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Time-series analysis of massive satellite images : Application to earth observation / Alexandre Constantin (2021)  

Public Titre : Time-series analysis of massive satellite images : Application to earth observation Titre original : Analyse de séries temporelles massives d'images satellitaires : Applications à la cartographie des écosystèmes Type de document : Thèse/HDR Auteurs : Alexandre Constantin, Auteur ; Stéphane Girard, Directeur de thèse ; Mathieu Fauvel, Directeur de thèse Editeur : Grenoble [France] : Université Grenoble Alpes Année de publication : 2021 Importance : 136 p. Format : 21 x 30 cm Note générale : bibliographie Thèse Pour obtenir le grade de Docteur de l'Université de Grenoble Alpes, Specialité : Mathématiques Appliquées Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse multivariée [Termes IGN] carte d'occupation du sol [Termes IGN] classification dirigée [Termes IGN] classification pixellaire [Termes IGN] covariance [Termes IGN] échantillonnage de données [Termes IGN] écosystème [Termes IGN] image Sentinel-MSI [Termes IGN] processus gaussien [Termes IGN] Python (langage de programmation) [Termes IGN] régression [Termes IGN] série temporelle Index. décimale : THESE Thèses et HDR Résumé : (auteur) This thesis takes place in the context of the processing of the data from Sentinel-2 mission. This mission, initiated by the European Space Agency and launched in 2017, produces an unprecedented amount of Satellite Image Time-Series (SITS). Among the key analyses of these images, this thesis focuses on the classification task, i.e. land use or land cover maps that can be produced using spectro-temporal aspect of the Sentinel-2 SITS.Two main difficulties are identified in this thesis for the process of Sentinel-2 SITS. First, the unprecedented amount of data requires both scalable classifiers and code optimization techniques (such as parallel processing). Second, the acquisition noise (clouds, shadows) combined with the temporal aspect results in irregular and unevenly sampled time-series. Conventional approaches re-sample time-series to a set of time stamps, then they use machine learning techniques to classify vectors at a large-scale (national scale). The main disadvantage of this two-step processing approach is that it increases the number of operations applied to the SITS, implying a more difficult transition to massive amount of data. To a lower extent, the re-sampling step may slightly alter the temporal characteristics of the data.This thesis contributions are the following. We introduce a novel model-based approach with the ability to classify irregularly sampled time-series based on a mixture of multivariate Gaussian processes. A two-step approach has been used, by defining on one hand a model of uni-variate time-series, independent from the spectral wavelength point of view, then by considering on the second hand both spectral and temporal information from SITS. These models allow jointly a reconstruction of unobserved or noisy data. Estimation of both models has been implemented using a parallelized python code to be scalable to large-scale data-sets. The two models are evaluated numerically on Sentinel-2 SITS in terms of classification and reconstruction accuracy and are compared with conventional approaches. Analyses of the results illustrate the relevance of the two models and the benefit of using interpretable parametric models. Note de contenu : General Introduction 1- Satellite image time-series analysis and classification 2- Statistical modelling for time-series classification 3- Model-based classification for irregularly sampled time-series 4- Joint supervised classification and reconstruction of irregularly sampled satellite image times series 5- Mixture of multivariate gaussian processes for classification of irregularly sampled SITS Conclusion and perspectives Numéro de notice : 15280 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques Appliquées : Grenoble : 2021 Organisme de stage : Laboratoire Jean Kuntzmann DOI : sans En ligne : https://hal.science/tel-03682025 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101161