  The increasing accessibility and precision of Earth observation satellite
data offers considerable opportunities for industrial and state actors alike.
This calls however for efficient methods able to process time-series on a
global scale. Building on recent work employing multi-headed self-attention
mechanisms to classify remote sensing time sequences, we propose a modification
of the Temporal Attention Encoder. In our network, the channels of the temporal
inputs are distributed among several compact attention heads operating in
parallel. Each head extracts highly-specialized temporal features which are in
turn concatenated into a single representation. Our approach outperforms other
state-of-the-art time series classification algorithms on an open-access
satellite image dataset, while using significantly fewer parameters and with a
reduced computational complexity.
