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dc.contributor.authorJozwicki, Dorota
dc.contributor.authorSharma, Puneet
dc.contributor.authorMann, Ingrid
dc.contributor.authorHoppe, Ulf-Peter Jürgen
dc.date.accessioned2022-06-24T10:13:16Z
dc.date.available2022-06-24T10:13:16Z
dc.date.issued2022-06-22
dc.description.abstractEISCAT VHF radar data are used for observing, monitoring, and understanding Earth’s upper atmosphere. This paper presents an approach to segment Polar Mesospheric Summer Echoes (PMSE) from datasets obtained from EISCAT VHF radar data. The data consist of 30 observations days, corresponding to 56,250 data samples. We manually labeled the data into three different categories: PMSE, Ionospheric background, and Background noise. For segmentation, we employed random forests on a set of simple features. These features include: altitude derivative, time derivative, mean, median, standard deviation, minimum, and maximum values corresponding to neighborhood sizes ranging from 3 by 3 to 11 by 11 pixels. Next, in order to reduce the model bias and variance, we employed a method that decreases the weight applied to pixel labels with large uncertainty. Our results indicate that, first, it is possible to segment PMSE from the data using random forests. Second, the weighted-down labels technique improves the performance of the random forests method.en_US
dc.identifier.citationJozwicki, D.; Sharma, P.; Mann, I.; Hoppe U.-P. Segmentation of PMSE Data Using Random Forests. Remote Sens. 2022, 14, 2976en_US
dc.identifier.cristinIDFRIDAID 2032781
dc.identifier.doihttps://doi.org/10.3390/rs14132976
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10037/25561
dc.language.isoengen_US
dc.publisherMDPIen_US
dc.relation.journalRemote Sensing
dc.rights.accessRightsopenAccessen_US
dc.rights.holderCopyright 2022 The Author(s)en_US
dc.titleSegmentation of PMSE data using random forestsen_US
dc.type.versionpublishedVersionen_US
dc.typeJournal articleen_US
dc.typeTidsskriftartikkelen_US
dc.typePeer revieweden_US


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