Image segmentation for dust detection using unsupervised machine learning CyberTraining 2020: Big Data + High-Performance Computing + Atmospheric Sciences

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dc.contributor.authorBessac, Julie
dc.contributor.authorXu, Ling
dc.contributor.authorYu, Manzhu
dc.contributor.authorGangopadhyay, Aryya
dc.contributor.authorShi, Yingxi
dc.contributor.authorGuo, Pei
dc.date.accessioned2021-04-02T17:58:53Z
dc.date.available2021-04-02T17:58:53Z
dc.date.issued2020
dc.description.abstractDust and sandstorms originating from Earth’s major arid and semi-arid desert areas can significantly affect the climate system and health. Many existing methods use heuristic rules to classify on a pixel-level regarding dust or dust-free. However, these heuristic rules are limited in applicability when the study area or the study period has changed. Based on a multisensor collocation dataset, we sought to utilize unsupervised machine learning techniques to detect and segment dust in multispectral satellite imagery. In this report, we describe the datasets used, discuss our methodology, and provide preliminary validation results.en_US
dc.description.sponsorshipThis work is supported by the grant “CyberTraining: DSE: Cross-Training of Researchers in Computing, Applied Mathematics and Atmospheric Sciences using Advanced Cyberinfrastructure Re- sources” from the National Science Foundation (grant no. OAC–1730250). The hardware used in the computational studies is part of the UMBC High Performance Computing Facility (HPCF). The facility is supported by the U.S. National Science Foundation through the MRI program (grant nos. CNS–0821258, CNS– 1228778, and OAC–1726023) and the SCREMS program (grant no. DMS– 0821311), with additional substantial support from the University of Maryland, Baltimore County (UMBC). See hpcf.umbc.edu for more information on HPCF and the projects using its resources.en_US
dc.description.urihttp://hpcf-files.umbc.edu/research/papers/CT2020Team7.pdfen_US
dc.format.extent22 pagesen_US
dc.genretechnical reportsen_US
dc.identifierdoi:10.13016/m2zw8i-rb6h
dc.identifier.citationBessac, Julie; Xu, Ling; Yu, Manzhu; Gangopadhyay, Aryya; Shi, Yingxi; Guo, Pei; Image segmentation for dust detection using unsupervised machine learning CyberTraining 2020: Big Data + High-Performance Computing + Atmospheric Sciences (2020); http://hpcf-files.umbc.edu/research/papers/CT2020Team7.pdfen_US
dc.identifier.urihttp://hdl.handle.net/11603/21277
dc.language.isoen_USen_US
dc.publisherUMBC HPCFen_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.relation.ispartofseriesHPCF;2020–17
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.rightsPublic Domain Mark 1.0*
dc.rightsThis work was written as part of one of the author's official duties as an Employee of the United States Government and is therefore a work of the United States Government. In accordance with 17 U.S.C. 105, no copyright protection is available for such works under U.S. Law.
dc.rights.urihttp://creativecommons.org/publicdomain/mark/1.0/*
dc.subjectUMBC High Performance Computing Facility (HPCF)en_US
dc.titleImage segmentation for dust detection using unsupervised machine learning CyberTraining 2020: Big Data + High-Performance Computing + Atmospheric Sciencesen_US
dc.typeTexten_US

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