Bayesian Nonlinear Hyperspectral Unmixing With Spatial Residual Component Analysis

@article{Altmann2015BayesianNH,
  title={Bayesian Nonlinear Hyperspectral Unmixing With Spatial Residual Component Analysis},
  author={Yoann Altmann and Marcelo Pereyra and Stephen McLaughlin},
  journal={IEEE Transactions on Computational Imaging},
  year={2015},
  volume={1},
  pages={174-185}
}
This paper presents a new Bayesian model and algorithm for nonlinear unmixing of hyperspectral images. The proposed model represents the pixel reflectances as linear combinations of the endmembers, corrupted by nonlinear (with respect to the endmembers) terms and additive Gaussian noise. Prior knowledge about the problem is embedded in a hierarchical model that describes the dependence structure between the model parameters and their constraints. In particular, a gamma Markov random field is… CONTINUE READING
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