Detection of Small Roof Details in Image Sequences

  • Dimitri Bulatov
  • Melanie Pohl
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7944)

Abstract

Detecting smaller elevated objects, like chimneys, in high resolution images has several important applications, such as collision warning. On the other hand, the already existing 3D models (that already include the terrain, buildings and vegetation) can be enriched by new instances. There are not many contributions about extracting fine roof details in the literature. Therefore, we developed a new, modularized algorithm for detecting these details as hot spots in the local elevation maps; such a map is typically obtained by a multi-view dense matching method. We use explicit and implicit assumptions on data in order to tighten the search range for chimneys and reduce the number of false alarms. Finally, filtering hot spots by means of color or intensity images takes place. Thus, good detection rates can be achieved for a data set consisting of several high resolution images taken over a residential area.

Keywords

Chimneys depth map hot spot detection multi-view modeling roof details urban terrain 

References

  1. 1.
    Fischer, A., Kolbe, T., Lang, F., Cremers, A., Förstner, W., Plümer, L., Steinhage, V.: Extracting buildings from aerial images using hierarchical aggregation in 2D and 3D. Computer Vision and Image Understanding 72(2), 185–203 (1998)CrossRefGoogle Scholar
  2. 2.
    Rottensteiner, F.: Roof plane segmentation by combining multiple images and point clouds. In: Proc. of Photogrammetric Computer Vision and Image Analysis Conference, Int. Arch. of Photogrammetry and Remote Sensing, vol. 38(pt. 3A), pp. 245–250 (2010)Google Scholar
  3. 3.
    Zebedin, L., Bauer, J., Karner, K., Bischof, H.: Fusion of feature- and area-based information for urban buildings modeling from aerial imagery. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008, Part IV. LNCS, vol. 5305, pp. 873–886. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  4. 4.
    Brenner, C., Dold, C.: Gebäudeextraktion aus luftgestützten und terrestrischen Scandaten. In: Photogrammetrie-Laserscanning-Optische 3D-Messtechnik, Beiträge der Oldenburger 3D-Tage, pp. 312–319. Wichmann Verlag, Heidelberg (2004)Google Scholar
  5. 5.
    Lafarge, F., Descombes, X., Zerubia, J., Pierrot-Deseilligny, M.: Building reconstruction from a single DEM. In: IEEE Conference on Computer Vision and Pattern Recognition, CVPR, pp. 1–8 (2008)Google Scholar
  6. 6.
    Hommel, M.: Detektion und Klassifizierung eingestürzter Gebäude nach Katastrophenereignissen mittels Bildanalyse. PhD thesis, Karlsruhe Intitute of Technologies, KIT (2010)Google Scholar
  7. 7.
    Mosch, M.: 3D-Gebäudeextraktion aus Satellitenbildern suburbaner Regionen. PhD thesis, University of Freiburg (2006)Google Scholar
  8. 8.
    Bulatov, D., Wernerus, P., Heipke, C.: Multi-view dense matching supported by triangular meshes. ISPRS Journal of Photogrammetry and Remote Sensing 66(6), 907–918 (2011)CrossRefGoogle Scholar
  9. 9.
    Gross, H., Thönnessen, U., v. Hansen, W.: 3D-Modeling of urban structures. In: Joint Workshop of ISPRS/DAGM Object Extraction for 3D City Models, Road Databases, and Traffic Monitoring, CMRT 2005, Int. Arch. of Photogrammetry and Remote Sensing, vol. 36(pt. 3W24), pp. 137–142 (2005)Google Scholar
  10. 10.
    Bulatov, D., Solbrig, P., Gross, H., Wernerus, P., Repasi, E., Heipke, C.: Context-based urban-terrain reconstruction from m(uav)-videos for geo-information applications. In: Unmanned Aerial Vehicle in Geomatics Conference (ISSN XXXVIII-1/C22), 1682–1777 (2011)Google Scholar
  11. 11.
    Bulatov, D., Wernerus, P., Gross, H.: On applications of sequential multi-view dense reconstruction from aerial images. ICPRAM 2, 275–280 (2012)Google Scholar
  12. 12.
    Scharstein, D., Szeliski, R.: A Taxonomy and Evaluation of Dense Two-frame Stereo Correspondence Algorithms. International Journal of Computer Vision 47(1), 7–42 (2002)MATHCrossRefGoogle Scholar
  13. 13.
    Hirschmüller, H.: Stereo processing by semi-global matching and mutual information. IEEE Transactions on Pattern Analysis and Machine Intelligence 30(2), 328–341 (2008)CrossRefGoogle Scholar
  14. 14.
    Boykov, Y., Kolmogorov, V.: An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision. IEEE Trans. Pattern Anal. Mach. Intell. 26(9), 1124–1137 (2004)CrossRefGoogle Scholar
  15. 15.
    Delong, A., Osokin, A., Isack, H.N., Boykov, Y.: Fast approximate energy minimization with label costs. International Journal of Computer Vision 96(1), 1–27 (2012)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Fischler, M., Bolles, R.: Random sample consensus: A paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM, 381–395 (1981)Google Scholar
  17. 17.
    Matas, J., Chum, O., Urban, M., Pajdla, T.: Robust wide baseline stereo from maximally stable extremal regions. In: BMVC, pp. 761–767 (2002)Google Scholar
  18. 18.
    Anderer, C., Thönnessen, U., Carlsohn, M.F., Klonz, A.: Ein Bildsegmentierer für die echzeitnahe Verarbeitung. In: DAGM-Symposium, pp. 380–384 (1989)Google Scholar
  19. 19.
    Wassenberg, J., Bulatov, D., Middelmann, W., Sanders, P.: Determination of maximally stable extremal regions in large images. In: SPPRA-Conference (2008)Google Scholar
  20. 20.
    Bulatov, D.: Towards Euclidean reconstruction from video sequences. In: Int. Conf. Computer Vision Theory and Applications, vol. (2), pp. 476–483 (2008)Google Scholar
  21. 21.
    Beder, C., Steffen, R.: Incremental Estimation Without Specifying A-priori Covariance Matrices for the Novel Parameters. In: VLMP Workshop on IEEE Conference on Computer Vision and Pattern Recognition, Anchorage, USA (2008)Google Scholar
  22. 22.
    ISPRS: ISPRS gtest project on 3D building reconstruction (2012), http://www.commission3.isprs.org/wg4/
  23. 23.
    Cramer, M.: The DGPF test on digital aerial camera evaluation - overview and test design. Photogrammetrie – Fernerkundung – Geoinformation 2, 73–82 (2010)CrossRefGoogle Scholar
  24. 24.
    Spreckels, V., Syrek, L., Schlienkamp, A.: DGPF project: evaluation of digital photogrammetric camera systems – stereoplotting. Photogrammetrie – Fernerkundung – Geoinformation 2, 117–130 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Dimitri Bulatov
    • 1
  • Melanie Pohl
    • 1
  1. 1.Fraunhofer Institute of Optronics, System Technologies and Image Exploitation (IOSB)EttlingenGermany

Personalised recommendations