dc.contributor.author | Domben, Erik Seip | |
dc.contributor.author | Sharma, Puneet | |
dc.contributor.author | Mann, Ingrid | |
dc.date.accessioned | 2023-09-08T11:00:58Z | |
dc.date.available | 2023-09-08T11:00:58Z | |
dc.date.issued | 2023-08-31 | |
dc.description.abstract | Polar 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.citation | Domben ES, Sharma P, Mann IB. Using Deep Learning Methods for Segmenting Polar Mesospheric Summer Echoes. Remote Sensing. 2023;15(17) | en_US |
dc.identifier.cristinID | FRIDAID 2171901 | |
dc.identifier.doi | 10.3390/rs15174291 | |
dc.identifier.issn | 2072-4292 | |
dc.identifier.uri | https://hdl.handle.net/10037/30833 | |
dc.language.iso | eng | en_US |
dc.publisher | MDPI | en_US |
dc.relation.journal | Remote Sensing | |
dc.rights.accessRights | openAccess | en_US |
dc.rights.holder | Copyright 2023 The Author(s) | en_US |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0 | en_US |
dc.rights | Attribution 4.0 International (CC BY 4.0) | en_US |
dc.title | Using Deep Learning Methods for Segmenting Polar Mesospheric Summer Echoes | en_US |
dc.type.version | publishedVersion | en_US |
dc.type | Journal article | en_US |
dc.type | Tidsskriftartikkel | en_US |
dc.type | Peer reviewed | en_US |