DOI QR코드

DOI QR Code

High-Speed Satellite Detection in High-Resolution Image Using Image Processing

영상 처리를 이용한 고해상도 영상 내 위성의 고속 검출

  • Shin, Seunghyeok (Department of Electronics and Computer Engineering, Hanyang University) ;
  • Lee, Jongmin (Ground Based Observation R&D Lab, LIGNex1) ;
  • Lee, Sangwook (Weapon Systems Modeling and Analysis Team, LIGNex1) ;
  • Yang, Taeseok (Seeker & EO/IR R&D Lab, LIGNex1) ;
  • Kim, Whoi-Yul (Department of Electronics and Computer Engineering, Hanyang University)
  • Received : 2017.11.29
  • Accepted : 2018.03.19
  • Published : 2018.05.01

Abstract

Many countries are trying to deploy satellite surveillance systems for their national defense, and one of these system uses optical systems to observe the satellites above their territories. The optical satellite surveillance system requires the coordinates of the satellites in an acquired image and expects that those coordinates to be delivered to the tracking system. The proposed method detects the satellite sources in a high-resolution image with fast image processing for the optical surveillance system. To achieve faster detection, the proposed method reduces the size of the original image and approximates the trajectory of a satellite, so image processing methods are only applied to the nearby area of the approximated trajectory in the original image. The proposed method shows the similar detection performance faster than the previous method.

국토 방위를 위해 국가들은 자국 상공을 지나가는 위성 감시 시스템을 구축하고 있으며 이 시스템들 중 하나는 광학계를 이용한 위성 감시 시스템이다. 광학계를 이용할 경우, 획득한 영상 내에 존재하는 위성 광원을 제한된 시간 내에 검출하여 그 위치를 추적 시스템에 전달하여야 한다. 제안하는 방법은 광학계를 이용한 위성 감시 시스템을 이용해 획득한 고해상도 상공 촬영 영상을 고속으로 영상 처리하여 위성 광원을 검출한다. 이를 위해 고해상도 영상 처리에 앞서 영상을 축소하여 저해상도 영상을 생성하여 위성의 궤적을 추정하고 고해상도 원본 영상에서는 궤적 근방 영역에서만 영상 처리 방법들이 적용되도록 하였다. 제안하는 방법은 기존에 위성 검출을 위해 사용되는 방법과 유사한 위성 검출 정확도를 보이면서 검출을 더 빠르게 수행하였다.

Keywords

References

  1. Ciurte, A., and Danescu, R., "Automatic Detection of MEO Satellite Streaks from Single Long Exposure Astronomic Images," 2014 International Conference on Computer Vision Theory and Applications, January 2014, pp. 538-544.
  2. Helin, E.F., and Shoemaker, E.M.., "The Palomar Planet-crossing Asteroid Survey, 1973-1978," Icarus, Vol. 40, No. 4, December 1979, pp. 321-328. https://doi.org/10.1016/0019-1035(79)90021-6
  3. Bertin, E., and Arnouts, S., "SExtractor: Software for Source Extraction," Astronomy and Astrophysics Supplement Series, Vol. 117, No. 2, June 1996, pp. 393-404. https://doi.org/10.1051/aas:1996164
  4. Lasker, B.M., Sturch, C.R., McLean, B.J., Russell, J.L., Jenkner, H., and Shara, M.M., "The Guide Star Catalog. I - Astronomical Foundations and Image Processing," Astronomical Journal, Vol. 99, June 1990, pp. 2019-2058, 2173-2178. https://doi.org/10.1086/115483
  5. Groth, E.J., "A pattern-matching algorithm for two-dimensional coordinate lists," Astronomical Journal, Vol. 91, May 1986, pp. 1244-1248. https://doi.org/10.1086/114099
  6. Shapiro, L., and Stockman, G., "Computer Vision," 1st Ed., Prentice Hall, New Jersey, 2001, pp. 69-74.
  7. Fischler, M.A., and Bolles, R.C., "Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography," Communications of the ACM, Vol. 24, No. 6, June 1981, pp. 381-395. https://doi.org/10.1145/358669.358692
  8. Calabretta, M.R., and Greisen, E.W., "Representations of celestial coordinates in FITS," Astronomy and Astrophysics, Vol. 395, December 2002, pp. 1077-1122. https://doi.org/10.1051/0004-6361:20021327