Journal of Korean Society for Geospatial Information Science (대한공간정보학회지)

Korea Spatial Information Society (대한공간정보학회)
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Image Quality Assessment of Mobile-based Image Acquisition System for Disaster Scientific Investigation

재난원인과학조사를 위한 차량기반 영상취득시스템의 영상품질평가

  • Kim, Mi Kyeong (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Sang Pil (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Kim, Nam Hoon (Department of Civil and Environmental Engineering, Yonsei University) ;
  • Song, Young Karb (Disaster Scientific Investigation Division, National Disaster Management Institute) ;
  • Sohn, Hong Gyoo (Department of Civil and Environmental Engineering, Yonsei University)
  • 김미경 (연세대학교 토목환경공학과) ;
  • 김상필 (연세대학교 토목환경공학과) ;
  • 김남훈 (연세대학교 토목환경공학과) ;
  • 송영갑 (국립재난안전연구원 재난원인조사실) ;
  • 손홍규 (연세대학교 토목환경공학과)
  • Received : 2016.07.25
  • Accepted : 2016.08.30
  • Published : 2016.09.30
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Abstract

There are various types of disasters now, and accordingly it is practically difficult to manage all types of disasters effectively. If we are able to reconstruct the disaster event and investigate the cause, it may be possible to prepare the recurrence of similar patterns of disasters. The vehicle-based system equipped with state-of-the-art sensors has been proposed in order to reconstruct the disaster site as much as possible and help the disaster investigator to analyze the cause of the disaster by providing high-quality information. However, the data quality obtained from the sensors can be lowered due to unpredictable circumstances of disaster site. In this aspect it is essential to provide practical procedures that assess and analyze the performance of the equipment on site. In this paper, we selected critical elements of performance that can evaluate the vehicle-based image acquisition system, since it is the most critical piece of information in the disaster sites. The quality of the images obtained from vehicle-based image system was analyzed and verified on the test site. From the results of spatial resolution based on GRD(Ground Resolved Distance), we were able to identify maximum 5mm of spatial resolution at a distance of 70m distance. The result of field test is expected to be used for data acquisition plan in future disaster situations.

재난은 다양한 형태로 발생하고 있으며, 모든 유형의 재난을 효과적으로 대비하는 것은 현실적으로 어렵다. 그러나 기 발생재난을 재구성하고 그 원인을 면밀히 분석한다면 유사 재난의 재발 방지가 가능할 것이다. 이에 재난현장을 재구성하고 재난 발생의 원인을 과학적으로 규명하기 위해 고품질 데이터 취득 장비를 탑재한 차량기반 시스템을 구축하고 있다. 해당 시스템이 취득하는 데이터는 재난 현장에서 예측하지 못한 변수로 인해 그 품질이 저하될 수 있으므로, 이론적인 시스템의 성능 이외에도 실제 취득된 데이터의 품질을 평가하는 과정이 필요하다. 이에 본 논문에서는 재난 현장의 정밀한 고품질 데이터를 취득하기 위해 구축된 차량 기반 영상취득시스템의 성능을 평가할 수 있는 요소를 선정하고, 데이터를 취득하여 영상의 품질을 검증 및 분석하였다. GRD(Ground Resolved Distance)에 근거한 공간해상력을 중심으로 영상취득시스템의 성능을 평가한 결과, 70m에서 최대 약 5mm의 공간해상력을 갖는 것을 확인하였으며 이러한 성능 평가의 결과는 향후 재난 현장 자료 취득 계획 시 사용될 수 있을 것으로 기대한다.

