Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery

@article{Altmann2011SupervisedNS,
  title={Supervised nonlinear spectral unmixing using a polynomial post nonlinear model for hyperspectral imagery},
  author={Yoann Altmann and Abderrahim Halimi and Nicolas Dobigeon and Jean-Yves Tourneret},
  journal={2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
  year={2011},
  pages={1009-1012}
}
This paper studies a hierarchical Bayesian model for nonlinear hyperspectral image unmixing. The proposed model assumes that the pixel reflectances are polynomial functions of linear mixtures of pure spectral components contaminated by an additive white Gaussian noise. The parameters involved in this model satisfy constraints that are naturally expressed within a Bayesian framework. A Gibbs sampler allows one to sample the unknown abundances and nonlinearity parameters according to the joint… CONTINUE READING
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