Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회지)

Korean Society of Surveying, Geodesy, Photogrammetry and Cartography (한국측량학회)
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Automatic Geo-referencing of Sequential Drone Images Using Linear Features and Distinct Points

선형과 특징점을 이용한 연속적인 드론영상의 자동기하보정

  • Choi, Han Seung (GIS Research Center, Geospatial Information Technology Co., Ltd) ;
  • Kim, Eui Myoung (Department of Spatial Information Engineering, Namseoul University)
  • Received : 2018.11.20
  • Accepted : 2018.02.20
  • Published : 2019.02.28
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Abstract

Images captured by drone have the advantage of quickly constructing spatial information in small areas and are applied to fields that require quick decision making. If an image registration technique that can automatically register the drone image on the ortho-image with the ground coordinate system is applied, it can be used for various analyses. In this study, a methodology for geo-referencing of a single image and sequential images using drones was proposed even if they differ in spatio-temporal resolution using linear features and distinct points. Through the method using linear features, projective transformation parameters for the initial geo-referencing between images were determined, and then finally the geo-referencing of the image was performed through the template matching for distinct points that can be extracted from the images. Experimental results showed that the accuracy of the geo-referencing was high in an area where relief displacement of the terrain was not large. On the other hand, there were some errors in the quantitative aspect of the area where the change of the terrain was large. However, it was considered that the results of geo-referencing of the sequential images could be fully utilized for the qualitative analysis.

드론영상은 소규모 지역의 공간정보를 신속하게 구축할 수 있는 장점을 가지고 있어 빠른 의사결정이 필요한 분야에 적용되고 있다. 이러한 드론영상을 지상좌표계가 설정되어 있는 정사영상에 자동으로 영상등록할 수 있는 기하보정 기법이 적용된다면 다양한 분석에 활용될 수 있다. 이에 본 연구에서는 선형정보와 특징점 정보를 이용하여 시 공간해상도에 차이가 있더라도 드론을 이용하여 촬영된 단일 영상 및 연속영상을 기하보정할 수 있는 방법론을 제안하였다. 선형정보를 이용하는 방법을 통해서 영상간의 초기 기하보정을 위한 투영변환 매개변수를 결정한 후 영상에서 다수 추출할 수 있는 특징점에 대한 템플릿 정합을 통해서 최종적으로 영상의 기하보정을 수행하였다. 실험을 통하여 지형의 기복이 많이 있지 않은 지역에서는 기하보정의 정확도가 높게 나타났다. 이에 반해 지형의 변화가 많은 지역에서는 정량적인 측면에서 다소 오차가 크게 나타났으나 정성적인 분석에는 연속영상의 기하보정 결과를 충분히 활용가능한 것으로 판단된다.

Keywords

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Fig. 1. Proposed methodology for geo-referencing

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Fig. 2. Similarity measure using linear feature(Choi and Kim, 2017)

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Fig. 3. Template setting in an input image

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Fig. 4. Search area setting in a reference image

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Fig. 5. Relation between ROP and EOP

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Fig. 6. Relation between ROP and EOP

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Fig. 7. Study area

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Fig. 8. Direct geo-referencing(A)

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Fig. 9. Direct geo-referencing(B)

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Fig. 10. Proposed geo-referencing(A)

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Fig. 11. Proposed geo-referencing(B)

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Fig. 12. Distribution of check points

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Fig. 13. Geo-referencing results in region A

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Fig. 14. Geo-referencing results in region B

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Fig. 15. Direction of error due to relief displacement

Table 1. Planimetric accuracy using checkpoints (unit: pixel)

