Handling data quality in extraction and selection of evolutions from displacement field time series obtained by satellite imagery ; Prise en compte de la qualité des données lors de l’extraction et de la sélection d’évolutions dans les séries temporelles de champs de déplacements en imagerie satellitaire

This PhD thesis deals with knowledge discovery from Displacement Field Time Series (DFTS) obtained by satellite imagery. Such series now occupy a central place in the study and monitoring of natural phenomena such as earthquakes, volcanic eruptions and glacier displacements. These series are indeed rich in both spatial and temporal information and can now be produced regularly at a lower cost thanks to spatial programs such as the European Copernicus program and its famous Sentinel satellites.Our proposals are based on the extraction of grouped frequent sequential patterns. These patterns, originally defined for the extraction of knowledge from Satellite Image Time Series (SITS), have shown their potential in early work to analyze a DFTS. Nevertheless, they cannot use the confidence indices coming along with DFTS and the swap method used to select the most promising patterns does not take into account their spatiotemporal complementarities, each pattern being evaluated individually.Our contribution is thus double. A first proposal aims to associate a measure of reliability with each pattern by using the confidence indices. This measure allows to select patterns having occurrences in the data that are on average sufficiently reliable. We propose a cor- responding constraint-based extraction algorithm. It relies on an efficient search of the most reliable occurrences by dynamic programming and on a pruning of the search space provided by a partial push strategy. This new method has been implemented on the basis of the exis- ting prototype SITS-P2miner, developed by the LISTIC and LIRIS laboratories to extract and rank grouped frequent sequential patterns.A second contribution for the selection of the most promising patterns is also made. This one, based on an informational criterion, makes it possible to take into account at the same time the confidence indices and the way the patterns complement each other spatially and temporally. For this aim, the confidence indices are interpreted as probabilities, and the ...