Graph pretraining approach to utilize synthetic data for SAR ATR

Caleb Parks; Susan Gauch; Matthew Scherreik; Ryan Socha

doi:10.1117/12.3025891

7 June 2024 Graph pretraining approach to utilize synthetic data for SAR ATR

Caleb Parks, Susan Gauch, Matthew Scherreik, Ryan Socha

Proceedings Volume 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI; 130320X (2024) https://doi.org/10.1117/12.3025891
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Because of limitations in availability of synthetic aperture radar (SAR) training data, automatic target recognition (ATR) researchers have turned to the use of synthetic SAR images. Unfortunately, training neural network classifiers on this synthetic data does not yield robust models. Assuming access to limited measured SAR data, we evaluate two natural, transfer-learning approaches to solve this problem, showing that both do not successfully lead to solutions. Motivated by the successes of contrastive, representation, and metric learning, we propose a novel graph-based pretraining approach to transfer knowledge from synthetic samples to real-world scenarios. We show that this approach is applicable to three different neural network architectures obtaining improvements over the baseline approach of 19.21%, 28.70%, and 8.27% respectively. We also demonstrate that our method is robust to the choice of hyperparameters.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Caleb Parks, Susan Gauch, Matthew Scherreik, and Ryan Socha "Graph pretraining approach to utilize synthetic data for SAR ATR", Proc. SPIE 13032, Algorithms for Synthetic Aperture Radar Imagery XXXI, 130320X (7 June 2024); https://doi.org/10.1117/12.3025891

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KEYWORDS

Synthetic aperture radar

Feature extraction

Neural networks

Machine learning

Automatic target recognition

Image classification

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