Volume 9 Issue 3
Jun.  2020
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Article Navigation >  Journal of Radars  > 2020  >  9(3): 497-513
GUO Qian, WANG Haipeng, and XU Feng. Research progress on aircraft detection and recognition in SAR imagery[J]. Journal of Radars, 2020, 9(3): 497–513. doi: 10.12000/JR20020
Citation: GUO Qian, WANG Haipeng, and XU Feng. Research progress on aircraft detection and recognition in SAR imagery[J]. Journal of Radars, 2020, 9(3): 497–513. doi: 10.12000/JR20020
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Research Progress on Aircraft Detection and Recognition in SAR Imagery

DOI: 10.12000/JR20020

  • Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China

Funds:  The National Natural Science Foundation of China (61991422)
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  • Corresponding author: WANG Haipeng, hpwang@fudan.edu.cn
  • Received Date: 2020-03-17
  • Rev Recd Date: 2020-05-29
    • Available Online: 2020-06-18
  • Publish Date: 2020-06-01
    Abstract
  • Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.

     

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