The recent evolution of hyperspectral imaging technology and the
proliferation of new emerging applications presses for the processing of
multiple temporal hyperspectral images. In this work, we propose a novel
spectral unmixing (SU) strategy using physically motivated parametric endmember
representations to account for temporal spectral variability. By representing
the multitemporal mixing process using a state-space formulation, we are able
to exploit the Bayesian filtering machinery to estimate the endmember
variability coefficients. Moreover, by assuming that the temporal variability
of the abundances is small over short intervals, an efficient implementation of
the expectation maximization (EM) algorithm is employed to estimate the
abundances and the other model parameters. Simulation results indicate that the
proposed strategy outperforms state-of-the-art multitemporal SU algorithms