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dc.contributor.authorBlix, Katalin
dc.contributor.authorEspeseth, Martine
dc.contributor.authorEltoft, Torbjørn
dc.date.accessioned2021-04-26T12:59:43Z
dc.date.available2021-04-26T12:59:43Z
dc.date.issued2021-02-17
dc.description.abstractThis paper addresses the problem of up-scaling full polarimetric (quad-pol) parameters from small quad-pol synthetic aperture radar (SAR) scenes to large dual-pol scenes, using a sophisticated Machine Learning (ML) method, namely the Gaussian Process Regression (GPR). The approach is to let the GPR model learn the relationships between the dual-pol input data and the quad-pol parameters on a quad-pol scene, and then extrapolate the relationships to the whole dual-pol scene. We demonstrate the procedure on two pairs of quadpol Radarsat-2 (RS2) and dual-pol ScanSAR Sentinel-1 (S1) scenes, acquired less than 20 minutes apart. The results are visualised as pixel-wise parametric maps, supported by three quantitative regression performance measures. In addition, we show certainty level maps for the estimated parameters. Our results indicate the potential of using the ML GPR model to upscale quad-pol scenes to large dual-pol images.en_US
dc.description© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.en_US
dc.identifier.citationBlix K, Espeseth M, Eltoft T. Comparison of Machine Learning Methods for Predicting Quad-Polarimetric Parameters from Dual-Polarimetric SAR Data. IEEE International Geoscience and Remote Sensing Symposium proceedings. 2020en_US
dc.identifier.cristinIDFRIDAID 1807873
dc.identifier.doi10.1109/IGARSS39084.2020.9324192
dc.identifier.issn2153-6996
dc.identifier.issn2153-7003
dc.identifier.urihttps://hdl.handle.net/10037/21057
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.journalIEEE International Geoscience and Remote Sensing Symposium proceedings
dc.relation.projectIDinfo:eu-repo/grantAgreement/RCN/SFI/237906/Norway/Centre for Integrated Remote Sensing and Forecasting for Arctic Operations/CIRFA/en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.subjectVDP::Technology: 500::Information and communication technology: 550::Geographical information systems: 555en_US
dc.subjectVDP::Teknologi: 500::Informasjons- og kommunikasjonsteknologi: 550::Geografiske informasjonssystemer: 555en_US
dc.titleComparison of Machine Learning Methods for Predicting Quad-Polarimetric Parameters from Dual-Polarimetric SAR Dataen_US
dc.type.versionacceptedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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