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dc.contributor.authorTaelman, Catherine Cecilia A
dc.contributor.authorChlaily, Saloua
dc.contributor.authorKhachatrian, Eduard
dc.contributor.authorVan Der Sommen, Fons
dc.contributor.authorMarinoni, Andrea
dc.description.abstractThe field of Earth observation is dealing with increasingly large, multimodal data sets. An important processing step consists of providing these data sets with labels. However, standard label propagation algorithms cannot be applied to multimodal remote sensing data for two reasons. First, multimodal data is heterogeneous while classic label propagation algorithms assume a homogeneous network. Second, real-world data can show both homophily (’birds of a feather flock together’) and heterophily (’opposites attract’) during propagation, while standard algorithms only consider homophily. Both shortcomings are addressed in this work and the result is a graph-based label propagation algorithm for multimodal data that includes homophily and/or heterophily. Furthermore, the method is also able to transfer information between uni- and multimodal data. Experiments on the remote sensing data set of Houston, which contains a LiDAR and a hyperspectral image, show that our approach ties state-of-the-art methods for classification with an OA of 91.4%, while being more flexible and not constrained to a specific data set or a specific combination of modalities.en_US
dc.descriptionSource at <a href=></a>.en_US
dc.identifier.citationTaelman, Chlaily, Khachatrian, Van Der Sommen, Marinoni. On the Exploitation of Heterophily in Graph-Based Multimodal Remote Sensing Data Analysis. CEUR Workshop Proceedings. 2022en_US
dc.identifier.cristinIDFRIDAID 2078738
dc.publisherCEUR Workshop Proceedingsen_US
dc.relation.journalCEUR Workshop Proceedings
dc.relation.projectIDNorges forskningsråd: 237906en_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleOn the Exploitation of Heterophily in Graph-Based Multimodal Remote Sensing Data Analysisen_US
dc.typeJournal articleen_US
dc.typePeer revieweden_US

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Attribution 4.0 International (CC BY 4.0)
Except where otherwise noted, this item's license is described as Attribution 4.0 International (CC BY 4.0)