. Istanbul 2004 
  
  
  
  
  
   
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al components: 
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spatial objects 
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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B3. Istanbul 2004 
  
Table 2: Parameter settings for the SOM training 
  
  
  
  
Parameter Value 
Size (m) 1160 
Dimensionality 2 
Neighbourhood Gaussian 
  
Learning rate ( Q ) 
a(t)=a, (1+100t/7) 
  
Initial learning rate (@,) 
0.5 for the coarse period 
0.05 for the fine period 
  
Training length in epochs 
(T) 
0.51 epochs for the coarse 
period 
2.05 epochs for the fine 
period 
  
Initial neighbourhood 
radius ( o.) 
20 
  
Final neighbourhood radius 
  
  
5 for the coarse period 
1 for the fine period 
  
  
  
Figure 5: The original point cloud consisting of 9072 points 
  
Figure 6: Component visualizations of the SOM: (a) x 
coordinate, (b) y coordinate, (c) z coordinate, and (d) intensity 
à 
© 286 
  
© 
Figure 7: Five clusters detected from Umatrix of the SOM 
In order to detect different spatial objects, we derive a unified 
distance matrix (U-matrix) between the adjacent neurons 
(Ultsch and Siemon 1990). Figure 7 illustrates the distance from 
each neuron to its neighbouring neurons. We can note those 
neurons that are surrounded by darker colours tend to be 
clusters. We tried to select those points that best match to the 
clusters in the SOM, and it ends up with 5 meaningful clusters 
as indicated in figure 7. The cluster 0 match to the stones quite 
well, while the rest four clusters match to clay-road. Figure 8 
illustrates those points associated with clusters 1-4 (a) and 
points representing clay road (b). Visual inspection suggests the 
model is a useful tool for filtering scanning datasets. In the 
meantime, cautious should be taken for the model, as other 
spatial objects such as spruce and ground are not clearly shown 
with the clusters in the umatrix of the SOM. This suggests 
further work is needed with the training process, probably by 
introduction of a weight among xyz coordinates and return 
intensity. 
   
(a) (b) 
Figure 8: Points associated with detected clusters 1-4 (a) and 
points representing clay road 
5. CONCLUSIONS 
This paper explores a new approach to filtering laser-scanning 
dataset for the extraction of spatial objects based unsupervised