Lightweight Temporal Self-Attention for Classifying Satellite Image Time Series

1 Jul 2020  ·  Vivien Sainte Fare Garnot, Loic Landrieu ·

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.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Time Series Classification s2-agri PSE+L-TAE mIoU 51.7 # 1
oAcc 94.3 # 1