Extraction of Buildings Footprint from LiDAR Altimetry Data with the Hermite Transform

  • José Luis Silván-Cárdenas
  • Le Wang
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6718)

Abstract

Building footprint geometry is a basic layer of information required by government institutions for a number of land management operations and research. LiDAR (light detection and ranging) is a laser-based altimetry measurement instrument that is flown over relatively wide land areas in order to produce digital surface models. Although high spatial resolution LiDAR measurements (of around 1 m horizontally) are suitable to detect aboveground features through elevation discrimination, the automatic extraction of buildings in many cases, such as in residential areas with complex terrain forms, has proved a difficult task. In this study, we developed a method for detecting building footprint from LiDAR altimetry data and tested its performance over four sites located in Austin, TX. Compared to another standard method, the proposed method had comparable accuracy and better efficiency.

Keywords

Building footprint Hermite Transform Local Orientation 

References

  1. 1.
    Herrera-Domínguez, P., Altamirano-Robles, L.: A Hierarchical Recursive Partial Active Basis Model. Advances in Pattern Recognition, 1–10 (2010)Google Scholar
  2. 2.
    Miliaresis, G., Kokkas, N.: Segmentation and object-based classification for the extraction of the building class from LIDAR DEMs. Computers & Geosciences 33(8), 1076–1087 (2007)CrossRefGoogle Scholar
  3. 3.
    Silván-Cárdenas, J.L., Escalante-Ramírez, B.: Image coding with a directional-oriented discrete hermite transform on a hexagonal sampling lattice. In: Tescher, A. (ed.) Applications of Digital Image Processing XXIV, vol. 4472, pp. 528–536. SPIE, San Diego (2001)CrossRefGoogle Scholar
  4. 4.
    Silván-Cárdenas, J.L., Escalante-Ramírez, B.: The multiscale Hermite transform for local orientation analysis. IEEE Transactions on Image Processing 15(5), 1236–1253 (2006)CrossRefGoogle Scholar
  5. 5.
    Silván-Cárdenas, J.L., Wang, L.: A multi-resolution approach for filtering LiDAR altimetry data. ISPRS Journal of Photogrammetry and Remote Sensing 61(1), 11–22 (2006)CrossRefGoogle Scholar
  6. 6.
    Silván-Cárdenas, J., Wang, L., Rogerson, P., Wu, C., Feng, T., Kamphaus, B.: Assessing fine-spatial-resolution remote sensing for small-area population estimation. International Journal of Remote Sensing 31(21), 5605–5634 (2010)CrossRefGoogle Scholar
  7. 7.
    Song, W., Haithcoat, T.: Development of comprehensive accuracy assessment indexes for building footprint extraction. IEEE Transactions on Geoscience and Remote Sensing 43(2), 402–404 (2005)CrossRefGoogle Scholar
  8. 8.
    Weidner, U., Förstner, W.: Towards automatic building extraction from high-resolution digital elevation models. ISPRS Journal of Photogrammetry and Remote Sensing 50(4), 38–49 (1995)CrossRefGoogle Scholar
  9. 9.
    Wu, Y., Si, Z., Gong, H., Zhu, S.: Learning active basis model for object detection and recognition. International Journal of Computer Vision, 1–38 (2009)Google Scholar
  10. 10.
    Zhang, K., Yan, J., Chen, S.: Automatic construction of building footprints from airborne LIDAR data. IEEE Transactions on Geoscience and Remote Sensing 44(9), 2523–2533 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • José Luis Silván-Cárdenas
    • 1
  • Le Wang
    • 2
  1. 1.Centro de Investigación en Geografía y Geomática “Ing. Jorge L. Tamayo” A.C.Mexico D.F.Mexico
  2. 2.Department of GeographyThe State University of New YorkBuffaloUSA

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