Abstract
The CycleGAN generative adversarial network is applied to simulated electo-optical (EO) images in order to transition them into a Synthetic Aperture Radar (SAR)-like domain. If possible this would allow the user to simulate radar images without computing the phase history of the scene. Though visual inspection leaves the output images appearing SAR-like, examination by t-distributed Stochastic Neighbor Embedding (t-SNE) shows that CycleGAN was insufficient at generalizing an EO-to-SAR conversion. Further, using the transitioned images as training data for a neural network shows that SAR features used for classification are not present in the simulated images.
Library of Congress Subject Headings
Synthetic aperture radar--Computer simulation; Machine learning; Neural networks (Computer science); Electrooptics--Data processing
Publication Date
5-8-2020
Document Type
Thesis
Student Type
Graduate
Degree Name
Imaging Science (MS)
Department, Program, or Center
Chester F. Carlson Center for Imaging Science (COS)
Advisor
Michael Gartley
Advisor/Committee Member
Charles Bachmann
Advisor/Committee Member
Michael Jay Schillaci
Recommended Citation
Slover, Jason, "Synthetic Aperture Radar simulation by Electro Optical to SAR Transformation using Generative Adversarial Network" (2020). Thesis. Rochester Institute of Technology. Accessed from
https://repository.rit.edu/theses/10566
Campus
RIT – Main Campus
Plan Codes
IMGS-MS
