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

The Korea Institute of Military Science and Technology (한국군사과학기술학회)
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A Dataset of Ground Vehicle Targets from Satellite SAR Images and Its Application to Detection and Instance Segmentation

위성 SAR 영상의 지상차량 표적 데이터 셋 및 탐지와 객체분할로의 적용

  • 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) ;
  • Yoo, Ji Hee (Defense AI Technology Center, Agency for Defense Development)
  • 박지훈 (국방과학연구소 국방인공지능기술센터) ;
  • 최여름 (국방과학연구소 국방인공지능기술센터) ;
  • 채대영 (국방과학연구소 국방인공지능기술센터) ;
  • 임호 (국방과학연구소 국방인공지능기술센터) ;
  • 유지희 (국방과학연구소 국방인공지능기술센터)
  • Received : 2021.08.04
  • Accepted : 2021.12.13
  • Published : 2022.02.05
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Abstract

The advent of deep learning-based algorithms has facilitated researches on target detection from synthetic aperture radar(SAR) imagery. While most of them concentrate on detection tasks for ships with open SAR ship datasets and for aircraft from SAR scenes of airports, there is relatively scarce researches on the detection of SAR ground vehicle targets where several adverse factors such as high false alarm rates, low signal-to-clutter ratios, and multiple targets in close proximity are predicted to degrade the performances. In this paper, a dataset of ground vehicle targets acquired from TerraSAR-X(TSX) satellite SAR images is presented. Then, both detection and instance segmentation are simultaneously carried out on this dataset based on the deep learning-based Mask R-CNN. Finally, this paper shows the future research directions to further improve the performances of detecting the SAR ground vehicle targets.

Keywords

References

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