Proceedings Article | 14 September 2011
Proc. SPIE. 8159, Lidar Remote Sensing for Environmental Monitoring XII
KEYWORDS: Roads, LIDAR, Image processing, Reflectivity, Clouds, 3D modeling, Buildings, Image classification, Raster graphics, 3D image processing
Airborne LiDAR data have become cost-effective to produce at local and regional scales across the United States and
internationally. These data are typically collected and processed into surface data products by contractors for state and
local communities. Current algorithms for advanced processing of LiDAR point cloud data are normally implemented in
specialized, expensive software that is not available for many users, and these users are therefore unable to experiment
with the LiDAR point cloud data directly for extracting desired feature classes. The objective of this research is to
identify and assess automated, readily implementable GIS procedures to extract features like buildings, vegetated areas,
parking lots and roads from LiDAR data using standard image processing tools, as such tools are relatively mature with
many effective classification methods. The final procedure adopted employs four distinct stages. First, interpolation is
used to transfer the 3D points to a high-resolution raster. Raster grids of both height and intensity are generated. Second,
multiple raster maps - a normalized surface model (nDSM), difference of returns, slope, and the LiDAR intensity map -
are conflated to generate a multi-channel image. Third, a feature space of this image is created. Finally, supervised
classification on the feature space is implemented. The approach is demonstrated in both a conceptual model and on a
complex real-world case study, and its strengths and limitations are addressed.
