Abstract
In this paper, we present a technique to help the 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 data.
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Julea, A., Méger, N., Rigotti, C., Trouvé, E., Bolon, P., Lăzărescu, V. (2011). Mining Pixel Evolutions in Satellite Image Time Series for Agricultural Monitoring. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2011. Lecture Notes in Computer Science(), vol 6870. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-23184-1_15
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