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dc.contributor.authorBlix, Katalin
dc.contributor.authorEspeseth, Martine
dc.contributor.authorEltoft, Torbjørn
dc.date.accessioned2019-02-27T18:55:44Z
dc.date.available2019-02-27T18:55:44Z
dc.date.issued2018-06
dc.description.abstractIn this paper, we investigated the capabilities of the Gaussian Process Regression (GPR) algorithm in predicting of two quad-polarimetric parameters (relevant for sea ice analysis) from 6-dimensional dual-polarimetric input vectors. The GRP is trained on few hundred samples selected randomly from an image subset, and tested on the entire image. The performance is assessed by visual comparisons, and by quantifying two regression performance statistical measures. The results of the regression showed big variations from scene to scene, and between the estimated output parameters, but the overall assessment is that the method gave surprisingly good correspondence to the real quad-polarimetric parameters.en_US
dc.descriptionSource at <a href=https://www.vde-verlag.de/proceedings-en/454636136.html>https://www.vde-verlag.de/proceedings-en/454636136.html</a>.en_US
dc.identifier.citationBlix, K., Espeseth, M. & Eltoft, T. (2018). Machine Learning simulations of quad-polarimetric features from dual-polarimetric measurements over sea ice. <i>EUSAR 2018 - 12th European Conference on Synthetic Aperture Radar, Electronic proceedings</i>, 661-665.en_US
dc.identifier.cristinIDFRIDAID 1627467
dc.identifier.issn2197-4403
dc.identifier.urihttps://hdl.handle.net/10037/14787
dc.language.isoengen_US
dc.publisherVDE VERLAG GMBHen_US
dc.relation.journalElectronic proceedings (EUSAR)
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.subjectVDP::Technology: 500::Environmental engineering: 610en_US
dc.subjectVDP::Teknologi: 500::Miljøteknologi: 610en_US
dc.subjectVDP::Mathematics and natural science: 400::Geosciences: 450::Oceanography: 452en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Geofag: 450::Oseanografi: 452en_US
dc.subjectSea iceen_US
dc.subjectGaussian Process Regressionen_US
dc.subjectMachine Learningen_US
dc.titleMachine Learning simulations of quad-polarimetric features from dual-polarimetric measurements over sea iceen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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