Simple Synthetic Data as Source Domain for Transfer Learning to Remote Sensing as a Target Domain

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dc.contributor.advisorZüfle, Andreas
dc.creatorShaw, Brian L
dc.date2022-08-19
dc.date.accessioned2023-06-14T12:03:57Z
dc.date.available2023-06-14T12:03:57Z
dc.description.abstractDeep Learning continues to grow as a prevalent toolset among multiple disciplines, including Remote Sensing and image analysis. Correspondingly, to more easily apply the deep neural networks to different subject matter domains, Transfer Learning, from natural image datasets, including ImageNet, has become a de-facto method for many Deep Learning applications, including Remote Sensing. However, such an approach may have limitations related to the differences on the characteristics of natural photographic image datasets and the characteristics of Remote Sensing. This study aims to determine if a fairly arbitrary, easily produced set of synthetic datasets can be iteratively developed and used for Transfer Learning for a typical Deep Learning task. We found this is readily and surprisingly feasible.
dc.format.mediummasters theses
dc.identifier.urihttps://hdl.handle.net/1920/13313
dc.language.isoen
dc.rightsCopyright 2022 Brian L. Shaw
dc.rights.urihttps://rightsstatements.org/vocab/InC/1.0
dc.subject.keywordsTransfer learning
dc.subject.keywordsRemote sensing
dc.subject.keywordsSynthetic data
dc.subject.keywordsDeep neural network
dc.subject.keywordsExplainable artificial intelligence
dc.subject.keywordsConvolutional neural network
dc.titleSimple Synthetic Data as Source Domain for Transfer Learning to Remote Sensing as a Target Domain
thesis.degree.disciplineGeoinformatics and Geospatial Intelligence
thesis.degree.grantorGeorge Mason University
thesis.degree.levelMaster's
thesis.degree.nameMaster of Science in Geoinformatics and Geospatial Intelligence

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