Change Detection in Urban Areas by Direct Comparison of Multi-view and Multi-temporal ALS Data

  • Marcus Hebel
  • Michael Arens
  • Uwe Stilla
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6952)

Abstract

Change detection in urban areas requires the comparison of multi-temporal remote sensing data. ALS (airborne laser scanning) is one of the established techniques to deliver these data. A novelty of our approach is the consideration of multiple views that are acquired with an oblique forward-looking laser scanner. In addition to advantages in terms of data coverage, this configuration is ideally suited to support helicopter pilots during their mission, e.g., with an obstacle warning system, terrain-referenced navigation, or online change detection. In this paper, we present a framework for direct comparison of current ALS data to given reference data of an urban area. Our approach extends the concept of occupancy grids known from robot mapping, and the proposed change detection method is based on the Dempster-Shafer theory. Results are shown for an urban test site at which multi-view ALS data were acquired at an interval of one year.

Keywords

Airborne laser scanning LiDAR change detection multi-temporal data analysis urban areas 

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References

  1. [2009]
    Champion, N., Rottensteiner, F., Matikainen, L., Liang, X., Hyyppä, J., Olsen, B.P.: A Test of Automatic Building Change Detection Approaches. In: CMRT 2009, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Paris, vol. 38(3/W4), pp. 145–150 (2009)Google Scholar
  2. [2007]
    Hebel, M., Stilla, U.: Automatic Registration of Laser Point Clouds of Urban Areas. In: PIA 2007, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, München, vol. 36 (3/W49A), pp. 13–18 (2007)Google Scholar
  3. [2008]
    Hebel, M., Stilla, U.: Pre-classification of Points and Segmentation of Urban Objects by Scan Line Analysis of Airborne LiDAR Data. In: Proceedings of Commission III, XXI ISPRS Congress 2008. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Beijing, vol. 37(Part B3a), pp. 105–110 (2008)Google Scholar
  4. [2010]
    Hebel, M., Stilla, U.: LiDAR-supported Navigation of UAVs Over Urban Areas. Surveying and Land Information Science, Journal of the American Congress on Surveying and Mapping 70(3), 139–149 (2010); ISSN (printed) 1538-1242, ISSN (electronic) 1559-7202Google Scholar
  5. [2008]
    Himmelsbach, M., Müller, A., Lüttel, T., Wünsche, H.-J.: LIDAR-based 3D Object Perception. In: Proceedings of 1st International Workshop on Cognition for Technical Systems, München, p. 7 (2008)Google Scholar
  6. [2009]
    Hommel, M.: Verification of a Building Damage Analysis and Extension to Surroundings of Reference Buildings. In: Laserscanning 2009, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Paris, vol. 38(3/W8), pp. 18–23 (2009)Google Scholar
  7. [2010]
    Matikainen, L., Hyyppä, J., Ahokas, E., Markelin, L., Kaartinen, H.: Automatic Detection of Buildings and Changes in Buildings for Updating of Maps. Remote Sensing 2(5), 1217–1248 (2010)CrossRefGoogle Scholar
  8. [2011]
    Moras, J., Cherfaoui, V., Bonnifait, P.: Moving Objects Detection by Conflict Analysis in Evidential Grids. In: Proceedings of the IEEE Intelligent Vehicles Symposium IV 2011, Baden-Baden (2011)Google Scholar
  9. [1985]
    Moravec, H.P., Elfes, A.: High Resolution Maps from Wide Angle Sonar. In: Proceedings of the IEEE International Conference on Robotics and Automation, St. Louis, pp. 116–121 (1985)Google Scholar
  10. [1999]
    Murakami, H., Nakagawa, K., Hasegawa, H., Shibata, T., Iwanami, E.: Change Detection of Buildings Using an Airborne Laser Scanner. ISPRS Journal of Photogrammetry and Remote Sensing 54(2-3), 148–152 (1999)CrossRefGoogle Scholar
  11. [1991]
    Puente, E.A., Moreno, L., Salichs, M.A., Gachet, D.: Analysis of Data Fusion Methods in Certainty Grids - Application to Collision Danger Monitoring. In: Proceedings of the IEEE International Conference on Industrial Electronics, Control and Instrumentation, Kobe, pp. 1133–1137 (1991)Google Scholar
  12. [2010]
    Rutzinger, M., Rüf, B., Höfle, B., Vetter, M.: Change Detection of Building Footprints from Airborne Laser Scanning Acquired in Short Time Intervals. In: ISPRS TC VII Symposium 100 Years ISPRS, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, Vienna, vol. 38(7b), pp. 475–480 (2010)Google Scholar
  13. [1976]
    Shafer, G.: A Mathematical Theory of Evidence. Princeton University Press, Princeton (1976)MATHGoogle Scholar
  14. [1998]
    Thrun, S.: Learning Metric-Topological Maps for Indoor Mobile Robot Navigation. Artificial Intelligence 99(1), 21–71 (1998)CrossRefMATHGoogle Scholar
  15. [2004]
    Vögtle, T., Steinle, E.: Detection and Recognition of Changes in Building Geometry Derived from Multitemporal Laserscanning Data. In: XX ISPRS Congress 2004, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Istanbul, vol. 35(B2), pp. 428–433 (2004)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Marcus Hebel
    • 1
  • Michael Arens
    • 1
  • Uwe Stilla
    • 2
  1. 1.Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSBEttlingenGermany
  2. 2.Department of Photogrammetry and Remote SensingTechnische Universität MünchenMünchenGermany

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