Skip to main content

Advertisement

Log in

Building Extraction from Stereo Aerial Imagery Using Dynamic Programming

  • Research Article-Civil Engineering
  • Published:
Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

In this paper, a novel technique for building detection and extraction and simple building reconstruction from stereo aerial imagery is presented. This research hypothesises that geometric distortion in buildings will lead to occlusion at depth discontinuities. Depth discontinuities around buildings can be identified by determining the occlusion. Accordingly, the roof can be distinguished from the ground. Occlusion usually occurs in the direction that is perpendicular or at the edge angled to the baseline, and no occlusion occurs when the building edge is parallel to the baseline. Therefore, another stereo pair should be used to detect the depth discontinuities around the object. Dynamic programming algorithm is implemented to detect occluded pixels at the depth discontinuities. Accordingly, the occluded pixels can be identified in the form of a point cloud. The produced point cloud is scattered and cannot be used to identify or extract the building boundary. Therefore, the point cloud is converted to a raster image for detecting buildings that are shown as blobs. The algorithm is later used to extract the building shape and construction. The proposed technique is fully automated and does not require human interference. The algorithm is applied to two study areas. Two stereo pairs are used for the first study area, and only one stereo pair is available and applied for the second study area. Analyses show that the correctness and completeness of object accuracy assessment for the first study area are 100% and 97%, respectively; those for the second study area are 83% and 75%.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Biljecki, F.; et al.: Applications of 3D city models: state of the art review. ISPRS Int J Geo-Inf 4(4), 2842–2889 (2015)

    Article  Google Scholar 

  2. Haala, N.; Kada, M.: An update on automatic 3D building reconstruction. ISPRS J Photogramm Remote Sens 65(6), 570–580 (2010)

    Article  Google Scholar 

  3. Ahmadi, S.; et al.: 2010 Automatic urban building boundary extraction from high resolution aerial images using an innovative model of active contours. Int J Appl Earth Observation Geoinf 12(3), 150–157 (2010)

    Article  Google Scholar 

  4. Dornaika, F.; et al.: Building detection from orthophotos using a machine learning approach: an empirical study on image segmentation and descriptors. Expert Syst. Appl. 58, 130–142 (2016)

    Article  Google Scholar 

  5. Zhu, Q.; et al.: MAP-Net: multi attending path neural network for building footprint extraction from remote sensed imagery. arXiv preprint arXiv:1910.12060 (2019).

  6. Sebastian, C.; et al.: Contextual pyramid attention network for building segmentation in aerial imagery. arXiv preprint arXiv:2004.07018 (2020).

  7. Sebastian, C.; et al.: Adversarial loss for semantic segmentation of aerial imagery. arXiv preprint arXiv:2001.04269 (2020).

  8. Wang, S.; Hou, X.; Zhao, X.: Automatic building extraction from high-resolution aerial imagery via fully convolutional encoder-decoder network with non-local block. IEEE Access 8, 7313–7322 (2020)

    Article  Google Scholar 

  9. Zhang, X.; et al.: An improved architecture for urban building extraction based on depthwise separable convolution. J Intell Fuzzy Syst (2020). https://doi.org/10.3233/JIFS-179669

    Article  Google Scholar 

  10. Shi, Y.; Li, Q.; Zhu, X.X.: Building segmentation through a gated graph convolutional neural network with deep structured feature embedding. ISPRS J Photogramm Remote Sens 159, 84–197 (2020)

    Article  Google Scholar 

  11. Braun, J.: Aspects on true-orthophoto production. In: Proceedings of 49th Photogrammetric Week (2003).

  12. Tarantino, E.; Figorito, B.: Extracting buildings from true color stereo aerial images using a decision making strategy. Remote Sens 3(12), 553–1567 (2011)

    Google Scholar 

  13. Xiao, J.; Gerke, M.; Vosselman, G.: Building extraction from oblique airborne imagery based on robust façade detection. ISPRS J Photogramm Remote Sens 68, 56–68 (2012)

