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Nonlinear unmixing of hyperspectral images using a generalized bilinear model: matlab codes Download the software [ .zip - 40Ko ].


Nonlinear models have recently shown interesting properties for spectral unmixing. Our research studies a generalized bilinear model and a hierarchical Bayesian algorithm for unmixing hyperspectral images. The proposed model is a generalization not only of the accepted linear mixing model but also of a bilinear model that has been recently introduced in the literature. Appropriate priors are chosen for its parameters to satisfy the positivity and sum-to-one constraints for the abundances. The joint posterior distribution of the unknown parameter vector is then derived. Unfortunately, this posterior is too complex to obtain analytical expressions of the standard Bayesian estimators. As a consequence, a Metropolis-within-Gibbs algorithm is proposed, which allows samples distributed according to this posterior to be generated and to estimate the unknown model parameters. The performance of the resulting unmixing strategy shows promising results for both synthetic and real data.

Endmember variability in hyperspectral images:


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