Assimilation of Images via Dictionary Learning-Based Sparsity Regularization Strategy: An Application for Retrieving Fluid Flows

Autor: Long Li, Jianwei Ma, François-Xavier Le Dimet, Arthur Vidard

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, 2022, 60, pp.5907120:1-20. ⟨10.1109/TGRS.2021.3110799⟩

International audience; In this work, we propose a structure sparsity regularization strategy in the framework of 4-D variational data assimilation (4-D Var). In meteorology and oceanography, the number of unknown model variables is far fewer than th

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8cc4ae6877a8b6d87e9021892c6c5720
https://doi.org/10.1109/tgrs.2021.3110799

Images Assimilation: An Ocean Perspective

Autor: Innocent Souopgui, Hans Ngodock, François-Xavier Le Dimet

Publikováno v: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications (Vol. IV) ISBN: 9783030777210

This paper discusses the potential benefits of images assimilation in the context of operational oceanography, with the goal of eventually exploiting the dynamical information contained in sequences of ocean images to improve ocean model predictions.

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::c721a4bc8dff53c0f8cbc68accc69e31
https://doi.org/10.1007/978-3-030-77722-7_15

Sensitivity with respect to observations in variational data assimilation

Autor: François-Xavier Le Dimet, Victor P. Shutyaev, Elena Shubina

Publikováno v: Russian Journal of Numerical Analysis and Mathematical Modelling. 32:61-71

The problem of variational data assimilation for a nonlinear evolution model is formulated as an optimal control problem to find the initial condition function (analysis). The observation data, and hence the optimal solution, may contain uncertaintie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::791a29647b4e25485f9602bfe4e5e3f7
https://doi.org/10.1515/rnam-2017-0006

Hessian-based covariance approximations in variational data assimilation

Autor: Igor Yu. Gejadze, Victor P. Shutyaev, François-Xavier Le Dimet

Publikováno v: Russian Journal of Numerical Analysis and Mathematical Modelling
Russian Journal of Numerical Analysis and Mathematical Modelling, 2018, 33 (1), pp.25-39. ⟨10.1515/rnam-2018-0003⟩
Russian Journal of Numerical Analysis and Mathematical Modelling, De Gruyter, 2018, 33 (1), pp.25-39. ⟨10.1515/rnam-2018-0003⟩

[Departement_IRSTEA]Eaux [TR1_IRSTEA]GEUSI; International audience; The problem of variational data assimilation (estimation) for a nonlinear model is considered in general operator formulation. Hessian-based methods are presented to compute the esti

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::40e1bc5cd7e3b4aca679d0f3dbb5e73b
https://hal.science/hal-02068763/document

A level-set based image assimilation method: Potential applications for predicting the movement of oil spills

Autor: Arthur Vidard, Jianwei Ma, François-Xavier Le Dimet, Long Li

Publikováno v: IEEE Transactions on Geoscience and Remote Sensing
IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2017, 55 (11), pp.6330-6343. ⟨10.1109/TGRS.2017.2726013⟩
IEEE Transactions on Geoscience and Remote Sensing, 2017, 55 (11), pp.6330-6343. ⟨10.1109/TGRS.2017.2726013⟩

In this paper, we present a novel method for assimilating geometric information from observed images. Image assimilation technology fully utilizes structural information from the dynamics of the images to retrieve the state of a system and thus to be

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22cb5bb12b7a4e206b099d009676dc76
https://hal.inria.fr/hal-01411878/file/Manuscript-onecolumn.pdf