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.

该研究使用多头自注意机制并修改时间注意编码器，以有效处理全球规模的卫星图像时间序列分类，提高时间序列分类的准确性和效率。在开放访问的卫星图像数据集中，使用更少的参数和降低计算复杂度，该方法优于其他现有的时间序列分类算法。