Keywords

References

  1. Ariyasu, E., Koizumi, M., Ikubo, M. and Hatake, S., 2012, Application of mobile LIDAR mapping for damage survey after Great East Japan Earthquake, Proc. of ISPRS 2012, International Society of Photogrammetry, Remote Sensing and Spatial Information Sciences, Melbourne, Australia, Vol. 39, pp. 573-576.
  2. Campbell, J. B., 2002, Introduction to remote sensing, third edition. Guilford Press, New York, N.Y.
  3. Che, G. S., Chang, W. S. and Oh, J., 2012, A study on the MTF of optical system with optical path difference, The Journal of Korea Navigation Institute, Vol. 16, No. 3, pp. 518-525.
  4. Cho, Y. M., 2002, Characteristics of the image quality parameter in satellite imaging instrument, Aerospace Engineering and Technology, Vol 1, No. 2, pp. 66-74.
  5. Earl, F. G., 1998, USAF 1951 and microcopy resolution test charts and pixel profiles, efg's computer lab, http://www.efg2.com/Lab/ImageProcessing/TestTargets/
  6. Estribeau, M. and Magnan, P., 2004, Fast MTF measurement of CMOS imagers using ISO 12333 slanted-edge methodology, Proc. of SPIE, Detectors and Associated Signal Processing, St. Etienne, France, Vol. 5251, pp. 243-252.
  7. Gong, J. and Maher, A., 2014, Use of mobile lidar data to assess hurricane damage and visualize community vulnerability, Transportation Research Record: Journal of the Transportation Research Board, Vol. 2459, pp. 119-126. https://doi.org/10.3141/2459-14
  8. Gopal, A. and Samant, S. S., 2003, Bar-pattern technique for modulation transfer function measurement in portal imaging, Proc. of SPIE, Medical Imaging, Vol. 5030, pp. 433-444.
  9. Gusella, L., Adams, B. J. and Bitelli, G., 2007, Use of mobile mapping technology for post-disaster damage information collection and integration with remote sensing imagery, Proc. of 5th International Symposium on Mobile Mapping Technology, International Society of Photogrammetry, Remote Sensing and Spatial Information Sciences, Padua, Italy, Vol. 34. pp. 1-8.
  10. Hellmuth, M. E., Mason, S., Vaughan, C., Aalst, M. and Choularton, R., 2011, A better climate for disaster risk management, International Research Institute for Climate and Society, Columbia University: New York, USA.
  11. Honkavaara, E., Jaakkola, J., Markelin, L. and Becker, S., 2006, Evaluation of resolving power and MTF of DMC, Proc. of ISPRS, International Society of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 36, No. 6, pp. 17-22.
  12. Hwang, H., Park, W. and Kwak, S., 2011, MTF assessment and image restoration technique for post-launch calibration of DubaiSat-1, Korean Journal of Remote Sensing, Vol. 27, No. 5, pp. 573-586. https://doi.org/10.7780/kjrs.2011.27.5.573
  13. Jacobson, R. E., Ray, S. F., Attridge, G. G. and Axford, N. R., 2000, The manual of photography, Oxford: Focal Press, UK.
  14. Kim, J. I., Jeong, J. and Kim, T., 2010, Development of GRD measurement method using natural target in imagery, Korean Journal of Remote Sensing, Vol. 26, No. 5, pp. 527-536. https://doi.org/10.7780/kjrs.2010.26.5.527
  15. Leachtenauer, J. C. and Driggers, R. G., 2001, Surveillance and reconnaissance imaging systems: modeling and performance prediction, Artech House, Boston, USA.
  16. Lee, J. S., Ko, S. J., Kang, S. S., Kim, J. H., Kim, D. H. and Kim, C., 2013a, Quantitative evaluation of image quality using automatic exposure control and sensitivity in the digital chest image, Journal of the Korea Contents Association, Vol. 13, No. 8, pp. 275-283.
  17. Lee, M. R., Koo, S. and No, S. J., 2013b, Establishment and operation systems of the DSI (Disaster: Scientific Investigation) Center, National Disaster Management Institute.
  18. Lee, M., Park, Y., Moon, S., Kim, K., Song, Y., Cho, S., Koo, S., Choi, M. and Seo, B., 2014, Development of operational strategy on scientific disaster investigation and its forensic technologies, National Disaster Management Institute.
  19. Lee, T. Y., Lee, J. O. and Yun, B. Y., 2012, Verification of spatial resolution in DMC imagery using bar target, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 30, No. 5, pp. 485-492. https://doi.org/10.7848/ksgpc.2012.30.5.485
  20. Nuutinen, M., Orenius, O., Saamanen, T. and Oittinen, P., 2012, A framework for measuring sharpness in natural images captured by digital cameras based on reference image and local areas, EURASIP Journal on Image and Video Processing, Vol. 2012, No. 1, pp. 1-15. https://doi.org/10.1186/1687-5281-2012-1
  21. ODI and CDKN, 2014, The future framework for disaster risk reduction: a guide for decision-makers, second edition, Overseas Development Institute and Climate and Development Knowledge Network: London, UK.
  22. Patra, S. K., Mishra, N., Chandrakanth, R. and Ramachandran, R., 2002, Image quality improvement through MTF compensation-a treatment to high resolution data, Indian Cartographer, Vol. 22, No. 15, pp. 88-93.
  23. Shin J. M, Im, J. H., and Lee, S. R., 2003, Satellite image quality analysis using MTF modeling, Proc. of the Korean Society for Aeronautical and Space Sciences 2003, Korean Society For Aeronautical And Space Sciences, pp. 254-258.
  24. Statistics Korea, 2016, Status of accidents, Statistics Korea, http://www.index.go.kr/potal/main/EachDtlPageDetail.do?idx_cd=1627
  25. Thomson, G. H., 2009, A note on spatial resolution measurement and its implications for image radiometry, International Journal of Remote Sensing, Vol. 30, No. 1, pp. 1-8. https://doi.org/10.1080/01431160802339480
  26. UNISDR, 2016a, A background paper on: strengthening the role of science and technology for disaster risk reduction in the Arab Region, UN office for disaster risk reduction, http://www.unisdr.org/files/45951_sciencetechnologyfordrr.pdf
  27. UNISDR, 2016b, Sendai framework for disaster risk reduction 2015-2030, UN office for disaster risk reduction, http://www.unisdr.org/files/43291_sendaiframeworkfordrren.pdf
  28. United Nations, 2016, Global sustainable development report 2016, United Nations, https://sustainabledevelopment.un.org/content/documents/2328GSDR%202016.pdf
  29. Walker, J. D., 2014, Fundamentals of physics / Halliday & Resnick, extended edition, Wiley, New York, USA.
  30. Woo, J. E., Lee, Y. G., Bae, S. H. and Kim, Y. G., 2012, An evaluation method of X-ray imaging system resolution for non-engineers, Korean Society of Radiological Science, Vol. 35, No. 4, pp. 309-314.