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dc.contributor.authorKhaleghian, Salman
dc.contributor.authorUllah, Habib
dc.contributor.authorKræmer, Thomas
dc.contributor.authorEltoft, Torbjørn
dc.contributor.authorMarinoni, Andrea
dc.date.accessioned2022-03-08T09:12:51Z
dc.date.available2022-03-08T09:12:51Z
dc.date.issued2021-10-14
dc.description.abstractIn this article, we propose a novelteacher–student-based label propagation deep semisupervised learning (TSLP-SSL) method for sea ice classification based on Sentinel-1 synthetic aperture radar data. For sea ice classification, labeling the data precisely is very time consuming and requires expert knowledge. Our method efficiently learns sea ice characteristics from a limited number of labeled samples and a relatively large number of unlabeled samples. Therefore, our method addresses the key challenge of using a limited number of precisely labeled samples to achieve generalization capability by discovering the underlying sea ice characteristics also from unlabeled data. We perform experimental analysis considering a standard dataset consisting of properly labeled sea ice data spanning over different time slots of the year. Both qualitative and quantitative results obtained on this dataset show that our proposed TSLP-SSL method outperforms deep supervised and semisupervised reference methods.en_US
dc.identifier.citationKhaleghian, Ullah, Kræmer, Eltoft, Marinoni. Deep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classification . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021;14:10761-10772en_US
dc.identifier.cristinIDFRIDAID 1958743
dc.identifier.doi10.1109/JSTARS.2021.3119485
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.urihttps://hdl.handle.net/10037/24319
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofKhaleghian, S. (2022). Scalable computing for earth observation - Application on Sea Ice analysis. (Doctoral thesis). <a href=https://hdl.handle.net/10037/27513>https://hdl.handle.net/10037/27513</a>.
dc.relation.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
dc.relation.projectIDinfo:eu-repo/grantAgreement/EC/INDUSTRIAL LEADERSHIP /825258/Greece/From Copernicus Big Data to Extreme Earth Analytics/ExtremeEarth /en_US
dc.relation.urihttps://hdl.handle.net/11250/2832772
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.titleDeep Semisupervised Teacher–Student Model Based on Label Propagation for Sea Ice Classificationen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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