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References

  1. Choi, H.S. and Kim, E.M. (2017), Image registration of drone images through association analysis of linear features, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography, Vol. 35, No. 6, pp. 441- 452. (in Korean with English abstract) https://doi.org/10.7848/KSGPC.2017.35.6.441
  2. Choi, K.A., Lee, J.H., and Lee, I.P. (2011), Development of a close-range real-time aerial monitoring system based on a low altitude unmanned air vehicle, Journal of Korea spatial information society, Vol. 19, No. 4, pp. 21-31. (in Korean with English abstract)
  3. Han, Y.G. (2013), Automatic image-to-image registration between high-resolution multisensor satellite data in urban areas, Ph.D. dissertation, Seoul National University, Seoul, Korea, 148p.
  4. Huang, S.M., Huang, C.C., and Chou, C.C. (2012), Image registration among UAV image sequence and google satellite image under quality mismatch, 2012 12th International Conference on. ITS Telecommunications, 5-8 November 2012, Taipei, Taiwan, pp. 311-315.
  5. Jung, K.Y. and Yun, H.C. (2012), Utilization of real-time aerial monitoring system for effective damage investigation of natural hazard, Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, Vol. 30, No. 4, pp. 369-377. (in Korean with English abstract) https://doi.org/10.7848/ksgpc.2012.30.4.369
  6. Kim, S.S., Jung, J.H., Kim, E.M., Yoo, H.H., and Sohn, H.G. (2008), Geocoding of low altitude UAV imagery using affine transformation model, Journal of Korean Society for Geospatial Information System, Vol. 16, No. 4, pp. 79-87. (in Korean with English abstract)
  7. Kim, J.N. (2015), Characteristic analysis for 3D spatial data by fixed wing unmaned aerial vehicle, Ph.D. dissertation, Chonbuk National University, Chonbuk, Korea, 125p.
  8. Koch, T., Zhuo, X., Peinartz, P.m., and Fraundorfer, F. (2016), A new paradigm for matching UAV-and aerial images, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences. International Society for Photogrammetry and Remote Sensing, 12-19 July 2016, Prague, Czech, Vol. III-3, pp. 83-90.
  9. Lee, M.R., Park, Y.J., Moon, S.R., Kim, K.J., Song, Y.K., Cho, S.B., Min, M.J., and Seo, B.Y. (2014), Development of Operational Strategy on Scientific Disaster Investigation and its Forensic Technologies, 11-1750140-00013-01, National Disaster Management Institute, Ulsan, Korea, pp. 1-245.
  10. Lin, Y. and Medioni, G. (2007), Map-enhanced UAV image sequence registration and synchronization of multiple image sequences, 2007. CVPR '07. IEEE Conference on, Computer Vision and Pattern Recognition, 17-22 June 2007, Minneapolis, MN, USA, pp. 1-7.
  11. Mazaheri, M. and Habib, A. (2015), Quaternion-based solutions for the single photo resection problem, Photogrammetric Engineering and Remote Sensing, Vol. 81. No. 3, pp. 209-217. https://doi.org/10.14358/PERS.81.3.209-217
  12. Ok, A.O., Wegner, J.D., Heipke, C., Rottensteiner, F., Soergel, U., and Toprak, V. (2012), Matching of straight line segments from aerial stereo images of urban areas, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 74, pp. 133-152. https://doi.org/10.1016/j.isprsjprs.2012.09.003
  13. Rami, A. (2004), Semi-automatic registration of multi-source satellite imagery with varying geometric resolutions, Ph.D. dissertation, University of Calgary, Alberta, Canada, 141p.
  14. Yun, B.Y. and Lee, J.O. (2014), A Study on application of the UAV in Korea for integrated operation with spatial information, Journal of Korean Society for Geospatial Information System, Vol. 22, No. 2, pp. 3-9. (in Korean with English abstract) https://doi.org/10.7319/KOGSIS.2014.22.2.003
  15. Zhongliang, F. and Zhiqun, S. (2008), An algorithm of straight line features matching on aerial imagery, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, International Society for Photogrammetry and Remote Sensing, 3-11 July 2008, Beijing, China, Vol. XXXVII, pp. 97-102.