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hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
hal.structure.identifierInstitut Mediterrani d'Estudis Avancats [IMEDEA]
dc.contributor.authorGOMEZ-NAVARRO, Laura
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorCOSME, Emmanuel
hal.structure.identifierInstitut des Géosciences de l’Environnement [IGE]
dc.contributor.authorSOMMER, Julien Le
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorPAPADAKIS, Nicolas
hal.structure.identifierInstitut Mediterrani d'Estudis Avancats [IMEDEA]
dc.contributor.authorPASCUAL, Ananda
dc.date.accessioned2024-04-04T02:48:51Z
dc.date.available2024-04-04T02:48:51Z
dc.date.issued2020-02
dc.identifier.issn2072-4292
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/191776
dc.description.abstractEnIn a near future, the Surface Water Ocean Topography (SWOT) mission will provide images of altimetric data at kilometric resolution. This unprecedented 2-dimensional data structure will allow the estimation of geostrophy-related quantities that are essential for studying the ocean surface dynamics and for data assimilation uses. To estimate these quantities, i.e. compute spatial derivatives of the Sea Surface Height (SSH) measurements, the small-scale noise expected to affect the SWOT data must be smoothed out while minimizing the loss of relevant, physical SSH information. This paper introduces a new technique for de-noising the future SWOT SSH images. The de-noising model is formulated as a regularized least-square problem with a Tikhonov regularization based on the first, second, and third-order derivatives of SSH. The method is implemented and compared to other, convolution-based filtering methods with boxcar and Gaussian kernels. This is performed using a large set of pseudo-SWOT data generated in the Western Mediterranean Sea, from a 1/60 • simulation and the SWOT simulator. Based on Root Mean Square Error and spectral diagnostics, our de-noising method shows a better performance than the convolution-based methods. We find the optimal parametrization to be when only the second-order SSH derivative is penalized. This de-noising reduces the spatial scale resolved by SWOT by a factor of 2, and at 10 km wavelengths the noise level is reduced by 10 4 and 10 3 for Summer and Winter respectively. This is encouraging for the processing of the future SWOT data.
dc.description.sponsorshipVers des produits de la circulation océanique de surface à la résolution kilométrique : exploitation de la future mission altimétrique SWOT - ANR-17-CE01-0009
dc.language.isoen
dc.publisherMDPI
dc.subject.enSWOT
dc.subject.enDe-noising
dc.subject.enVariational regularization
dc.subject.enwestern Mediterranean
dc.title.enDevelopment of an Image De-Noising Method in Preparation for the Surface Water and Ocean Topography Satellite Mission
dc.typeArticle de revue
dc.identifier.doi10.3390/rs12040734
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
dc.subject.halPlanète et Univers [physics]/Sciences de la Terre/Océanographie
dc.subject.halSciences de l'environnement/Ingénierie de l'environnement
bordeaux.journalRemote Sensing
bordeaux.page734
bordeaux.volume12
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.issue4
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-02490206
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-02490206v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Remote%20Sensing&rft.date=2020-02&rft.volume=12&rft.issue=4&rft.spage=734&rft.epage=734&rft.eissn=2072-4292&rft.issn=2072-4292&rft.au=GOMEZ-NAVARRO,%20Laura&COSME,%20Emmanuel&SOMMER,%20Julien%20Le&PAPADAKIS,%20Nicolas&PASCUAL,%20Ananda&rft.genre=article


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