  Hyperspectral image unmixing is an inverse problem aiming at recovering the
spectral signatures of pure materials of interest (called endmembers) and
estimating their proportions (called abundances) in every pixel of the image.
However, in spite of a tremendous applicative potential and the avent of new
satellite sensors with high temporal resolution, multitemporal hyperspectral
unmixing is still a relatively underexplored research avenue in the community,
compared to standard image unmixing. In this paper, we propose a new framework
for multitemporal unmixing and endmember extraction based on a state-space
model, and present a proof of concept on simulated data to show how this
representation can be used to inform multitemporal unmixing with external prior
knowledge, or on the contrary to learn the dynamics of the quantities involved
from data using neural network architectures adapted to the identification of
dynamical systems.
