Data-driven assimilation of irregularly-sampled image time series

We address in this paper the reconstruction of irregurlarlysampled 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.

Keyword(s)

Data assimilation, irregular sampling, image time series, data-driven methods, Kalman methods

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Fablet Ronan, Viet P., Lguensat R., Chapron Bertrand (2017). Data-driven assimilation of irregularly-sampled image time series. Image Processing (ICIP), 2017 IEEE International Conference on. ISSN 2381-8549 . 5p.. https://doi.org/10.1109/ICIP.2017.8297094, https://archimer.ifremer.fr/doc/00403/51440/

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