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Communication Dans Un Congrès Année : 2017

Data-driven assimilation of irregularly-sampled image time series

Résumé

We address in this paper the reconstruction of irregurlarly-sampled image time series with an emphasis on geophysical remote sensing data. We develop a data-driven approach, referred to as an analog assimilation and stated as an ensemble Kalman method. Contrary to model-driven assimilation models, we do not exploit a physically-derived dynamic prior but we build a data-driven dynamic prior from a representative dataset of the considered image dynamics. Our contribution is here to extend analog assimilation to images, which involve high-dimensional state space. We combine patch-based representations to a multiscale PCA-constrained decomposition. Numerical experiments for the interpolation of missing data in satellite-derived ocean remote sensing images demonstrate the relevance of the proposed scheme. It outperforms the classical optimal interpolation with a relative RMSE gain of about 50% for the considered case study.

Dates et versions

hal-01757749 , version 1 (03-04-2018)

Identifiants

Citer

Ronan Fablet, Phi Huynh Viet, Redouane Lguensat, Bertrand Chapron. Data-driven assimilation of irregularly-sampled image time series. ICIP 2017 : IEEE International Conference on Image Processing, Sep 2017, Beijing, China. ⟨10.1109/ICIP.2017.8297094⟩. ⟨hal-01757749⟩
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