Interpolation de données manquantes dans des séquences multi-modales d'images géophysiques satellitaires

Cet article etudie l'estimation conjointe de donnees manquantes et de champs de deplacements dans des sequences multimodales d'observations satellitaires geophysiques. La complexite de la tâche est liee au taux eleve de donnees manquantes (entre 20% et 90%) pour des observations journalieres de haute resolution et la reconstruction de structures fines en accord avec la dynamique sous jacente. Nous avons developpe une methode basee sur l'assimilation variationnelle de donnees pour des series multimodales et multi-resolutions. A l'aide de donnees synthetiques et de donnees reelles de la surface oceanique, une evaluation numerique et qualitative demontre l'apport de deux composantes cles du modele propose: la fusion d'informations multimodales a partir d'une contrainte geometrique basee sur les structures frontales, et la methode d'assimilation variationnelle utilisant comme a priori dynamique un modele d'advection-diffusion. Les experimentations conduites montrent que de bonnes performances de reconstruction sont obtenues pour les observations hautes resolutions en depit du pourcentage eleve de donnees manquantes

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