Dust Detection in Satellite Data using Convolutional Neural Networks
Links to Fileshttp://hpcf-files.umbc.edu/research/papers/CT2019Team5.pdf
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Type of Work13 pages
Citation of Original PublicationCai, 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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convolutional neural networks
UMBC High Performance Computing Facility (HPCF)
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.