Efficient Spatiotemporal Mining of Satellite Image Time Series for Agricultural Monitoring

Andreea Julea 1, 2 Nicolas Méger 1 Christophe Rigotti 3, 4, 5 Emmanuel Trouvé 1 Romain Jolivet 6 Philippe Bolon 1
4 DM2L - Data Mining and Machine Learning
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information
5 BEAGLE - Artificial Evolution and Computational Biology
LIRIS - Laboratoire d'InfoRmatique en Image et Systèmes d'information, Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
6 Cycle sismique et déformations transitoires
ISTerre - Institut des Sciences de la Terre
Abstract : In this paper, we present a technique for helping experts in agricultural monitoring, by mining Satellite Image Time Series over cultivated areas. We use frequent sequential patterns extended to this spatiotemporal context in order to extract sets of connected pixels sharing a similar temporal evolution. We show that a pixel connectivity constraint can be partially pushed to prune the search space, in conjunction with a support threshold. Together with a simple maximality constraint, the method reveals meaningful patterns in real datasets.
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Journal articles
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https://hal.archives-ouvertes.fr/hal-00702433
Contributor : Nicolas Méger <>
Submitted on : Wednesday, May 30, 2012 - 11:29:23 AM
Last modification on : Wednesday, November 20, 2019 - 3:27:44 AM

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  • HAL Id : hal-00702433, version 1

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Andreea Julea, Nicolas Méger, Christophe Rigotti, Emmanuel Trouvé, Romain Jolivet, et al.. Efficient Spatiotemporal Mining of Satellite Image Time Series for Agricultural Monitoring. Transactions on Machine Learning and Data Mining, IBAI Publishing, 2012, 5 (1), pp.23-44. ⟨hal-00702433⟩

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