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Machine Learning Methods for SAR Interference Mitigation

In: Synthetic Aperture Radar (SAR) Data Applications

Author

Listed:
  • Yan Huang

    (Southeast University)

  • Lei Zhang

    (Sun Yat-san University)

  • Jie Li

    (Nanjing University of Aeronautics and Astronautics)

  • Mingliang Tao

    (Northwestern Polytechnical University)

  • Zhanye Chen

    (Chongqing University)

  • Wei Hong

    (Southeast University)

Abstract

Interference mitigation problem is a major issue in active remote sensing especially via a wideband synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This chapter provides a comprehensive study of the interference mitigation techniques applicable for an SAR system. Typical signal models for various interference types are provided, together with many illustrative examples from real SAR data. In addition, advanced signal processing techniques, specifically machine learning methods, for suppressing interferences are analyzed in detail. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends is provided from the perspective of cognitive and deep learning frameworks.

Suggested Citation

  • Yan Huang & Lei Zhang & Jie Li & Mingliang Tao & Zhanye Chen & Wei Hong, 2022. "Machine Learning Methods for SAR Interference Mitigation," Springer Optimization and Its Applications, in: Maciej Rysz & Arsenios Tsokas & Kathleen M. Dipple & Kaitlin L. Fair & Panos M. Pardalos (ed.), Synthetic Aperture Radar (SAR) Data Applications, pages 113-146, Springer.
    • Handle: RePEc:spr:spochp:978-3-031-21225-3_6
    DOI: 10.1007/978-3-031-21225-3_6
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