Applying Generative Adversarial Networks to Intelligent Subsurface Imaging and Identification

30 May 2019William Rice

To augment training data for machine learning models in Ground Penetrating Radar (GPR) data classification and identification, this thesis focuses on the generation of realistic GPR data using Generative Adversarial Networks. An innovative GAN architecture is proposed for generating GPR B-scans, which is, to the author's knowledge, the first successful application of GAN to GPR B-scans... (read more)

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