International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
  
with Matlab 6 (Vesanto et al. 2000). Although the number of 
output vectors (neurons) of a SOM can be arbitrarily 
determined, usually we choose a number that is smaller than 
that of the input vectors. Through the training process, each 
point is supposed to have a BMU from the set of neurons within 
the SOM. It helps to set up a linkage between a SOM and the 
corresponding point cloud. The specific procedure for setting up 
such a linkage in ArcView GIS platform is as follows (Figure 
3): 
e Create a polygon theme in which each polygon has a 
hexagonal shape, representing a neuron with output 
vectors as attributes in a table (SOM table) 
e Create a link table (LINK table) with two fields, 
namely BMU and point ID 
e Link the SOM table and LINK table (note fields 
SOM-ID and BMU are equivalent) 
e Link the LINK table and NETWORK table through 
the common field street-ID 
Through the above procedure, a linkage that is set up between a 
SOM and corresponding point cloud will help to select points 
belonging to different spatial objects. 
POINT CLOUD table 
SOM table LINK table 
SOM-ID| X |Y |Z | ! 
1 
D|x[|vizl! 
1 
2 — 2 
— 
   
Figure 3: Linkage between a SOM and point cloud 
3.2 An interactive environment for clustering and selection 
Based on the above procedure, an interactive environment for 
clustering and selection can be built in a GIS platform. The 
trained SOM is imported into a GIS to setup a linkage to the 
point cloud that is represented as both 2D theme and 3D scene. 
In order to detect various clusters with the SOM, a unified 
distance matrix between a neuron and its neighbouring neurons 
(Ultsch and Siemon 1990) is computed. The distance matrix 
reflects the level of similarity between a neuron and its 
neighbouring neurons. With color scales for representing the 
distance matrix, we can easily detect clusters, i.e. those neurons 
tied closely. From the view entitled as SOM4029 in figure 4, 
we note that those neurons with light colors are supposed to be 
clusters, while those neurons with dark colors are neurons that 
are far from various centres of clusters. With the same figure, a 
cluster is selected with yellow, and the corresponding set of 
points is highlighted in both 2D view and 3D scene, from which 
we note the points are those from forest rather than from the 
ground. 
p BET 
  
  
  
  
  
  
  
  
  
Figure 4: An interface with three connected visual components: 
SOM, 2D view and 3D scene of a point cloud 
4. A CASE STUDY 
To validate the approach, we carried out a case study applied to 
a dataset that consists of 9072 points (figure 5). The dataset was 
a part of a larger dataset captured using a terrestrial laser 
scanner by the GIS institute at the University of Gávle. The 
reason why we choose the dataset is that the GIS institute has 
already manually filtered the dataset. Different spatial objects 
such as clay-road, stones, spruce and ground are extracted. Thus 
it provides a base to validate the model. 
Using a heuristic way, we decided a SOM with the size of 
40 x 29 to train the dataset. The process is performed in the way 
as follows with reference to the description in section 2. The 
1160 neurons are initialised by randomly giving some values of 
Xyz coordinates and the return intensity; and each of the 
neurons compares to the individual points with the point cloud 
to determines its best match unit using equation [1]; Then the 
winning neurons and its neighbourhood are adjusted their 
values of xyz coordinates and the intensity according to 
equation [2]. Details on parameter settings for the training 
process are shown in table 1. Once a pre-determined 
convergence is reached, the training process is finished with a 
trained SOM. The trained SOM is supposed to retain the initial 
structure of the point cloud. Figure 6 is the component 
visualizations of the SOM, and the smooth color transitions 
reflect the fact that similar neurons are being closer than those 
dissimilar. 
  
   
  
   
     
  
   
    
   
  
  
  
  
  
  
  
  
  
  
  
  
  
   
    
   
   
     
    
   
      
   
    
   
   
    
   
   
    
    
    
    
  
Internati 
Table 2: 
Param 
| Dime 
Neigh 
Learn 
Initial 
Traini 
(T) 
Initial 
radius 
Final 1 
  
Figure 
Figu 
coordina