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dc.contributor.authorKhachatrian, Eduard
dc.contributor.authorChlaily, Saloua
dc.contributor.authorEltoft, Torbjørn
dc.contributor.authorMarinoni, Andrea
dc.date.accessioned2021-12-28T13:44:25Z
dc.date.available2021-12-28T13:44:25Z
dc.date.issued2021-11-13
dc.description.abstractWhen dealing with multivariate remotely sensed records collected by multiple sensors, an accurate selection of information at the data, feature, or decision level is instrumental in improving the scenes’ characterization. This will also enhance the system’s efficiency and provide more details on modeling the physical phenomena occurring on the Earth’s surface. In this article, we introduce a flexible and efficient method based on graph Laplacians for information selection at different levels of data fusion. The proposed approach combines data structure and information content to address the limitations of existing graph-Laplacian-based methods in dealing with heterogeneous datasets. Moreover, it adapts the selection to each homogenous area of the considered images according to their underlying properties. Experimental tests carried out on several multivariate remote sensing datasets show the consistency of the proposed approach.en_US
dc.identifier.citationKhachatrian, Chlaily, Eltoft, Marinoni. A Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalization. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021;14:11546-11566en_US
dc.identifier.cristinIDFRIDAID 1955100
dc.identifier.doi10.1109/JSTARS.2021.3124308
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.urihttps://hdl.handle.net/10037/23528
dc.language.isoengen_US
dc.publisherIEEEen_US
dc.relation.ispartofKhachatrian, E. (2023). Multimodal Integrated Remote Sensing for Arctic Sea Ice Monitoring. (Doctoral thesis). <a href=https://hdl.handle.net/10037/29338>https://hdl.handle.net/10037/29338</a>.
dc.relation.journalIEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
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::Mathematics and natural science: 400::Physics: 430en_US
dc.subjectVDP::Matematikk og Naturvitenskap: 400::Fysikk: 430en_US
dc.titleA Multimodal Feature Selection Method for Remote Sensing Data Analysis Based on Double Graph Laplacian Diagonalizationen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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