussian distributions. However, in real applications, the distribution of a material is often not Gaussian. In this paper, we use Gaussian mixture models (GMM) to represent endmember variability. We show, given the GMM starting premise, that the distribution of the mixed pixel (under the linear mixing… 

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