  Satellite image time series, bolstered by their growing availability, are at
the forefront of an extensive effort towards automated Earth monitoring by
international institutions. In particular, large-scale control of agricultural
parcels is an issue of major political and economic importance. In this regard,
hybrid convolutional-recurrent neural architectures have shown promising
results for the automated classification of satellite image time series.We
propose an alternative approach in which the convolutional layers are
advantageously replaced with encoders operating on unordered sets of pixels to
exploit the typically coarse resolution of publicly available satellite images.
We also propose to extract temporal features using a bespoke neural
architecture based on self-attention instead of recurrent networks. We
demonstrate experimentally that our method not only outperforms previous
state-of-the-art approaches in terms of precision, but also significantly
decreases processing time and memory requirements. Lastly, we release a large
open-access annotated dataset as a benchmark for future work on satellite image
time series.
