| Registro completo |
| Provedor de dados: |
ArchiMer
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| País: |
France
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| Título: |
Data-driven assimilation of irregularly-sampled image time series
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| Autores: |
Fablet, Ronan
Viet, P.
Lguensat, R.
Chapron, Bertrand
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| Data: |
2017
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| Ano: |
2017
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| Palavras-chave: |
Data assimilation
Irregular sampling
Image time series
Data-driven methods
Kalman methods
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| Resumo: |
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.
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| Tipo: |
Text
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| Idioma: |
Inglês
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| Identificador: |
https://archimer.ifremer.fr/doc/00403/51440/52009.pdf
DOI:10.1109/ICIP.2017.8297094
https://archimer.ifremer.fr/doc/00403/51440/
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| Editor: |
Image Processing (ICIP), 2017 IEEE International Conference on. ISSN 2381-8549 . 5p.
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| Formato: |
application/pdf
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| Direitos: |
ICIP 2017. All rights reserved.
info:eu-repo/semantics/openAccess
restricted use
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