FN Archimer Export Format
PT C
TI Data-driven assimilation of irregularly-sampled image time series
BT 
AF FABLET, Ronan
   VIET, P.
   LGUENSAT, R.
   CHAPRON, Bertrand
AS 1:1;2:1;3:1;4:2;
FF 1:;2:;3:;4:PDG-ODE-LOPS-SIAM;
C1 IMT Atlantique, Brest, France.
   IFREMER, Brest, France.
C2 IMT ATLANTIQUE, FRANCE
   IFREMER, FRANCE
SI BREST
SE PDG-ODE-LOPS-SIAM
UM LOPS
IN WOS Ifremer jusqu'en 2018
   copubli-france
UR https://archimer.ifremer.fr/doc/00403/51440/52009.pdf
LA English
DT Proceedings paper
DE ;Data assimilation;irregular sampling;image time series;data-driven methods;Kalman methods
AB 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. 
PY 2017
CT Image Processing (ICIP), 2017 IEEE International Conference on. ISSN 2381-8549 . 5p.
DI 10.1109/ICIP.2017.8297094
ID 51440
ER

EF