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Article Dans Une Revue Transactions on Machine Learning and Data Mining Année : 2012

Efficient Spatiotemporal Mining of Satellite Image Time Series for Agricultural Monitoring

Résumé

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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Dates et versions

hal-00702433 , version 1 (30-05-2012)

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  • HAL Id : hal-00702433 , version 1

Citer

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, 2012, 5 (1), pp.23-44. ⟨hal-00702433⟩
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