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Interpolating RGB radar images based on machine learning

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dc.contributor.authorYi, Chenke, author
dc.contributor.authorChandrasekar, V., advisor
dc.contributor.authorChen, Haonan, advisor
dc.contributor.authorSiller, Thomas, committee member
dc.contributor.authorGooch, Steven, committee member
dc.date.accessioned2023-06-01T17:27:30Z
dc.date.available2025-05-26T17:27:30Z
dc.date.issued2023
dc.description.abstractWeather radar interpolation is the process of estimating and predicting rainfall data in areas that are not directly observed by radar. This technique is commonly used in weather forecasting, flood prediction, and agricultural planning. The main goal of weather radar interpolation is to produce accurate and reliable precipitation maps in areas with limited radar coverage or where the radar data is incomplete. The interpolation methods can be categorized into two main groups: deterministic and stochastic. Deterministic methods use mathematical equations and physical models to estimate the rainfall, while stochastic methods rely on statistical algorithms to analyze the correlations between the radar measurements and ground observations. In recent years, machine learning algorithms have also been applied to weather radar interpolation, showing promising results in accuracy and robustness. In this paper, we mainly propose a radar image interpolation method based on spatio-temporal convolutional networks. The experiments are mainly compared and analyzed for different combinations of networks, connection methods, and different loss functions.
dc.format.mediumborn digital
dc.format.mediummasters theses
dc.identifierYi_colostate_0053N_17751.pdf
dc.identifier.urihttps://hdl.handle.net/10217/236633
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.rights.accessEmbargo Expires: 05/26/2025
dc.subjectweather radar
dc.subjectmachine learning
dc.titleInterpolating RGB radar images based on machine learning
dc.typeText
dcterms.embargo.expires2025-05-26
dcterms.embargo.terms2025-05-26
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineElectrical and Computer Engineering
thesis.degree.grantorColorado State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science (M.S.)

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