    Article  Google Scholar 

  14. Wu, B.; et al.: Building reconstruction from high-resolution multiview aerial imagery. IEEE Geosci. Remote Sens. Lett. 12(4), 855–859 (2014)

    Google Scholar 

  15. Wang, Y.: Automatic extraction of building outline from high resolution aerial imagery. ISPRS Int Arch Photogramm Remote Sens Spatial Inf Sci 41, 419–423 (2016)

    Article  Google Scholar 

  16. Tian, J.; Krauß, T.; d’Angelo, P.: Automatic rooftop extraction in stereo imagery using distance and building shape regularized level set evolution. ISPRS-Int Arch Photogramm Remote Sens Spatial Inf Sci 42(W1), 393–397 (2017)

    Article  Google Scholar 

  17. Bellman, R.: Dynamic Programming. Princeton University Press, Princeton, USA (1957)

    MATH  Google Scholar 

  18. Luus, R.: Iterative Dynamic Programming. Chapman and Hall/CRC press, London (2000)

    MATH  Google Scholar 

  19. Sakoe, H.; Chiba, S.: Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49 (1978)

    Article  Google Scholar 

  20. Senin, P.: Dynamic time warping algorithm review. Information and Computer Science Department University of Hawaii at Manoa Honolulu, USA. vol. 855, no. (1–23), p. 40 (2008)

  21. Birchfield, S.; Tomasi, C.: Depth discontinuities by pixel-to-pixel stereo. Int J Comput Vis 35(3), 269–293 (1999)

    Article  Google Scholar 

  22. Belhumeur, P.N.: A binocular stereo algorithm for reconstructing sloping, creased, and broken surfaces in the presence of half-occlusion. In: 1993 (4th) International Conference on Computer Vision. IEEE. (1993)

  23. Intille, S.S.; Bobick A.F.: Disparity-space images and large occlusion stereo. In: European Conference on Computer Vision. 1994. Springer, Berlin.

  24. Cox, I.J.; et al.: A maximum likelihood stereo algorithm. Comput Vis Image Understanding 63(3), 542–567 (1996)

    Article  Google Scholar 

  25. Quenot, G.M.: Image matching using dynamic programming: application to stereovision and image interpolation in Image Communication 1996. Citeseer.

  26. Rojas, A.; Calvo, A.; Muñoz, J.: A dense disparity map of stereo images. Pattern Recogn. Lett. 18(4), 385–393 (1997)

    Article  Google Scholar 

  27. Salehian, B.; Fotouhi, A.M.; Raie, A.A.: Dynamic programming-based dense stereo matching improvement using an efficient search space reduction technique. Optik 160, 1–12 (2018)

    Article  Google Scholar 

  28. Ishikawa, H. and D. Geiger. Occlusions, discontinuities, and epipolar lines in stereo. In: European Conference on Computer Vision. 1998. Springer, Berlin.

  29. Bobick, A.F.; Intille, S.S.: Large occlusion stereo. Int J Comput Vis 33(3), 181–200 (1999)

    Article  Google Scholar 

  30. Gimel’farb, G.: Stereo terrain reconstruction by dynamic programming. Handbook of Computer Vision and Applications 2, 505–530 (1999)

    Google Scholar 

  31. Torr, P.H.; Criminisi, A.: Dense stereo using pivoted dynamic programming. Image Vis. Comput. 22(10), 795–806 (2004)

    Article  Google Scholar 

  32. Krauß, T.; et al.: DEM generation from very high resolution stereo satellite data in urban areas using dynamic programming. Int Arch Photogramm Remote Sens Spatial Inf Sci, 2005. 36.

  33. Alobeid, A.; Jacobsen, K.; Heipke C.: Building height estimation in urban areas from very high resolution satellite stereo images. In: ISPRS Hannover Workshop (2009).

