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Ratio-Based Multitemporal SAR Images Denoising: RABASAR

hal.structure.identifierLaboratoire Traitement et Communication de l'Information [LTCI]
dc.contributor.authorZHAO, Weiying
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorDELEDALLE, Charles-Alban
hal.structure.identifierLaboratoire Hubert Curien [LHC]
dc.contributor.authorDENIS, Loïc
hal.structure.identifierLaboratoire Traitement et Communication de l'Information [LTCI]
dc.contributor.authorMAÎTRE, Henri
hal.structure.identifierLaboratoire Traitement et Communication de l'Information [LTCI]
dc.contributor.authorNICOLAS, Jean-Marie
hal.structure.identifierLaboratoire Traitement et Communication de l'Information [LTCI]
dc.contributor.authorTUPIN, Florence
dc.date.accessioned2024-04-04T03:03:51Z
dc.date.available2024-04-04T03:03:51Z
dc.date.issued2019
dc.identifier.issn0196-2892
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193105
dc.description.abstractEnIn this paper, we propose a fast and efficient multitemporal despeckling method. The key idea of the proposed approach is the use of the ratio image, provided by the ratio between an image and the temporal mean of the stack. This ratio image is easier to denoise than a single image thanks to its improved stationarity. Besides, temporally stable thin structures are well preserved thanks to the multi-temporal mean.The proposed approach can be divided into three steps: 1) estimation of a “super-image” by temporal averaging andpossibly spatial denoising; 2) denoising of the ratio between the noisy image of interest and the “super-image”; 3) computation of the denoised image by re-multiplying the denoised ratio by the “super-image”.Because of the improved spatial stationarity of the ratio images, denoising these ratio images with a speckle-reductionmethod is more effective than denoising images from the original multi-temporal stack. The amount of data that is jointlyprocessed is also reduced compared to other methods through the use of the “super-image” that sums up the temporal stack. The comparison with several state-of-the-art reference methods shows better results numerically (peak signal-noise-ratio, structure similarity index) as well as visually on simulated and SAR time series. The proposed ratio-based denoising framework successfully extends single-image SAR denoising methods to time series by exploiting the persistence of many geometrical structures.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers
dc.subject.enSAR
dc.subject.ensuper- image
dc.subject.enratio image
dc.subject.enMulti-temporal SAR series
dc.subject.enspeckle reduction
dc.title.enRatio-Based Multitemporal SAR Images Denoising: RABASAR
dc.typeArticle de revue
dc.identifier.doi10.1109/TGRS.2018.2885683
dc.subject.halInformatique [cs]/Traitement du signal et de l'image
bordeaux.journalIEEE Transactions on Geoscience and Remote Sensing
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.peerReviewedoui
hal.identifierhal-01791355
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01791355v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=IEEE%20Transactions%20on%20Geoscience%20and%20Remote%20Sensing&rft.date=2019&rft.eissn=0196-2892&rft.issn=0196-2892&rft.au=ZHAO,%20Weiying&DELEDALLE,%20Charles-Alban&DENIS,%20Lo%C3%AFc&MA%C3%8ETRE,%20Henri&NICOLAS,%20Jean-Marie&rft.genre=article


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