Research Papers

Conflating LiDAR data and multispectral imagery for efficient building detection

[+] Author Affiliations
Ling Zhu

Beijing University of Civil Engineering and Architecture, School of Surveying and Mapping Engineering, No.1 Zhanlanguan Road, Xicheng District, Beijing, 100044 China

Key Laboratory for Urban Geomatics of National Administration of Surveying, Mapping and Geoinformation, No.1 Zhanlanguan Road, Xicheng District, Beijing, 100044 China

Ashton M. Shortridge

Michigan State University, Department of Geography, Geography Building, East Lansing, Michigan 48824

David Lusch

Michigan State University, Department of Geography, Geography Building, East Lansing, Michigan 48824

J. Appl. Remote Sens. 6(1), 063602 (Nov 14, 2012). doi:10.1117/1.JRS.6.063602
History: Received December 27, 2011; Revised September 5, 2012; Accepted October 11, 2012
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Abstract.  Light detection and ranging (LiDAR) point cloud data can contain millions of point returns from a diverse range of surface features, and directly reconstructing buildings from these data is challenging. Trees and other vegetation pose a particular problem in many built environments. This paper investigates several efficient procedures for detecting buildings and excluding vegetation using LiDAR and imagery data. Two general approaches for identifying and filtering out returns from vegetation are investigated: the first uses a normalized difference vegetation index (NDVI) image, while the second uses height differences. The utility of an entropy filter for improving NDVI filter performance as well as two distinct approaches for height-difference modeling are also evaluated. All methods use efficient raster-based algorithms for filtering while retaining the high spatial precision of the vector LiDAR point returns. Following removal of nonbuilding points, remaining points are segmented into distinct building features. In addition, we place particular emphasis on the analysis of processing challenges and special cases as well as the accuracy of these different methods on a large-volume LiDAR dataset covering a challenging build environment.

© 2012 Society of Photo-Optical Instrumentation Engineers

Topics

Buildings ; LIDAR

Citation

Ling Zhu ; Ashton M. Shortridge and David Lusch
"Conflating LiDAR data and multispectral imagery for efficient building detection", J. Appl. Remote Sens. 6(1), 063602 (Nov 14, 2012). ; http://dx.doi.org/10.1117/1.JRS.6.063602


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