Image segmentation for dust detection using unsupervised machine learning CyberTraining 2020: Big Data + High-Performance Computing + Atmospheric Sciences
Links to Fileshttp://hpcf-files.umbc.edu/research/papers/CT2020Team7.pdf
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Type of Work22 pages
Citation of Original PublicationBessac, 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.pdf
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This 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.
Dust 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.
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