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

  • 제21권3호
  • /
  • Pages.315-322
  • /
  • 2018
  • /
  • 1598-9127(pISSN)
  • /
  • 2636-0640(eISSN)
한국군사과학기술학회 (The Korea Institute of Military Science and Technology)
권호 표지
DOI QR코드

DOI QR Code

프레임동영상의 근실시간 센서모델 보정시스템 개발 및 성능분석

Development and Performance Analysis of a Near Real-Time Sensor Model Correction System for Frame Motion Imagery

  • 권혁태 ((주)픽소니어) ;
  • 고진우 (국방과학연구소 제3기술연구본부) ;
  • 김상희 (국방과학연구소 제3기술연구본부) ;
  • 박세형 ((주)픽소니어)
  • Kwon, Hyuk Tae (Pixoneer Geomatics, Inc.) ;
  • Koh, Jin-Woo (The 3rd Research and Development Institute, Agency for Defense Development) ;
  • Kim, Sanghee (The 3rd Research and Development Institute, Agency for Defense Development) ;
  • Park, Se Hyoung (Pixoneer Geomatics, Inc.)
  • 투고 : 2017.10.20
  • 심사 : 2018.04.13
  • 발행 : 2018.06.05
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

Due to the increasing demand for more rapid, precise and accurate geolocation of the targets on video frames from UAVs, an efficient and timely method for correcting sensor models of motion imagery is required. In this paper, we propose a method to adjust or correct sensor models of motion imagery frames using space resection via image matching with reference data. The proposed method adopts image matching between the motion imagery frames and the reference frames which are synthesized from reference data. Ground or reference control points are generated or selected through the matching process in near real time, and are used for space resection to get adjusted sensor models. Finally, more precise and accurate geolocation of the targets can possibly be done on the fly, and we have got the promising result on performance analysis in terms of the geolocation quality.

키워드

참고문헌

  1. C. R. Taylor, R. J. Settergren, "Full-Motion Video Georegistration for Accuracy Improvement, Accuracy Assessment, and Robustness," Proc. SPIE 8386, Full Motion Video(FMV) Workflows and Technologies for Intelligence, Surveillance, and Reconnaissance (ISR) and Situational Awareness, May 2012.
  2. J.-M. Morel, G. Yu, “ASIFT: A New Framework for Fully Affine Invariant Image Comparison,” SIAM Journal on Image Sciences, Vol. 2, No. 2, pp. 438-469, 2009. https://doi.org/10.1137/080732730
  3. J.-W. Koh, H. T. Kwon, S. Kim, "Performance Analysis of the Sensor Model Correction for Frame Motion Imagery," KIMST Annual Conference Proceedings, pp. 469-470, 2017.
  4. H. Bay, A. Ess, T. Tuytelaars, L. Van Gool, “SURF: Speeded Up Robust Features,” Computer Vision and Image Understanding, Vol. 110, No. 3, pp. 346-359, 2008. https://doi.org/10.1016/j.cviu.2007.09.014
  5. D. Nister, "Preemptive RANSAC for Live Structure and Motion Estimation," Machine Vision and Applications, Vol. 16, Issue 5, pp. 321-329, 2005. https://doi.org/10.1007/s00138-005-0006-y
  6. C. Forster, M. Pizzoli, D. Scaramuzza, "SVO: Fast Semi-Direct Monocular Visual Odometry," IEEE International Conference on Robotics and Automation (ICRA), 2014.
  7. Motion Imagery Standards Board(MISB), "MISP- 2015.1 Motion Imagery Standards Profile," October 2014.
  8. X. Xu, et. al., "A Two-Phase Space Resection Model for Accurate Topographic Reconstruction from Luna Imagery with Pushbroom Scanners," Sensors, Vol. 16, Issue 4, 507, 2016. https://doi.org/10.3390/s16040507