Improving SAR ATR using synthetic data via transfer learning

Brian O. Raeker; Tyler A. Hill; Chris Kreucher; Katherine M. Banas; Kevin Tactac; Kyle Simpson; Kirk Weeks

doi:10.1117/12.2663615

13 June 2023 Improving SAR ATR using synthetic data via transfer learning

Brian O. Raeker, Tyler A. Hill, Chris Kreucher, Katherine M. Banas, Kevin Tactac, Kyle Simpson, Kirk Weeks

Proceedings Volume 12520, Algorithms for Synthetic Aperture Radar Imagery XXX; 125200L (2023) https://doi.org/10.1117/12.2663615
Event: SPIE Defense + Commercial Sensing, 2023, Orlando, Florida, United States

Abstract

Attempts to use synthetic data to augment measured data for improved synthetic aperture radar (SAR) automatic target recognition (ATR) performance have been hampered by domain mismatch between datasets. Past work which leveraged synthetic data in a transfer learning framework has been successful but was primarily focused on transferring generic SAR features. Recently SAMPLE, a paired synthetic and measured dataset was introduced to the SAR community, enabling demonstration of good ATR performance using 100% synthetic data. In this work, we examine how to leverage synthetic data and measured data to boost ATR using transfer learning. The synthetic dataset corresponds to the MSTAR 15o dataset. We demonstrate that high quality synthetic data can enhance ATR performance even when substantial measured data is available, and that synthetic data can reduce measured data requirements by over 50% while maintaining classification accuracy.

Conference Presentation

Citation Download Citation

Brian O. Raeker, Tyler A. Hill, Chris Kreucher, Katherine M. Banas, Kevin Tactac, Kyle Simpson, and Kirk Weeks "Improving SAR ATR using synthetic data via transfer learning", Proc. SPIE 12520, Algorithms for Synthetic Aperture Radar Imagery XXX, 125200L (13 June 2023); https://doi.org/10.1117/12.2663615

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