Regression Networks For Calculating Englacial Layer Thickness

Simple item page
dc.contributor.authorVarshney, Debvrat
dc.contributor.authorRahnemoonfar, Maryam
dc.contributor.authorYari, Masoud
dc.contributor.authorPaden, John
dc.date.accessioned2022-03-04T20:14:52Z
dc.date.available2022-03-04T20:14:52Z
dc.date.issued2021-06-12
dc.description.abstractIce thickness estimation is an important aspect of ice sheet studies. In this work, we use convolutional neural networks with multiple output nodes to regress and learn the thickness of internal ice layers in Snow Radar images collected in northwest Greenland. We experiment with some state-of-the-art networks and find that with the residual connections of ResNet50, we could achieve a mean absolute error of 1.251 pixels over the test set. Such regression-based networks can further be improved by embedding domain knowledge and radar information in the neural network in order to reduce the requirement of manual annotations.en_US
dc.description.urihttps://arxiv.org/abs/2104.04654en_US
dc.format.extent4 pagesen_US
dc.genrejournal articlesen_US
dc.genrepreprintsen_US
dc.identifierdoi:10.13016/m2v2cs-21t8
dc.identifier.urihttps://doi.org/10.48550/arXiv.2104.04654
dc.identifier.urihttp://hdl.handle.net/11603/24350
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.en_US
dc.titleRegression Networks For Calculating Englacial Layer Thicknessen_US
dc.typeTexten_US
dcterms.creatorhttps://orcid.org/0000-0002-9949-8683en_US

Files

License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Information Systems Department
UMBC Faculty Collection