Building Change Detection from Uniform Regions

  • Charles Beumier
  • Mahamadou Idrissa
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7441)


This paper deals with building change detection by supervised classification of image regions into ’built’ and ’non-built’ areas. Regions are the connected components of low gradient values in a multi-spectral aerial image. Classes are learnt from spectral (colour, vegetation index) and elevation cues relatively to building polygons and non building areas as defined in the existing database. Possible candidate building regions are then filtered by geometrical features. Inconsistencies in the database with the recent image are automatically detected. Tests in cooperation with the Belgian National Geographical Institute on an area with sufficient buildings and landscape variety have shown that the system allows for the effective verification of unchanged buildings, and detection of destructions and new candidate buildings.


Building verification building detection spectral cues geometrical cues Digital Surface Model 


  1. 1.
    Busch, A., Gerke, M., Grünreich, D., Heipke, C., Liedtke, C.-E., Müller, S.: Automated Verification of a Topographic Reference Dataset: System Design and Practical Results. In: Int. Archives of Photogrammetry and Remote Sensing IAPRS, Istanbul, Turkey, vol. XXXV, B2, pp. 735–740 (2004)Google Scholar
  2. 2.
    Champion, N., Stamon, G., Pierrot-Deseilligny, M.: Automatic Revision of 2D Building Databases from High Resolution Satellite Imagery: A 3D Photogrammetric Approach. In: AGILE, Hannover Germany (2009)Google Scholar
  3. 3.
    Niederöst, M.: Detection and Reconstruction of Buildings for Automated Map updating, These Institut für Geodäsie und Photogrammetrie, ETH Zürich (2003)Google Scholar
  4. 4.
    Olsen, B.: Automatic Change Detection for Validation of Digital Map Databases. In: ISPRS XX, Commission II, Istambul, vol. XXXV, pp. 569–574 (2004)Google Scholar
  5. 5.
    Heipke, C., Mooney, K.: EuroSDR – A research organisation serving Europe’s geospatial information needs. In: Fritsch, D. (hrsg.) Photogrammetric Week 2009, pp. 321–330. Wichmann, Heidelberg (2009)Google Scholar
  6. 6.
    Baltsavias, E.: Object Extraction and Revision by Image Analysis using Existing Geodata and Knowledge: Current Status and Steps towards Operational Systems. ISPRS Journal of Photogrammetry and Remote Sensing 58, 129–151 (2004)CrossRefGoogle Scholar
  7. 7.
    Matikainen, I., Hyyppa, J., Ahokas, E., Markelin, L., Kartinen, H.: Automatic Detection of Buildings and Changes in Buildings for Updating of Maps. Remote Sensing 2, 1217–1248 (2010)CrossRefGoogle Scholar
  8. 8.
    Rottensteiner, F.: Building Change Detection from Digital Surface Models and Multi-spectral Images. In: Photogrammetric Image Analysis, Munich, Germany, pp. 145–150 (2007)Google Scholar
  9. 9.
    Idrissa, M., Lacroix, V.: A Multiresolution-MRF Approach for Stereo Dense Disparity Estimation. In: IEEE-GRSS/ISPRS Joint Urban Remote Sensing Event, Shanghai, China (2009)Google Scholar
  10. 10.
    Beumier, C., Idrissa, M.: Building Change Detection by Histogram Classification. In: Int. Conf. on Signal-Image Technology and Internet-Based Systems, Dijon, France (2011)Google Scholar
  11. 11.
    Beumier, C.: Building verification from geometrical and photometric cues. In: Applic. of Digital Image Processing XXX, San Diego, California. Proc. of SPIE, vol. 6696 (2007)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Charles Beumier
    • 1
  • Mahamadou Idrissa
    • 1
  1. 1.Signal & Image CentreRoyal Military AcademyBrusselsBelgium

Personalised recommendations