  Making the most of multispectral image time-series is a promising but still
relatively under-explored research direction because of the complexity of
jointly analyzing spatial, spectral and temporal information. Capturing and
characterizing temporal dynamics is one of the important and challenging
issues. Our new method paves the way to capture real data dynamics and should
eventually benefit applications like unmixing or classification. Dealing with
time-series dynamics classically requires the knowledge of a dynamical model
and an observation model. The former may be incorrect or computationally hard
to handle, thus motivating data-driven strategies aiming at learning dynamics
directly from data. In this paper, we adapt neural network architectures to
learn periodic dynamics of both simulated and real multispectral time-series.
We emphasize the necessity of choosing the right state variable to capture
periodic dynamics and show that our models can reproduce the average seasonal
dynamics of vegetation using only one year of training data.
