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Offre de bourse de thèse en télédétection multi temporelle - AgroTIC - Agriculture numérique
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Titre: Analyse de séries temporelles massives d’images satellitaires Equipes encadrantes: Dynafor, Université de Toulouse, INRA, INPT, INPT-EI PURPAN, 31326 Castanet Tolosan,
Mistis, Inria Grenoble Rhône-Alpes, LJK, 38334 Montbonnot, France
Modal, Inria Lille Nord Europe, LPP UMR-8524, Université Lille, 59655 Villeneuve d’Ascq, France
Objectif: Cette thèse en s’intéresse à la statistique fonctionnelle pour la cartographie des écosystèmes. Pour cela, nous allons développer des méthodes d’analyses permettant à la fois de s’affranchir de l’étape de recalage temporel et de manipuler de grands volumes de données.
Le point de départ de ce travail est la définition d’une méthode d’analyse permettant de classifier un pixel quels que soient les instants d’échantillonnage temporel. Nous allons considérer que les échantillons, conditionnellement aux classes, sont distribués selon des processus Gaussiens.
De plus, nous relâcherons l’hypothèse d’échantillonnage temporel irrégulier en supposant simplement que chaque pixel xi est observé Ti fois durant l’année (ou plus généralement durant la période d’analyse), Ti dépendant donc de la localisation géographique, de l’orbite du satellite, des nuages et ombres observés pour xi.
Le coeur des travaux de thèse s’attachera à spécifier les méthodes et les outils pour apprendre les paramètres des processus Gaussiens. En particulier, lors de la création du processus Gaussien, des bases fonctionnelles temporelles vont être définies. Elles devront pendre en compte à la fois l’évolution temporelle (corrélation forte des mesures entre deux instants proches et faible lorsque les instants sont éloignés) et la réponse spectrale à un instant donné (corrélation inter-bandes).
Cette étape est importante car l’utilisation d’une base adaptée permet à la fois de s’affranchir du bruit et des données manquantes tout en capturant l’information utile à l’analyse. Précisons toutefois qu’il n’est pas envisagé ici de lisser/projeter les pixels sur cette base, mais d’exprimer la fonction de décision à partir de cette représentation spectro-temporelle de manière à pouvoir l’appliquer sur n’importe quel échantillonnage temporel.
Pour traiter les flux massifs d’images, des algorithmes d’optimisation efficaces utilisant des approches HPC (high performance computing) devront être définis. A cet effet, nous allons nous appuyer sur certaines propriétés intéressantes de l’algorithme d’apprentissage des paramètres des processus Gaussiens :
Modèles génératifs : Cela va permettre de traiter les classes indépendamment les unes des autres, l’apprentissage des paramètres pouvant se faire dans des taches séparées, sur des unités de calculs différentes sans partage de mémoire. Estimateurs explicites : Les estimateurs ne nécessitent pas d’étapes d’optimisation complexes (e.g., gradient sous contraintes). De plus, ces estimateurs supportent des modes “on-line”, c’est à dire qu’il est possible de répartir les estimations sur différents paquets de données et de mettre à jour l’estimateur final en procédant à la mise en commun. L’approche envisagée serait basée sur la norme MPI (Message Passing Interface), la taille des informations à mettre en commun étant limitée (quelques Mo). La plupart des calculs pour l’apprentissage et pour la prédiction sont basés sur de l’algèbre linaire (produit matriciel, déterminant, décomposition en valeurs propres/vecteurs propres . . . ) qui peuvent grandement tirer partie des techniques de parallélisation sur des unités de calculs, soit sur processeurs de type CPU soit de type GPU.
Les développements informatiques seront réalisés à l’aide de l’interface de programmation Orfeo Toolbox. Le prototypage pour la mise en oeuvre sur une grappe de serveurs sera validée à l’aide des moyens de calculs distribués offert par l’INRA et par Inria. Les codes sources seront distribués sous une licence copyleft pour permettre leur distribution et utilisation par la communauté
scientifique. Dans un premier temps, seulement la classification supervisée sera considérée. Une fois les différents objectifs pré-cités remplis, les modèles construits serviront de support pour la définition d’une version non-supervisée de l’algorithme, et à plus long terme, d’une version pour l’estimation de variables (indices de biodiversité …).
Candidature Pour candidater, prendre contact avec les co-encadrants par courriel (joindre un CV détaillé) :
— mathieu.fauvel@ensat.fr
— stephane.girard@inria.fr
— serge.iovleff@inria.fr
Les candidatures sont ouvertes jusqu’à mi-Mars 2018.
Pour plus de détails: Sujet de Thèse