Committee Chair

Wu, Dalei

Committee Member

Liang, Yu; Yang, Li

Department

Dept. of Computer Science and Engineering

College

College of Engineering and Computer Science

Publisher

University of Tennessee at Chattanooga

Place of Publication

Chattanooga (Tenn.)

Abstract

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 ar- chitecture is proposed for generating GPR B-scans, which is, to the author’s knowledge, the first successful application of GAN to GPR B-scans. As one of the major contri- butions, a novel loss function is formulated by merging frequency domain with time domain features. To test the efficacy of generated B-scans, a real time object classifier is proposed to measure the performance gain derived from augmented B-Scan images. The numerical experiment illustrated that, based on the augmented training data, the proposed GAN architecture demonstrated a significant increase (from 82% to 98%) in the accuracy of the object classifier.

Degree

M. S.; A thesis submitted to the faculty of the University of Tennessee at Chattanooga in partial fulfillment of the requirements of the degree of Master of Science.

Date

5-2019

Subject

Ground penetrating radar; Neural networks (Computer science)

Keyword

Ground penetrating radar (GPR); Object detection; Generative adversarial networks (GAN); Short Term Fourier Transform (STFT); Neural Network (NN); Deep Learning;

Document Type

Masters theses

DCMI Type

Text

Extent

xiv, 51 leaves

Language

English

Rights

https://rightsstatements.org/page/InC/1.0/?language=en

License

http://creativecommons.org/licenses/by-nc-nd/3.0/

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