Early Classification for Agricultural Monitoring from Satellite Time Series

Marc Rußwurm 1 Romain Tavenard 2, 3 Sébastien Lefèvre 3 Marco Körner 1
2 LETG - Rennes - Littoral, Environnement, Télédétection, Géomatique
LETG - Littoral, Environnement, Télédétection, Géomatique UMR 6554
3 OBELIX - Environment observation with complex imagery
UBS - Université de Bretagne Sud, IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : In this work, we introduce a recently developed early classification mechanism to satellite-based agricultural monitoring. It augments existing classification models by an additional stopping probability based on the previously seen information. This mechanism is end-to-end trainable and derives its stopping decision solely from the observed satellite data. We show results on field parcels in central Europe where sufficient ground truth data is available for an empiric evaluation of the results with local phenological information obtained from authorities. We observe that the recurrent neural network outfitted with this early classification mechanism was able to distinguish the many of the crop types before the end of the vegetative period. Further, we associated these stopping times with evaluated ground truth information and saw that the times of classification were related to characteristic events of the observed plants' phenology.
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Conference papers
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https://hal.archives-ouvertes.fr/hal-02343851
Contributor : Sébastien Lefèvre <>
Submitted on : Sunday, November 3, 2019 - 4:06:49 PM
Last modification on : Tuesday, November 5, 2019 - 1:19:28 AM

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  • HAL Id : hal-02343851, version 1
  • ARXIV : 1908.10283

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Marc Rußwurm, Romain Tavenard, Sébastien Lefèvre, Marco Körner. Early Classification for Agricultural Monitoring from Satellite Time Series. AI for Social Good Workshop at International Conference on Machine Learning (ICML), 2019, Long Beach, United States. ⟨hal-02343851⟩

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