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dc.contributor.authorKhachatrian, Eduard
dc.contributor.authorChlaily, Saloua
dc.contributor.authorEltoft, Torbjørn
dc.contributor.authorDierking, Wolfgang Fritz Otto
dc.contributor.authorDinessen, Frode
dc.contributor.authorMarinoni, Andrea
dc.date.accessioned2021-12-27T13:51:25Z
dc.date.available2021-12-27T13:51:25Z
dc.date.issued2021-07-26
dc.description.abstractIt is of considerable benefit to combine information obtained from different satellite sensors to achieve advanced and improved characterization of sea ice conditions. However, it is also true that not all the information is relevant. It may be redundant, corrupted, or unnecessary for the given task, hence decreasing the performance of the algorithms. Therefore, it is crucial to select an optimal set of image attributes which provides the relevant information content to enhance the efficiency and accuracy of the image interpretation and retrieval of geophysical parameters. Comprehensive studies have been focused on the analysis of relevant features for sea ice analysis obtained from different sensors, especially synthetic aperture radar. However, the outcomes of these studies are mostly data and application-dependent and can, therefore, rarely be generalized. In this article, we employ a feature selection method based on graph Laplacians, which is fully automatic and easy to implement. The proposed approach assesses relevant information on a global and local level using two metrics and selects relevant features for different regions of an image according to their physical characteristics and observation conditions. In the recent study, we investigate the effectiveness of this approach for sea ice classification, using different multi-sensor data combinations. Experiments show the advantage of applying multi-sensor data sets and demonstrate that the attributes selected by our method result in high classification accuracies. We demonstrate that our approach automatically considers varying technical, sensor-specific, environmental, and sea ice conditions by employing flexible and adaptive feature selection method as a pre-processing step.en_US
dc.description.sponsorshipNorges forskningsråden_US
dc.identifier.citationKhachatrian, Chlaily, Eltoft, Dierking, Dinessen, Marinoni. Automatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classification. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021;14:9025-9037en_US
dc.identifier.cristinIDFRIDAID 1937726
dc.identifier.doi10.1109/JSTARS.2021.3099398
dc.identifier.issn1939-1404
dc.identifier.issn2151-1535
dc.identifier.urihttps://hdl.handle.net/10037/23514
dc.language.isoengen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_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/NRC/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.subjectFjernanalyse / Remote sensingen_US
dc.subjectSjøis / Sea iceen_US
dc.subjectSyntetisk aperturradar / Synthetic aperture radaren_US
dc.subjectVDP::Technology: 500en_US
dc.subjectVDP::Teknologi: 500en_US
dc.titleAutomatic Selection of Relevant Attributes for Multi-Sensor Remote Sensing Analysis: A Case Study on Sea Ice Classificationen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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