Dust Detection in Satellite Data using Convolutional Neural Networks

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CT2019Team5.pdf (2.7 MB)

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http://hpcf-files.umbc.edu/research/papers/CT2019Team5.pdf

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http://hdl.handle.net/11603/16928

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UMBC Information Systems Department
UMBC Faculty Collection
UMBC Joint Center for Earth Systems Technology (JCET)
UMBC Physics Department
UMBC Student Collection

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Author/Creator ORCID

Date

2019

Type of Work

technical reports
Text

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Program

Citation of Original Publication

Cai, Changjie; Lee, Jangho; Shi, Yingxi Rona; Zerfas, Camille; Guo, Pei; Zhang, Zhibo; Dust Detection in Satellite Data using Convolutional Neural Networks; http://hpcf-files.umbc.edu/research/papers/CT2019Team5.pdf

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Subjects

atmospheric dust
health ailments
dust patterns
satellite data
physical-based algorithms
convolutional neural networks
dust detection
UMBC High Performance Computing Facility (HPCF)

Abstract

Atmospheric dust is known to cause health ailments and impacts earth’s climate and weather patterns. Due to the many issues atmospheric dust contributes to, it is important to study dust patterns and how it enters the atmosphere. In the past, many scientists have used satellite data and physical-based algorithms to detect and track dust, but these algorithms have many shortcomings. Herein, we consider Convolutional Neural Networks to classify dust in satellite images to try to improve the accuracy of dust detection. We describe the satellite data used, discuss the model structures, and provide results for the models built. These models show promising preliminary results.