2020/07/01 by Vivien Sainte Fare Garnot, Garnot, Vivien Sainte Fare, Loïc Landrieu +1 · 11 citations
Computer Science · Engineering · #Advanced Chemical Sensor Technologies #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2007.00586
openalex publication_date 2020/07/01 · openalex created_date 2020/07/10 · openalex updated_date 2026/07/28
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.