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31 May 2022 Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images
Matthew Swan, Anne Major, Jacob Lear, Caleb G. Parks, Justin Zhan
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Proceedings Volume 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX; 120950B (2022) https://doi.org/10.1117/12.2624110
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States
Abstract
Due to their ability to capture images in a variety of environmental conditions, Synthetic Aperture Radars (SAR) are of particular interest in the automatic target recognition (ATR) domain. In order to develop SARATR machine learning (ML) algorithms, a large sample set indicative of the underlying population must be used. This is an issue since gathering SAR images, even for a single target, is an expensive and time consuming process. Recently a data set, known as the SAMPLE data set, consisting of synthetic SAR samples has been released. Ideally theses synthetic images can be used in place of real SAR samples. Unfortunately, training SAR-ATR ML algorithms with samples exclusively from the SAMPLE data set produces algorithms with poor performance on real SAR images. This paper is focused on creating new variants of cycle-consistent generative adversarial networks (CycleGAN) to produce a transformation function that maps a synthetic SAR image to a useful approximation of a real SAR image. By introducing a new feature correlation module to the cycle consistent GAN architecture we take the first steps in closing the gap between synthetic SAR images and measured SAR images.
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Matthew Swan, Anne Major, Jacob Lear, Caleb G. Parks, and Justin Zhan "Enforcing feature correlation on cycle-consistent GAN generated functions: a first step in closing the synthetic measured gap found in SAR images", Proc. SPIE 12095, Algorithms for Synthetic Aperture Radar Imagery XXIX, 120950B (31 May 2022); https://doi.org/10.1117/12.2624110
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KEYWORDS
Synthetic aperture radar
Algorithm development
Automatic target recognition
Machine learning
Neural networks
Image processing
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