Image assimilation for motion estimation of atmospheric layers with shallow-water model

Nicolas Papadakis 1 Patrick Héas 1 Etienne Memin 1
1 VISTA - Vision spatio-temporelle et active
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : The complexity of dynamical laws governing 3D atmospheric flows associated to incomplete and noisy observations makes very difficult the recovery of atmospheric dynamics from satellite images sequences. In this paper, we face the challenging problem of joint estimation of time-consistent horizontal motion fields and pressure maps at various atmospheric depths. Based on a vertical decomposition of the atmosphere, we propose a dense motion estimator relying on a multi-layer dynamical model. Noisy and incomplete pressure maps obtained from satellite images are reconstructed according to shallow-water model on each cloud layer using a framework derived from data assimilation. While reconstructing dense pressure maps, this variational process estimates time-consistent horizontal motion fields related to the multi-layer model. The proposed approach is validated on a synthetic example and applied to a real world meteorological satellite image sequence.
Type de document :
Communication dans un congrès
Asian Conference on Computer Vision (ACCV'08), Nov 2007, Tokyo, Japan. pp.864-874, 2007
Liste complète des métadonnées

https://hal.archives-ouvertes.fr/hal-00596194
Contributeur : Nicolas Papadakis <>
Soumis le : jeudi 26 mai 2011 - 16:38:03
Dernière modification le : vendredi 13 janvier 2017 - 14:18:47

Identifiants

  • HAL Id : hal-00596194, version 1

Collections

Citation

Nicolas Papadakis, Patrick Héas, Etienne Memin. Image assimilation for motion estimation of atmospheric layers with shallow-water model. Asian Conference on Computer Vision (ACCV'08), Nov 2007, Tokyo, Japan. pp.864-874, 2007. <hal-00596194>

Partager

Métriques

Consultations de la notice

124