Publications in Scientific Journals:

M. Rutzinger, B. Höfle, M. Hollaus, N. Pfeifer:
"Object-Based Point Cloud Analysis of Full-Waveform Airborne Laser Scanning Data for Urban Vegetation Classification";
Sensors, 8 (2008), 4505 - 4528.

English abstract:
Airborne laser scanning (ALS) is a remote sensing technique well-suited for
3D vegetation mapping and structure characterization because the emitted laser pulses are
able to penetrate small gaps in the vegetation canopy. The backscattered echoes from the
foliage, woody vegetation, the terrain, and other objects are detected, leading to a cloud of
points. Higher echo densities (>20 echoes/m2) and additional classification variables from
full-waveform (FWF) ALS data, namely echo amplitude, echo width and information on multiple
echoes from one shot, offer new possibilities in classifying the ALS point cloud. Currently
FWF sensor information is hardly used for classification purposes. This contribution
presents an object-based point cloud analysis (OBPA) approach, combining segmentation and
classification of the 3D FWF ALS points designed to detect tall vegetation in urban environments.
The definition tall vegetation includes trees and shrubs, but excludes grassland
and herbage. In the applied procedure FWF ALS echoes are segmented by a seeded region
growing procedure. All echoes sorted descending by their surface roughness are used as
seed points. Segments are grown based on echo width homogeneity. Next, segment statistics
(mean, standard deviation, and coefficient of variation) are calculated by aggregating
echo features such as amplitude and surface roughness. For classification a rule base is derived
automatically from a training area using a statistical classification tree. To demonstrate
our method we present data of three sites with around 500,000 echoes each. The accuracy
of the classified vegetation segments is evaluated for two independent validation sites. In a
point-wise error assessment, where the classification is compared with manually classified 3D
points, completeness and correctness better than 90% are reached for the validation sites. In
comparison to many other algorithms the proposed 3D point classification works on the original
measurements directly, i.e. the acquired points. Gridding of the data is not necessary, a
process which is inherently coupled to loss of data and precision. The 3D properties provide
especially a good separability of buildings and terrain points respectively, if they are occluded
by vegetation.

Object-based point cloud analysis, Urban vegetation, Segmentation, 3D feature calculation, Classification, Error assessment, Full-waveform, Airborne laser scanning

"Official" electronic version of the publication (accessed through its Digital Object Identifier - DOI)

Created from the Publication Database of the Vienna University of Technology.