Journal of the Korea Institute of Military Science and Technology (한국군사과학기술학회지)

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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SAR Recognition of Target Variants Using Channel Attention Network without Dimensionality Reduction

차원축소 없는 채널집중 네트워크를 이용한 SAR 변형표적 식별

  • Park, Ji-Hoon (Defense AI Technology Center, Agency for Defense Development) ;
  • Choi, Yeo-Reum (Defense AI Technology Center, Agency for Defense Development) ;
  • Chae, Dae-Young (Defense AI Technology Center, Agency for Defense Development) ;
  • Lim, Ho (Defense AI Technology Center, Agency for Defense Development)
  • 박지훈 (국방과학연구소 국방인공지능기술센터) ;
  • 최여름 (국방과학연구소 국방인공지능기술센터) ;
  • 채대영 (국방과학연구소 국방인공지능기술센터) ;
  • 임호 (국방과학연구소 국방인공지능기술센터)
  • Received : 2021.09.29
  • Accepted : 2022.05.06
  • Published : 2022.06.05
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Abstract

In implementing a robust automatic target recognition(ATR) system with synthetic aperture radar(SAR) imagery, one of the most important issues is accurate classification of target variants, which are the same targets with different serial numbers, configurations and versions, etc. In this paper, a deep learning network with channel attention modules is proposed to cope with the recognition problem for target variants based on the previous research findings that the channel attention mechanism selectively emphasizes the useful features for target recognition. Different from other existing attention methods, this paper employs the channel attention modules without dimensionality reduction along the channel direction from which direct correspondence between feature map channels can be preserved and the features valuable for recognizing SAR target variants can be effectively derived. Experiments with the public benchmark dataset demonstrate that the proposed scheme is superior to the network with other existing channel attention modules.

Keywords

References

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