Deep multi-scale learning for automatic tracking of internal layers of ice in radar data

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dc.contributor.authorRahnemoonfar, Maryam
dc.contributor.authorYari, Masoud
dc.contributor.authorPaden, John
dc.contributor.authorKoenig, Lora
dc.contributor.authorIbikunle, Oluwanisola
dc.date.accessioned2020-11-19T17:00:09Z
dc.date.available2020-11-19T17:00:09Z
dc.date.issued2020-10-12
dc.description.abstractIn this study, our goal is to track internal ice layers on the Snow Radar data collected by NASA Operation IceBridge. We examine the application of deep learning methods on radar data gathered from polar regions. Artificial intelligence techniques have displayed impressive success in many practical fields. Deep neural networks owe their success to the availability of massive labeled data. However, in many real-world problems, even when a large dataset is available, deep learning methods have shown less success, due to causes such as lack of a large labeled dataset, presence of noise in the data or missing data. In our radar data, the presence of noise is one of the main obstacles in utilizing popular deep learning methods such as transfer learning. Our experiments show that if the neural network is trained to detect contours of objects in electrooptical imagery, it can only track a low percentage of contours in radar data. Fine-tuning and further training do not provide any better results. However, we show that selecting the right model and training it on the radar imagery from the start yields far better results.en_US
dc.description.sponsorshipThis work is supported by NSF BIGDATA awards (IIS-1838230, IIS-1838024), IBM and Amazon. Lynn Montgomery assisted in the use of the ground truth data. We also would like to thank Annals of Glaciology Associate Chief Editor Dr. Dustin Schroeder for handling our paperen_US
dc.description.urihttps://www.cambridge.org/core/journals/journal-of-glaciology/article/deep-multiscale-learning-for-automatic-tracking-of-internal-layers-of-ice-in-radar-data/24695561130F7DEF3826B7B1F49CB479en_US
dc.format.extent10 pagesen_US
dc.genrejournal articlesen_US
dc.identifierdoi:10.13016/m2nmps-s2kg
dc.identifier.citation: Rahnemoonfar M, Yari M, Paden J, Koenig L, Ibikunle O (2020). Deep multi-scale learning for automatic tracking of internal layers of ice in radar data. Journal of Glaciology 1–10. https://doi.org/10.1017/ jog.2020.80en_US
dc.identifier.urihttps://doi.org/10.1017/jog.2020.80
dc.identifier.urihttp://hdl.handle.net/11603/20093
dc.language.isoen_USen_US
dc.publisherCambridge University Pressen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Information Systems Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.relation.ispartofUMBC Student 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.
dc.rightsAttribution 4.0 International*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectUMBC Computer Vision and Remote Sensing Laboratoryen_US
dc.titleDeep multi-scale learning for automatic tracking of internal layers of ice in radar dataen_US
dc.typeTexten_US

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