A Novel Hierarchical Bayesian Approach for Sparse Semisupervised Hyperspectral Unmixing

@article{Themelis2012ANH,
  title={A Novel Hierarchical Bayesian Approach for Sparse Semisupervised Hyperspectral Unmixing},
  author={Konstantinos Themelis and Athanasios A. Rontogiannis and Konstantinos Koutroumbas},
  journal={IEEE Transactions on Signal Processing},
  year={2012},
  volume={60},
  pages={585-599}
}
In this paper the problem of semisupervised hyper spectral unmixing is considered. More specifically, the unmixing process is formulated as a linear regression problem, where the abundance's physical constraints are taken into account. Based on this formulation, a novel hierarchical Bayesian model is proposed and suitable priors are selected for the model parameters such that, on the one hand, they ensure the nonnegativity of the abundances, while on the other hand they favor sparse solutions… CONTINUE READING
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