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Improved Image Matching Method Based on Affine Transformation Using Nadir and Oblique-Looking Drone Imagery

  • Received : 2020.09.28
  • Accepted : 2020.10.21
  • Published : 2020.10.31

Abstract

Drone has been widely used for many applications ranging from amateur and leisure to professionals to get fast and accurate 3-D information of the surface of the interest. Most of commercial softwares developed for this purpose are performing automatic matching based on SIFT (Scale Invariant Feature Transform) or SURF (Speeded-Up Robust Features) using nadir-looking stereo image sets. Since, there are some situations where not only nadir and nadir-looking matching, but also nadir and oblique-looking matching is needed, the existing software for the latter case could not get good results. In this study, a matching experiment was performed to utilize images with differences in geometry. Nadir and oblique-looking images were acquired through drone for a total of 2 times. SIFT, SURF, which are feature point-based, and IMAS (Image Matching by Affine Simulation) matching techniques based on affine transformation were applied. The experiment was classified according to the identity of the geometry, and the presence or absence of a building was considered. Images with the same geometry could be matched through three matching techniques. However, for image sets with different geometry, only the IMAS method was successful with and without building areas. It was found that when performing matching for use of images with different geometry, the affine transformation-based matching technique should be applied.

Keywords

References

  1. Ajayi, O.G., Palmer, M., and Salubi, A.A. (2018), Modelling farmland topography for suitable site selection of dam construction using unmanned aerial vehicle (UAV) photogrammetry, Remote Sensing Application: Society and Environment, Vol. 11, pp. 220-230. https://doi.org/10.1016/j.rsase.2018.07.007
  2. Backes, D., Schumann, G., Teferele, F.N., and Boehm, J. (2019), Towards a high-resolution drone-based 3D mapping dataset to optimise flood hazard modelling, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. XI_II-2, pp. 181-187.
  3. Banu, T.P., Borlea, G.F., and Banu C. (2016), The use of drones in forestry, Journal of Environmental Science and Engineering B, Vol. 5, pp. 557-562.
  4. Bay, H., Ess, A., Tuytelaars, T., and Gool, L.V. (2008), Speeded-up robust features (SURF), Computer Vision and Image Understanding, Vol. 110, pp. 346-359. https://doi.org/10.1016/j.cviu.2007.09.014
  5. Colomina, I. and Molina, P. (2014), Unmanned aerial systems for photogrammetry and remote sensing: a review, ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 92, pp. 79-97. https://doi.org/10.1016/j.isprsjprs.2014.02.013
  6. Corcoran, M. (2014), Drone Journalism: Newsgathering Applications of Unmanned Aerial Vehicle (UAVs) in Covering Conflict, Civil Unrest and Disaster, Australian Broadcasting Corporation, https://assets.documentcloud.org/documents/1034066/final-drone-journalism-duringconflict-civil.pdf/ (last date accessed: 20 September 2020).
  7. Daneshmand, M., Helmi, A., Avots, E., Noroozi, F., Alisinanoglu, F., Arslan, H.S., Gorbova, J., Haamer, R.E., Ozcinar, C., and Anbarjafari, G. (2018), 3D scanning: a comprehensive survey, arXiv:1801.08863, pp. 1-18.
  8. Erdelj, M., Natalizio, E., Chowdhury, K.R., and Akyildiz, I.F. (2017), Help from the sky: leveraging UAVs for disaster management, IEEE Pervasive Computer, Vol. 16, pp. 24-32.
  9. Fischler, M.A. and Bolles, R.C. (1981), Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography, Communications of the ACM, Vol. 24, pp. 381-395. https://doi.org/10.1145/358669.358692
  10. Gonzalez, E.F., Vega, F.A., Ramirez, F.C., and Carricondo, P.M. (2020), UAV photogrammetry accuracy assessment for corridor mapping based on the number and distribution of ground control points, Remote Sensing, Vol. 12, pp.2447. https://doi.org/10.3390/rs12152447
  11. Google. Google Satellite Images, Google Earth Pro, http://www.google.com/earth/index.html/ (last date accessed: 14 September 2020).
  12. Holton, A.E., Lawson, S., and Love, C. (2015), Unmanned aerial vehicles opportunities, barriers, and the future of "drone journalism", Journalism Practice, Vol. 9, pp. 634-650. https://doi.org/10.1080/17512786.2014.980596
  13. Javadnejad, F. (2017), Small Unmanned Aircraft Systems (UAS) for Engineering Inspections and Geospatial Mapping, Ph.D. dissertation, Oregon State University, Corvallis, Oregon, United States of America, 168p.
  14. Lowe, D.G. (2004), Distinctive image features from scaleinvariant keypoints, Intermational Journal of Computer Vision, Vol. 60, pp. 91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  15. Meinen, B.U. and Robinson, D.T. (2020), Mapping erosion and deposition in an agricultural landscape: optimization of UAV image acquisition schemes for SfM-MVS, Remote Sensing of Environments, Vol. 239, pp. 1-10.
  16. Nesbit, P.R. and Hugenholtz, C.H. (2019), Enhancing UAVSfM 3D model accuracy in high-relief landscapes by incorporating oblique images, Remote Sensing, Vol. 11, pp. 239. https://doi.org/10.3390/rs11030239
  17. Rodriguez, M., Delon, J., and Morel, J.M. (2018), Fast affine invariant image matching, Image Processing on Line, Vol. 8, pp. 251-281. https://doi.org/10.5201/ipol.2018.225
  18. Rumpler, M., Tscharf, A., Mostegel, C., Daftry, S., Hoppe, C., Prettenthaler, R., Fraundorfer, F., Mayer, G., and Bischof, H. (2017), Evaluations on multi-scale camera networks for precise and geo-accurate reconstructions from aerial and terrestrial images with user guidance, Computer Vision and Image Understanding, Vol. 157, pp. 255-273. https://doi.org/10.1016/j.cviu.2016.04.008
  19. Solomitckii, D., Gapeyenko, M., Semkin, V., Andreev, S., and Koucheryavy, Y. (2018), Technologies for efficient amateur drone detection in 5G millimeter-wave cellular infrastructure, IEEE Communications Magazine, Vol. 56, pp. 43-50.
  20. Suzuki, S. (2018), Recent researches on innovative drone technologies in robotics field, Advanced Robotics, Vol. 32, pp. 1008-1022. https://doi.org/10.1080/01691864.2018.1515660
  21. Yoon, H.K. (2015), Use of drones in the cultural industries, Journal of the Korea Society of Digital Industry and Information Management, Vol. 11, pp. 99-112. (in Korean with English abstract) https://doi.org/10.17662/ksdim.2015.11.4.099

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