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Target Recognition in SAR Images by Deep Learning with Training Data Augmentation.

Zhe Geng1, Ying Xu1, Bei-Ning Wang1

  • 1Key Laboratory of Radar Imaging and Microwave Photonics, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China.

Sensors (Basel, Switzerland)
|January 21, 2023
PubMed
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High-quality synthetic Synthetic Aperture Radar (SAR) imagery enhances deep learning for automatic target recognition (ATR). New data augmentation techniques improve classification accuracy and robustness against unknown targets.

Area of Science:

  • Computer Science, Artificial Intelligence, Machine Learning
  • Electrical Engineering, Signal Processing
  • Remote Sensing, Geospatial Analysis

Background:

  • Deep learning (DL) based SAR automatic target recognition (ATR) requires extensive high-quality training data for open-world performance.
  • Existing datasets like MSTAR and SAMPLE provide valuable SAR imagery but require augmentation for diverse real-world scenarios.
  • Robustness against out-of-distribution (OOD) samples is crucial for reliable ATR systems.

Purpose of the Study:

  • To develop advanced data augmentation strategies for synthetic SAR imagery.
  • To enhance the performance and robustness of DL-based SAR-ATR algorithms.
  • To lay the groundwork for large-scale, open-field deployment of SAR-ATR systems.

Main Methods:

Keywords:
automatic target recognitiondeep learningsparse representationsynthetic aperture radartraining data augmentation

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  • Exploited target scattering center sparsity for novel pose synthesis.
  • Implemented clutter transfer for diverse background augmentation.
  • Introduced a novel contrast-based data augmentation technique for the SAMPLE dataset.
  • Utilized adversarial outlier exposure with real SAR data (MiniSAR) to improve OOD detection.

    • Main Results:

    • Proposed data augmentation methods significantly improved target classification accuracy.
    • The techniques enhanced the robustness of DL models against OOD samples.
    • Simulation results validated the effectiveness of the augmentation strategies.

    Conclusions:

    • The developed data augmentation techniques are effective for improving DL-based SAR-ATR.
    • The study provides a foundation for practical, large-scale SAR-ATR system implementation.
    • Findings hold significant theoretical and potential military application value.