TY - CPAPER T1 - Data-driven assimilation of irregularly-sampled image time series A1 - Fablet,Ronan A1 - Viet,P. A1 - Lguensat,R. A1 - Chapron,Bertrand AD - IMT Atlantique, Brest, France. AD - IFREMER, Brest, France. UR - https://archimer.ifremer.fr/doc/00403/51440/ DO - 10.1109/ICIP.2017.8297094 KW - Data assimilation KW - irregular sampling KW - image time series KW - data-driven methods KW - Kalman methods N2 - 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. Y1 - 2017 CY - ICIP 2017 - IEEE International Conference on Image Processing. 17-20 September 2017, Beijing, China SO - Image Processing (ICIP), 2017 IEEE International Conference on. ISSN 2381-8549 . 5p. ID - 51440 ER -