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Robust SAR Automatic Target Recognition Based on Transferred MS-CNN with L2-Regularization.

Yikui Zhai1, Wenbo Deng1, Ying Xu1

  • 1Department of Intelligent Manufacturing, Wuyi University, Jiangmen 529020, China.

Computational Intelligence and Neuroscience
|January 10, 2020
PubMed
Summary

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This summary is machine-generated.

This study introduces an efficient transferred Max-Slice Convolutional Neural Network (MS-CNN) with L2-Regularization for Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR). The method enhances feature extraction and recognition performance, especially with limited data.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Synthetic Aperture Radar (SAR) Automatic Target Recognition (ATR) faces challenges with limited data and model robustness.
  • Deep learning, particularly Convolutional Neural Networks (CNNs), shows promise but requires further development for SAR ATR.

Purpose of the Study:

  • To propose an efficient transferred Max-Slice CNN (MS-CNN) with L2-Regularization for improved SAR ATR.
  • To address issues of insufficient samples and model robustness in SAR target recognition.

Main Methods:

  • Data amplification to enrich SAR target features and reduce computation.
  • MS-CNN framework with L2-Regularization for robust feature extraction and overfitting prevention.
  • Transfer learning to enhance feature representation and discrimination on small datasets.

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  • Evaluation of various activation functions and dropout strategies.

    • Main Results:

    • The proposed MS-CNN with L2-Regularization significantly improves SAR ATR performance.
    • The method demonstrates superior performance compared to state-of-the-art techniques on public and extended MSTAR datasets.
    • Effective recognition is achieved even on random small datasets.

    Conclusions:

    • The developed MS-CNN with L2-Regularization offers a robust and efficient solution for SAR ATR.
    • Transfer learning and data amplification are crucial for enhancing performance with limited SAR data.
    • The proposed model provides a significant advancement in the field of SAR target recognition.