Multi-temporal hyperspectral images unmixing by mixed distribution considering smooth variation of abundance

The environmental change caused by time interval usually leads to the disturbance of endmember reflectance curve, which has an important influence on multi-temporal hyperspectral unmixing process. In this paper, a Bayesian unmixing model considering the spectral variability is proposed, in which a mixed prior distribution of the abundance is constructed. Different form the existing methods, the continuity of the abundance in time and spatial domain is considered simultaneously. In order to describe the smoothness of the abundance, a data-adaptive variance of probability distribution is designed based on the spatial local difference. Then combined with the priors of endmembers and spectral variability, the joint posterior distribution is set up and Markov chain Monte-Carlo(MCMC) algorithm is developed for posterior computation. Experiments on simulated and real datasets demonstrate the effectiveness of the proposed algorithm in terms of abundance estimation and endmember estimation.