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Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data

Publication ,  Journal Article
Inkawhich, N; Inkawhich, MJ; Davis, EK; Majumder, UK; Tripp, E; Capraro, C; Chen, Y
Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
January 1, 2021

Obtaining measured synthetic aperture radar (SAR) data for training automatic target recognition (ATR) models can be too expensive (in terms of time and money) and complex of a process in many situations. In response, researchers have developed methods for creating synthetic SAR data for targets using electro-magnetic prediction software, which is then used to enrich an existing measured training dataset. However, this approach relies on the availability of some amount of measured data. In this work, we focus on the case of having 100% synthetic training data, while testing on only measured data. We use the SAMPLE dataset public released by AFRL, and find significant challenges to learning generalizable representations from the synthetic data due to distributional differences between the two modalities and extremely limited training sample quantities. Using deep learning-based ATR models, we propose data augmentation, model construction, loss function choices, and ensembling techniques to enhance the representation learned from the synthetic data, and ultimately achieved over 95% accuracy on the SAMPLE dataset. We then analyze the functionality of our ATR models using saliency and feature-space investigations and find them to learn a more cohesive representation of the measured and synthetic data. Finally, we evaluate the out-of-library detection performance of our synthetic-only models and find that they are nearly 10% more effective than baseline methods at identifying measured test samples that do not belong to the training class set. Overall, our techniques and their compositions significantly enhance the feasibility of using ATR models trained exclusively on synthetic data.

Duke Scholars

Published In

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI

EISSN

2151-1535

ISSN

1939-1404

Publication Date

January 1, 2021

Volume

14

Start / End Page

2942 / 2955

Related Subject Headings

  • 4601 Applied computing
  • 4013 Geomatic engineering
  • 3709 Physical geography and environmental geoscience
  • 0909 Geomatic Engineering
  • 0801 Artificial Intelligence and Image Processing
  • 0406 Physical Geography and Environmental Geoscience
 

Citation

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MLA
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Inkawhich, N., Inkawhich, M. J., Davis, E. K., Majumder, U. K., Tripp, E., Capraro, C., & Chen, Y. (2021). Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 14, 2942–2955. https://doi.org/10.1109/JSTARS.2021.3059991
Inkawhich, N., M. J. Inkawhich, E. K. Davis, U. K. Majumder, E. Tripp, C. Capraro, and Y. Chen. “Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 14 (January 1, 2021): 2942–55. https://doi.org/10.1109/JSTARS.2021.3059991.
Inkawhich N, Inkawhich MJ, Davis EK, Majumder UK, Tripp E, Capraro C, et al. Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021 Jan 1;14:2942–55.
Inkawhich, N., et al. “Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data.” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 14, Jan. 2021, pp. 2942–55. Scopus, doi:10.1109/JSTARS.2021.3059991.
Inkawhich N, Inkawhich MJ, Davis EK, Majumder UK, Tripp E, Capraro C, Chen Y. Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 2021 Jan 1;14:2942–2955.

Published In

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI

EISSN

2151-1535

ISSN

1939-1404

Publication Date

January 1, 2021

Volume

14

Start / End Page

2942 / 2955

Related Subject Headings

  • 4601 Applied computing
  • 4013 Geomatic engineering
  • 3709 Physical geography and environmental geoscience
  • 0909 Geomatic Engineering
  • 0801 Artificial Intelligence and Image Processing
  • 0406 Physical Geography and Environmental Geoscience