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My ICIP 2006 Schedule
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Paper Detail
| Paper: | MP-P8.7 |
| Session: | Motion Detection and Estimation |
| Time: | Monday, October 9, 14:20 - 17:00 |
| Presentation: |
Poster |
| Topic: |
Motion Detection and Estimation: Parametric models for motion estimation |
| Title: |
ESTIMATION OF A MOTION FIELD ON SATELLITE IMAGES FROM A SIMPLIFIED OCEAN CIRCULATION MODEL |
| Authors: |
Isabelle Herlin; INRIA | | | | | Etienne Huot; INRIA | | | | | Jean-Paul Berroir; INRIA | | | | | François-Xavier Le Dimet; INRIA - LMC/IMAG | | | | | Gennady Korotaev; Marine Hydrophysical Institute | | |
| Abstract: |
This paper aims at estimating the apparent velocity from sequences of satellite images. This study is an illustration of a more general methodology for generating, from satellite images, pseudo-observations of physical variables, that are assimilated within a geophysical forecast model in view of improving the quality of its results. In the case of the presented study, the used satellite images are sequences of Sea Surface Temperature (SST), from which pseudo-observations of sea surface velocities are generated, and assimilated within an ocean circulation model. The originality of the approach lies in the definition of an Image Model, that predicts the evolution of image information -here, SST- as a function of the pseudo-observations -here, surface velocity-. Satellite data are then assimilated within the Image Model, yielding an estimation of the pseudo-observations. In the case of this paper, this methods allows the estimation of sea surface velocities,even when large parts of the satellite images are corrupted by clouds. The Image Model plays the role of an intermediate model, between satellite data and the forecast model, and allows the assimilation of image information which is not directly linked to the state variables of the forecast model. |
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