Physics – Data Analysis – Statistics and Probability
Scientific paper
2009-03-17
IEEE Trans. Signal Processing, vol. 57, no. 11, pp. 4355-4368, Nov. 2009
Physics
Data Analysis, Statistics and Probability
Scientific paper
10.1109/TSP.2009.2025797
This paper studies a fully Bayesian algorithm for endmember extraction and abundance estimation for hyperspectral imagery. Each pixel of the hyperspectral image is decomposed as a linear combination of pure endmember spectra following the linear mixing model. The estimation of the unknown endmember spectra is conducted in a unified manner by generating the posterior distribution of abundances and endmember parameters under a hierarchical Bayesian model. This model assumes conjugate prior distributions for these parameters, accounts for non-negativity and full-additivity constraints, and exploits the fact that the endmember proportions lie on a lower dimensional simplex. A Gibbs sampler is proposed to overcome the complexity of evaluating the resulting posterior distribution. This sampler generates samples distributed according to the posterior distribution and estimates the unknown parameters using these generated samples. The accuracy of the joint Bayesian estimator is illustrated by simulations conducted on synthetic and real AVIRIS images.
Coulon Martial
Dobigeon Nicolas
Hero Alfred O.
Moussaoui Said
Tourneret Jean-Yves
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