Dr. Justin Zhan Profile

Dr. Justin Zhan

at Univ. of Cincinnati

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Proceedings Article | 13 June 2023 Paper

Bridging the synthetic to measured SAR gap by splitting style and content

Caleb Parks, Matthew Swan, Susan Gauch, Justin Zhan

Proceedings Volume 12520, 125200O (2023) https://doi.org/10.1117/12.2667543

KEYWORDS: Synthetic aperture radar, Image classification, Machine learning, Data modeling, Convolution, Adversarial training

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Correctly classifying SAR imagery is a critical task in many applications; however, unseen samples may be difficult for machine learning models to classify. Challenges include sensitivity to slight changes in target alignment and backgrounds. To overcome this, learning algorithms require large amounts of data taken under a variety of conditions. Since collection of measured data is time-consuming, costly, and sometimes impossible, researchers utilize synthetic data to diversify existing datasets. Unfortunately, arbitrary classification algorithms do not accurately handle the domain shift from synthetic to measured images. To eliminate this shift, the community has used GAN-based image-to-image approaches to transform the synthetic images to appear measured. Our experimentation indicates some baseline approaches fail when measured data is scarce, failing to preserve labels and/or capture the full measured distribution. To alleviate this, we design a novel discriminator that separates content from style. Using this discriminator in a GAN, we force the generation procedure to preserve content while altering style. By preserving content, we avoid label-flipping and mode-seeking caused by biases in available measured data. Through extensive experiments, we show how our method is able to predict the measured distribution for out-of-sample images.

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