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dc.contributor.authorSapkota, Rajendra
dc.contributor.authorSharma, Puneet
dc.contributor.authorMann, Ingrid
dc.date.accessioned2022-11-11T12:13:08Z
dc.date.available2022-11-11T12:13:08Z
dc.date.issued2022-05-10
dc.description.abstractOptically thin layers of tiny ice particles near the summer mesopause, known as noctilucent clouds, are of significant interest within the aeronomy and climate science communities. Groundbased optical cameras mounted at various locations in the arctic regions collect the dataset during favorable summer times. In this paper, first, we compare the performances of various deep learningbased image classifiers against a baseline machine learning model trained with support vector machine (SVM) algorithm to identify an effective and lightweight model for the classification of noctilucent clouds. The SVM classifier is trained with histogram of oriented gradient (HOG) features, and deep learning models such as SqueezeNet, ShuffleNet, MobileNet, and Resnet are fine-tuned based on the dataset. The dataset includes images observed from different locations in northern Europe with varied weather conditions. Second, we investigate the most informative pixels for the classification decision on test images. The pixel-level attributions calculated using the guide back-propagation algorithm are visualized as saliency maps. Our results indicate that the SqueezeNet model achieves an F1 score of 0.95. In addition, SqueezeNet is the lightest model used in our experiments, and the saliency maps obtained for a set of test images correspond better with relevant regions associated with noctilucent clouds.en_US
dc.identifier.citationSapkota, Sharma, Mann. Comparison of Deep Learning Models for the Classification of Noctilucent Cloud Images. Remote Sensing. 2022;14(10)en_US
dc.identifier.cristinIDFRIDAID 2045685
dc.identifier.doi10.3390/rs14102306
dc.identifier.issn2072-4292
dc.identifier.urihttps://hdl.handle.net/10037/27344
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.rights.urihttps://creativecommons.org/licenses/by/4.0en_US
dc.rightsAttribution 4.0 International (CC BY 4.0)en_US
dc.titleComparison of Deep Learning Models for the Classification of Noctilucent Cloud Imagesen_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)