Exploring SAR ATR with neural networks: going beyond accuracy

Ryan Melzer; William M. Severa; Craig M. Vineyard

doi:10.1117/12.2618659

31 May 2022 Exploring SAR ATR with neural networks: going beyond accuracy

Ryan Melzer, William M. Severa, Craig M. Vineyard

Proceedings Volume 12096, Automatic Target Recognition XXXII; 120960D (2022) https://doi.org/10.1117/12.2618659
Event: SPIE Defense + Commercial Sensing, 2022, Orlando, Florida, United States

Abstract

Deep neural networks have recently demonstrated state-of-the-art accuracy on public Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) benchmark datasets. While attaining competitive accuracy on benchmark datasets is a necessary feature, it is important to characterize other facets of new SAR ATR algorithms. We extend this recent work by demonstrating not only improved state-of-the-art accuracy, but that contemporary deep neural networks can achieve several algorithmic traits beyond competitive accuracy which are necessitated by operational deployment scenarios. First, we employ several saliency map algorithms to provide explainability and insight into understanding black-box classifier decisions. Second, we collect and implement numerous data augmentation routines and training improvements both from the computer vision literature and specific to SAR ATR data in order to further improve model domain adaptation performance from synthetic to measured data, achieving a 99.26% accuracy on SAMPLE validation with a simple network architecture. Finally, we survey model reproducibility and performance variability under domain adaptation from synthetic to measured data, demonstrating potential consequences of training on only synthetic data.

Conference Presentation

Citation Download Citation

Ryan Melzer, William M. Severa, and Craig M. Vineyard "Exploring SAR ATR with neural networks: going beyond accuracy", Proc. SPIE 12096, Automatic Target Recognition XXXII, 120960D (31 May 2022); https://doi.org/10.1117/12.2618659

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