Imaging Science MS Thesis Defense - Jason Slover
Synthetic Aperture Radar simulation by Electro Optical to SAR Transformation using Generative Adversarial Network
Jason Slover
Imaging Science MS Candidate
Chester F. Carlson Center for Imaging Science, RIT
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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.
Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.
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