Istanbul 2004 
iatic building 
RS J. 50 (4), 
  
EXTRACTION OF SPATIAL OBJECTS FROM LASER-SCANNING DATA USING A 
CLUSTERING TECHNIQUE 
Bin Jiang 
Division of Geomatics, Dept. of Technology and Built Environment, University of Gävle, SE-801 76 Gävle, Sweden - 
bin.jiang(g)hig.se 
Commission III, WG III/3 
KEY WORDS: DEM/DTM, LIDAR, Laser-scanning, Algorithms, Classification 
ABSTRACT: 
This paper explores a novel approach to the extraction of spatial objects from the laser-scanning data using an unsupervised 
clustering technique. The technique, namely self-organizing maps (SOM), creates a set of neurons following a training process based 
on the input point clouds with attributes of xyz coordinates and the return intensity of laser-scanning data. The set of neurons 
constitutes a two dimensional planar map, with which each neuron has best match points from an input point cloud with similar 
properties. Because of its high capacity in data clustering, outlier detection and visualization, SOM provides a powerful technique 
for the extraction of spatial objects from laser-scanning data. The approach is validated by a case study applied to a point cloud 
captured using a terrestrial laser-scanning device. 
1. INTRODUCTION 
Laser-scanning has been proven to be as an effective 3D data 
acquisition means for extracting spatial object models such as 
digital terrain models and building models and it has been 
widely used nowadays in geospatial information industry 
(Ackermanm 1999). However what the laser-scanning can 
acquire is a digital surface model, which captures all points 
from treetops, buildings and ground surface depending on the 
circumstance. For many practical applications, we often expect 
spatial object models such as digital terrain models (DTM) of 
the bare earth surface and 3D building models. To this end, 
various research efforts have been made over the past years in 
an attempt to processing the original captured datasets for the 
derivation of various spatial objects. For instance, in order to 
get a DTM of the bare earth surface, we have to remove those 
non-terrain and undesired points. 
This process of deriving spatial objects involves a range of 
operations such as filtering, interpolation, segmentation, 
classification, modelling and possible human interaction if no 
complete automatic way is reached (Tao and Hu 2001). Most 
filtering algorithms are targeted to the derivation of DTM, thus 
assumptions on the spatial distributions of points or geometric 
characteristics of a point relative to its neighbourhood in a 
terrain surface are used to construct various filtering strategies. 
For instance, through using TopScan, Petzold et al. (1999) used 
the lowest points found in a mowing window to create a rough 
terrain model. And it is used to filtering out those points higher 
than a given threshold. Then repeat the procedure several times 
with smaller sizes of moving window, and finally lead to a 
DTM. Kraus and Pfeifer (1998) used an averaging surface 
between terrain points and vegetation points to derive residuals 
of individual points, and then use the residuals to determine the 
weights of individuals to be selected or eliminated. Maas and 
Vosselman (1999) adopted approaches based on the ideas of 
moment invariants and the intersection of planar faces in 
triangulated points for extracting building models. The slope- 
based filter (Vosselman 2000) considers an observed fact that a 
large height difference between two nearby points is unlikely to 
  
be caused by a steep slope in the terrain. These algorithms 
together with many others as reviewed in a recent comparison 
study (Sithole and Vosselman 2003) are proven to be effective 
and efficient in the studies, noting that they sometimes require 
interpolation of a point cloud into regular grid format in order 
to carry out the post-processing. When it is too complex to 
distinguish different object points, additional information is 
needed for classification or to achieve better results (e.g. Haala 
and Brenner 1999, McIntosh and Krupnik 2002). However, 
these filtering algorithms are all based one way or another on 
supervised classifications with prior knowledge or assumptions 
about different spatial objects. The supervised classification 
solutions show various constraints in the sense of efficiency, 
e.g. sensitivity to varying point densities, limited applicability 
for certain kinds of spatial objects or under a certain 
circumstance, and difficulty in dealing with stripe etc. 
The supervised classification solutions rely much on the human 
understanding or prior knowledge of the point geometric 
characteristics of spatial objects. However, it is very difficult in 
reality to get a true understanding, in particular when many 
objects are involved in a point cloud. It also depends on our 
specific task: e.g. to derive one single object or all objects with 
one point cloud. It is probably an easy task to derive one object 
rather than to distinguish all objects from an input point cloud. 
For the case of single object, we can investigate the point cloud 
and try to figure out the characteristics of its point distribution 
and further design an appropriate algorithm. Furthermore, many 
assumptions about the point characteristics do not always hold 
true, and they depend on the circumstances of laser-scanning. 
When come to the situation where all objects should be derived, 
we believe unsupervised clustering seems a more appropriate 
way. 
One of the major reasons why unsupervised methods are so 
important in the post-processing is that it is very difficult to 
assume some characteristics of a certain object. Instead of 
figuring out the assumption, unsupervised methods put these 
characteristics aside and adopt a simple assumption, i.e. same 
objects should have the same similarities in terms of their xyz