Publication Date
2024
Document Type
Thesis
Committee Members
Josh Ash, Ph.D. (Committee Co-Chair); Brian Rigling, Ph.D. (Committee Co-Chair); Fred Garber, Ph.D. (Committee Member)
Degree Name
Master of Science in Electrical Engineering (MSEE)
Abstract
To address the issues of limited target data in the Synthetic Aperture Radar Automatic Target Recognition (SAR ATR) problem set, synthetic data is often used to aid in filling the gap. This paper covers an in depth look at the use of colorization, dynamic range adjustment, and target extraction as data augmentation techniques to improve the accuracy of deep learning networks trained on synthetic SAR data. The use of multiple different data augmentations combine to dramatically improve the accuracy of a common Convolutional Neural Network (CNN) over the use of standard synthetic data. A comparison of increasing fraction of measured data were used to show that the less measured data there is available the more critical these data augmentation techniques are to improve target recognition.
Page Count
76
Department or Program
Department of Electrical Engineering
Year Degree Awarded
2024
Copyright
Copyright 2024, all rights reserved. My ETD will be available under the "Fair Use" terms of copyright law.
ORCID ID
0009-0000-6866-2682