  34. Cai, J.; Walker, R.: Height estimation from monocular image sequences using dynamic programming with explicit occlusions. IET Computer Vis 4(3), 149 (2010)

    Article  MathSciNet  Google Scholar 

  35. Jacobsen, K.; Alobeid, A.: Digital surface models in urban areas based on satellite imagery. In: EARSeL Workshop. (2010)

  36. Borisagar, V.H.; Zaveri, M.A.: Disparity map generation from illumination variant stereo images using efficient hierarchical dynamic programming. Sci. World J. 2014, 513417 (2014)

    Article  Google Scholar 

  37. Gruen, A.: Development and status of image matching in photogrammetry. Photogramm Record 27(137), 36–57 (2012)

    Article  Google Scholar 

  38. Chun-Jen, T.; Katsaggelos, A.K.: Dense disparity estimation with a divide-and-conquer disparity space image technique. IEEE Trans. Multimedia 1(1), 18–29 (1999)

    Article  Google Scholar 

  39. Ohta, Y.; Kanade, T.: Stereo by intra-and inter-scanline search using dynamic programming. IEEE Trans. Pattern Anal. Mach. Intell. 2, 139–154 (1985)

    Article  Google Scholar 

  40. Taylor, J.; et al.: Method for investigating intradriver heterogeneity using vehicle trajectory data: a dynamic time warping approach. Transp Res Part B Methodol 73, 59–80 (2015)

    Article  Google Scholar 

  41. Cho, W.; Schenk, T.; Madani, M.: Resampling digital imagery to epipolar geometry. Int Arch Photogramm Remote Sens 29, 404 (1993)

    Google Scholar 

  42. Rasmussen, J.; et al.: Are vegetation indices derived from consumer-grade cameras mounted on UAVs sufficiently reliable for assessing experimental plots? Euro J Agron 74, 75 (2016)

    Article  Google Scholar 

  43. Aptoula, E.; Lefèvre, S.: Morphological texture description of grey-scale and color images. In: Adv Imag Electron Phys, vol. 169, pp. 1–74. Elsevier (2011).

  44. Li, Z.; Zhu, C.; Gold, C.: Digital Terrain Modeling: Principles and Methodology. CRC Press, London (2005)

    Google Scholar 

  45. Rottensteiner, F.; et al.: Results of the ISPRS benchmark on urban object detection and 3D building reconstruction, in Commission III-Photogrammetric Computer Vision and Image Analysis, Working Group III/4-3D Scene Analysis. 2013. p. 1-17.

  46. Orteu, J.-J.: 3-D computer vision in experimental mechanics. Optics Lasers Eng 47(3–4), 282–291 (2009)

    Article  Google Scholar 

  47. Lin, G.-Y.; Chen, X.; Zhang, W. G.: A robust epipolar rectification method of stereo pairs. In: 2010 International Conference on Measuring Technology and Mechatronics Automation. Vol. 1, pp. 322–326.

  48. Loop, C.; Zhang, Z.: Computing rectifying homographies for stereo vision. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149). (1999). IEEE.

  49. Mikhail, E.; Bethel, J.; McGlone, J.: Introduction to Modern Photogrammetry. Wiley, New York (2001)

    Google Scholar 

  50. Sadeq, H.A.: Merging Digital Surface Models Sourced from Multi-Satellite Imagery and their Consequent Application in Automating 3D Building Modelling. University of Glasgow, Glasgow-UK (2015)

    Google Scholar 

Download references

Acknowledgement

The author would like to give special thanks to [45] for providing the necessary datasets for testing the algorithm and evaluation.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to H. A. Sadeq.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sadeq, H.A. Building Extraction from Stereo Aerial Imagery Using Dynamic Programming. Arab J Sci Eng 46, 5089–5103 (2021). https://doi.org/10.1007/s13369-020-05224-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13369-020-05224-9

Keywords

Navigation