Unmixing multitemporal hyperspectral images accounting for endmember variability

Abderrahim Halimi, Nicolas Dobigeon, Jean-Yves Toumeret, Steve McLaughlin, Paul Honeine
2015 2015 23rd European Signal Processing Conference (EUSIPCO)  
mixing multitemporal hyperspectral images accounting for endmember variability. 23rd European OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 15346 The contribution was presented at EUSIPCO 2015 : ABSTRACT This paper proposes an unsupervised Bayesian algorithm for unmixing successive hyperspectral images while
more » ... accounting for temporal and spatial variability of the endmembers. Each image pixel is modeled as a linear combination of the endmembers weighted by their corresponding abundances. Spatial endmember variability is introduced by considering the normal compositional model that assumes variable endmembers for each image pixel. A prior enforcing a smooth temporal variation of both endmembers and abundances is considered. The proposed algorithm estimates the mean vectors and covariance matrices of the endmembers and the abundances associated with each image. Since the estimators are difficult to express in closed form, we propose to sample according to the posterior distribution of interest and use the generated samples to build estimators. The performance of the proposed Bayesian model and the corresponding estimation algorithm is evaluated by comparison with other unmixing algorithms on synthetic images. Index Terms-Hyperspectral unmixing, spectral variability, temporal and spatial variability, Bayesian algorithm, Hamiltonian Monte-Carlo, MCMC methods.
doi:10.1109/eusipco.2015.7362665 dblp:conf/eusipco/HalimiDTMH15 fatcat:wm2gb4whgvc3faehftta3b2lai