Unmixing multitemporal hyperspectral images with variability: An online algorithm

Abstract

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 : 16914The contribution was presented at ICASSP 2016 :http://www.icassp2016.org/ ABSTRACTHyperspectral unmixing consists in determining the reference spectral signatures composing a hyperspectral image and their relative abundance fractions in each pixel. In practice, the identified signatures may be affected by a significant spectral variability resulting for instance from the temporal evolution of the imaged scene. This phenomenon can be accounted for by using a perturbed linear mixing model. This paper studies an online estimation algorithm for the parameters of this extended linear mixing model. This algorithm is of interest for the practical applications where the size of the hyperspectral images precludes the use of batch procedures. The performance of the proposed method is evaluated on synthetic data.Index Terms-Multi-temporal hyperspectral image, linear unmixing, endmember variability, online estimation, two-stage stochastic program.