A pattern-based method for handling confidence measures while mining satellite displacement field time series. Application to Greenland ice sheet and Alpine glaciers

International audienceFor more than 40 years, Earth observation satellites have been regularly providing images of glaciers that can be used to derive surface displacement fields and study their dynamics. In the context of global warming, the analysis of Displacement Field Time Series (DFTS) can provide useful information. Efficient data mining techniques are thus required to extract meaningful displacement evolutions from such large and complex datasets. In this paper, a pattern-based data mining approach which handles confidence measures is proposed to analyze DFTS. In order to focus on the most reliable measurements, a displacement evolution reliability measure is defined. It is aimed at assessing the quality of each evolution and pruning the search space. Experiments on two different DFTS (annual displacement fields derived from optical data over Greenland ice sheet and 11-day displacement fields derived from SAR data over Alpine glaciers) show the potential of the proposed approach

Recherche automatique des fenêtres temporelles optimales des motifs séquentiels

Ce mémoire concerne l'extraction sous contraintes de motifs dans une séquence d'événements. Les motifs extraits sont des règles d'épisodes. L'apport principal réside dans la détermination automatique de la fenêtre temporelle optimale de chaque règle d'épisodes. Nous proposons de n'extraire que les règles pour lesquelles il existe une telle fenêtre. Ces règles sont appelées FLM-règles. Nous présentons un algorithme, WinMiner, pour extraire les FLM-règles, sous les contraintes de support minimum, de confiance minimum, et de gap maximum. Les preuves de la correction de cet algorithme sont fournies. Nous proposons également une mesure d'intérêt dédiée qui permet de sélectionner les FLM-règles pour lesquelles il existe une forte dépendance entre corps et tête de règle. Deux applications de cet algorithme sont décrites. L'une concerne des données médicales tandis que l'autre a été réalisée sur des données sismiques.This work addresses the problem of mining patterns under constraints in event sequences. Extracted patterns are episode rules. Our main contribution is an automatic search for optimal time window of each one of the episode rules. We propose to extract only rules having such an optimal time window. These rules are termed FLM-rules. We present an algorithm, WinMiner, that aims to extract FLM-rules, given a minimum support threshold, a minimum confidence threshold and a maximum gap constraint. Proofs of the correctness of this algorithm are supplied. We also propose a dedicated interest measure that aims to select FLM-rules such that their heads and bodies can be considered as dependant. Two applications are described. The first one is about mining medical datasets while the other one deals with seismic datasets.VILLEURBANNE-DOC'INSA LYON (692662301) / SudocSudocFranceF

Fouille de données spatio-temporelles appliquée aux séries d'images satellite

National audiencePas de résum

Fouille de données spatio-temporelles appliquée aux séries d'images satellite

National audiencePas de résum

Fouille de données spatio-temporelles appliquée aux séries d'images satellite

National audiencePas de résum

Iterative Summarization of Satellite Image Time Series

International audienc

Iterative Summarization of Satellite Image Time Series

International audienc

Normalized Mutual Information-Based Ranking of Spatio-Temporal Localization Maps

International audienceSatellite image time series (SITS) can be described in an unsupervised way by means of spatio-temporal localization maps. These maps are extracted using data mining techniques that spatially and temporally locate pixel evolutions affecting a minimum number of pixels with sufficiently high connectivity. Depending on the parameter settings and on the original data, large numbers of maps may be produced. In order to focus on the most interesting ones,we propose a method to rank them by computing the normalized mutual information between the spatio-temporal localization maps extracted from the SITS and the ones extracted from the same but randomized SITS. The latter is obtained using a swap-randomization technique. Experimental results on a Landsat 7 SITS covering New Caledonia are presented

A swap randomization approach for mining motion field time series over the Argentiere glacier

International audienceGrouped Frequent Sequential patterns can be extracted in an unsupervised way from Image Time Series (ITS). Plotting the occurrence maps of these patterns allows to describe the dataset spatially and temporally while discarding random uncertainties. However these maps can be too numerous and a swap randomization ranking approach has been proposed recently to select the most promising patterns. This previous work experimented the technique on Satellite ITS, giving credit to the maps that are least likely to appear on a randomized ITS. In this paper, extraction and ranking of GFS patterns is performed on a motion field time series obtained by terrestrial photogrammetry over the Argentière glacier. The focus is extended to the maps that are most likely to occur on the randomized time series and the experiment is repeated thousand times to assess the stability of the ranking

GFS-pattern extraction in satellite image time series: application to the monitoring of Mount Etna

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