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

Channel-Based Attention for LCC Using Sentinel-2 Time Series