  Deep Neural Networks (DNNs) are getting increasing attention to deal with
Land Cover Classification (LCC) relying on Satellite Image Time Series (SITS).
Though high performances can be achieved, the rationale of a prediction yielded
by a DNN often remains unclear. An architecture expressing predictions with
respect to input channels is thus proposed in this paper. It relies on
convolutional layers and an attention mechanism weighting the importance of
each channel in the final classification decision. The correlation between
channels is taken into account to set up shared kernels and lower model
complexity. Experiments based on a Sentinel-2 SITS show promising results.
