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dc.contributor.authorKhachatrian, Eduard
dc.contributor.authorChlaily, Saloua
dc.contributor.authorEltoft, Torbjørn
dc.contributor.authorMarinoni, Andrea
dc.date.accessioned2023-10-03T07:47:06Z
dc.date.available2023-10-03T07:47:06Z
dc.date.issued2021
dc.description.abstractAutomatic ice charting cannot be achieved using only SAR modalities. It is fundamental to combine information from other remote sensors with different characteristics for more reliable sea ice characterization. In this paper, we employ principal feature analysis (PFA) to select significant information from multimodal remote sensing data. PFA is a simple yet very effective approach that can be applied to several types of data without loss of physical interpretability. Considering that different homogeneous regions require different types of information, we perform the selection patch-wise. Accordingly, by exploiting the spatial information, we increase the robustness and accuracy of PFA.en_US
dc.identifier.citationKhachatrian, Chlaily, Eltoft, Marinoni: Selecting principal attributes in multimodal remote sensing for sea ice characterization. In: VDE V. EUSAR 2021 : 13th European Conference on Synthetic Aperture Radar 29 March – 1 April 2021, Online Event, 2021. VDE Verlag GmbH p. 531-536en_US
dc.identifier.cristinIDFRIDAID 1930471
dc.identifier.isbn978-3-8007-5457-1
dc.identifier.urihttps://hdl.handle.net/10037/31385
dc.language.isoengen_US
dc.publisherVDE Verlagen_US
dc.relation.projectIDNorges forskningsråd: 237906en_US
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2021 The Author(s)en_US
dc.titleSelecting principal attributes in multimodal remote sensing for sea ice characterizationen_US
dc.type.versionsubmittedVersionen_US
dc.typeChapteren_US
dc.typeBokkapittelen_US


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