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.