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dc.contributor.authorDomben, Erik Seip
dc.contributor.authorSharma, Puneet
dc.contributor.authorMann, Ingrid
dc.date.accessioned2023-09-08T11:00:58Z
dc.date.available2023-09-08T11:00:58Z
dc.date.issued2023-08-31
dc.description.abstractPolar mesospheric summer echoes (PMSE) are radar echoes that are observed in the mesosphere during the arctic summer months in the polar regions. By studying PMSE, researchers can gain insights into physical and chemical processes that occur in the upper atmosphere—specifically, in the 80 to 90 km altitude range. In this paper, we employ fully convolutional networks such as UNET and UNET++ for the purpose of segmenting PMSE from the EISCAT VHF dataset. First, experiments are performed to find suitable weights and hyperparameters for UNET and UNET++. Second, different loss functions are tested to find one suitable for our task. Third, as the number of PMSE samples used is relatively small, this can lead to poor generalization. To address this, image-level and object-level augmentation methods are employed. Fourth, we briefly explain our findings by employing layerwise relevance propagation.en_US
dc.identifier.citationDomben ES, Sharma P, Mann IB. Using Deep Learning Methods for Segmenting Polar Mesospheric Summer Echoes. Remote Sensing. 2023;15(17)en_US
dc.identifier.cristinIDFRIDAID 2171901
dc.identifier.doi10.3390/rs15174291
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10037/30833
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalRemote Sensing
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2023 The Author(s)en_US
dc.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleUsing Deep Learning Methods for Segmenting Polar Mesospheric Summer Echoesen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_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)