Unsupervised Unmixing of Hyperspectral Images Accounting for Endmember Variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Tourneret
2015 IEEE Transactions on Image Processing  
This paper presents an unsupervised Bayesian algorithm for hyperspectral image unmixing accounting for endmember variability. The pixels are modeled by a linear combination of endmembers weighted by their corresponding abundances. However, the endmembers are assumed random to take into account their variability in the image. An additive noise is also considered in the proposed model generalizing the normal compositional model. The proposed algorithm exploits the whole image to provide spectral
more » ... nd spatial information. It estimates both the mean and the covariance matrix of each endmember in the image. This allows the behavior of each material to be analyzed and its variability to be quantified in the scene. A spatial segmentation is also obtained based on the estimated abundances. In order to estimate the parameters associated with the proposed Bayesian model, we propose to use a Hamiltonian Monte Carlo algorithm. The performance of the resulting unmixing strategy is evaluated via simulations conducted on both synthetic and real data.
doi:10.1109/tip.2015.2471182 pmid:26302517 fatcat:lwgecnrmlje5jch56fgwgysyny