Programming and Computer Software

, Volume 45, Issue 6, pp 311–318 | Cite as

Detection and 3D Reconstruction of Buildings from Aerial Images

  • L. V. NovotortsevEmail author
  • A. G. VoloboyEmail author


In this paper, we consider the problem of detection and 3D reconstruction of buildings from aerial images of the scene and usage of orientation data (camera parameters, GPS, etc.). Presently-available methods for solving this problem are inefficient on large amounts of data, where most of the data do not need thorough processing. In this work, we employ methods that find and analyze line segments on the image. This approach allows us to use image preprocessing based on line segment analysis, thereby reducing the amount of data that require computationally complex operations.



  1. 1.
    Peng, D. and Zhang, Y., Building change detection by combining lidar data and ortho image, Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci., 2016, vol. 41, p. 669.CrossRefGoogle Scholar
  2. 2.
    Malihi, S., et al., 3D building reconstruction using dense photogrammetric point cloud, Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci., 2016, vol. 3, pp. 71–74.CrossRefGoogle Scholar
  3. 3.
    Shal'nov, E.V., Gringauz, A.D., and Konushin, A.S., Estimation of the people position in the world coordinate system for video surveillance, Program. Comput. Software, 2016, vol. 42, no. 6, pp. 361–366.CrossRefGoogle Scholar
  4. 4.
    Rottensteiner, F. and Briese, C., A new method for building extraction in urban areas from high-resolution LIDAR data, Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci., 2002, vol. 34, no. 3/A, pp. 295–301.Google Scholar
  5. 5.
    Sohn, G. and Dowman, I., Data fusion of high-resolution satellite imagery and LIDAR data for automatic building extraction, ISPRS J. Photogramm. Remote Sens., 2007, vol. 62, no. 1, pp. 43–63.CrossRefGoogle Scholar
  6. 6.
    Sidike, P., et al., Automatic building change detection through adaptive local textural features and sequential background removal, Proc. IEEE Int. Geoscience and Remote Sensing Symp. (IGARSS), 2016, pp. 2857–2860.Google Scholar
  7. 7.
    Chaudhuri, D., et al., Automatic building detection from high-resolution satellite images based on morphology and internal gray variance, IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens., 2016, vol. 9, no. 5, pp. 1767–1779.CrossRefGoogle Scholar
  8. 8.
    Lu, Y.H., Trinder, J.C., and Kubik, K., Automatic building detection using the Dempster–Shafer algorithm, Photogramm. Eng. Remote Sens., 2006, vol. 72, no. 4, pp. 395–403.CrossRefGoogle Scholar
  9. 9.
    Brunn, A. and Weidner, U., Extracting buildings from digital surface models, Int. Arch. Photogramm. Remote Sens., 1997, vol. 32, no. 3, pp. 27–34.Google Scholar
  10. 10.
    Girard, S., et al., Building detection from high-resolution color images, Remote Sens. Int. Soc. Opt. Photonics, 1998, pp. 278–289.Google Scholar
  11. 11.
    Berthod, M., et al., High-resolution stereo for the detection of buildings, Automatic Extraction of Man-Made Objects from Aerial and Space Images, Basel: Birkhäuser, 1995, pp. 135–144.Google Scholar
  12. 12.
    Ghaffarian, S. and Ghaffarian, S., Automatic building detection based on supervised classification using high resolution Google Earth images, Int. Arch. Photogramm., Remote Sens. Spat. Inf. Sci., 2014, vol. 40, no. 3, pp. 101–106.CrossRefGoogle Scholar
  13. 13.
    Ok, A.O., Automated extraction of buildings and roads in a graph partitioning framework, ISPRS Ann. Photogramm., Remote Sens. Spat. Inf. Sci., 2013, pp. 79–84.CrossRefGoogle Scholar
  14. 14.
    Singhal, S. and Radhika, S., Automatic detection of buildings from aerial images using color invariant features and canny edge detection, Int. J. Eng. Trends Technol.(IJETT), 2014, vol. 11, no. 8, pp. 393–396.CrossRefGoogle Scholar
  15. 15.
    Kharinov, M.V., Pixel clustering for color image segmentation, Program. Comput. Software, 2015, vol. 41, no. 5, pp. 258–266.MathSciNetCrossRefGoogle Scholar
  16. 16.
    Baillard, C. and Zisserman, A., A plane-sweep strategy for the 3D reconstruction of buildings from multiple images, Int. Arch. Photogramm. Remote Sens., 2000, vol. 33, no. B2, pp. 56–62.Google Scholar
  17. 17.
    Wang, Y.C., et al., 3D reconstruction of piecewise planar models from multiple views utilizing coplanar and region constraints, J. Inf. Sci. Eng., 2013, vol. 29, no. 2, pp. 361–378.Google Scholar
  18. 18.
    Novotortsev, L.V. and Voloboi, A.G., Improved plane-sweep method for the reconstruction of buildings from aerial images, Preprint of Keldysh Inst. Appl. Math., 2018, no. 207.Google Scholar
  19. 19.
    Bashkirova, D.R., Ioshidzava, Sh., Latypov, R.Kh., and Iokota, Kh., Fast L1 Gaussian transform for image smoothing with preservation of boundaries, Tr. Inst. Sistemnogo Program. Ross. Akad. Nauk (Proc. Inst. Syst. Program. Russ. Acad. Sci.), 2017, vol. 29, no. 4, pp. 55–72.Google Scholar
  20. 20.
    Hough, P.V.C., Method and means for recognizing complex patterns, US Patent 3069654, 1962.Google Scholar
  21. 21.
    von Gioi, R.G., et al., LSD: A fast line segment detector with a false detection control, IEEE Trans. Pattern Anal. Mach. Intell., 2010, vol. 32, no. 4, pp. 722–732.CrossRefGoogle Scholar
  22. 22.
    Akinlar, C. and Topal, C., EDLines: A real-time line segment detector with a false detection control, Pattern Recognit. Lett., 2011, vol. 32, no. 13, pp. 1633–1642.CrossRefGoogle Scholar
  23. 23.
    Novotortsev, L.V. and Voloboi, A.G., Detection and matching of regions that contain buildings on aerial images, Trudy XXVI Mezhdunar. Konf. Komp’yuternoi Grafike i Mashinnomu Zreniyu “Grafikon” (Proc. 26th Int. Conf. Computer Graphics and Machine Vision “Grafikon”), Nizhny Novgorod, 2016, pp. 404–408.Google Scholar
  24. 24.
    Wang, X. and Fu, W., Optimized SIFT image matching algorithm, Proc. IEEE Int. Conf. Automation and Logistics, 2008, pp. 843–847.Google Scholar
  25. 25.
    Rublee, E., et al., ORB: An efficient alternative to SIFT or SURF, Proc. IEEE Int. Conf. Computer Vision (ICCV), 2011, pp. 2564–2571.Google Scholar
  26. 26.
    Dahlke, D., Linkiewicz, M., and Meissner, H., True 3D building reconstruction: Facade, roof and overhang modeling from oblique and vertical aerial imagery, Int. J. Image Data Fusion, 2015, vol. 6, no. 4, pp. 314–329.CrossRefGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2019

Authors and Affiliations

  1. 1.JSC RacursMoscowRussia
  2. 2.Keldysh Institute of Applied Mathematics, Russian Academy of SciencesMoscowRussia

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