International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
original CIR imagery based on the available DSM. Figure 5 
shows the resulting ortho-image of the small test area. 
3.2. Combination of Multisource Data 
A number of techniques, aiming at the combination of 
multisource data for scene labelling, are available. A review of 
concepts and ideas for utilization of additional datasets in 
multispectral classification procedures is for example given in 
(Hahn and Stätter (1998)). Generally, for our application two 
approaches are feasible. The available multispectral and 
geometric information can be combined applying the additional 
channel concept or the hierarchical classification approach. 
In hierarchical classification, the different types of data are 
applied in order to successively divide the working area into 
more detailed object classes (Savian and Landgrebe (1991)). 
Figure 6 shows the subdivision of the scene into vegetation 
(black) and non-vegetation (white) areas. This step was 
performed based on the analysis of the CIR ortho-image. 
Fig. 6. Vegetation and non-vegetation regions extracted 
from CIR aerial image. 
In the next step of the hierarchical classification, these areas can 
be further subdivided based on the laser scanning data using the 
information on the local height above ground, which is 
provided by the normalized DSM. The vegetation regions can 
be separated into tree regions (high values of the normalized 
DSM) and other vegetation like grass-covered areas (low values 
of the normalized DSM). Accordingly, the non-vegetation areas 
can be differentiated into buildings (high areas) and non 
building regions like streets (low areas). 
The main problem of this hierarchical or layered classification is 
that classification errors of the first step are propagated to the 
subsequent steps. Furthermore, the additional channel concept 
enables a more flexible processing of the data. This is the 
reason, why we prefer the use of this concept in our approach. 
3.3. Additional Channel Concept 
The main objects we are interested in are buildings, streets, trees 
and grass-covered areas. In order to demonstrate the 
insufficiencies of a standard classification, which is restricted to 
the analysis of multispectral information, a maximum likelihood 
classification was applied to the CIR ortho-image. The result of 
the separation into the required object classes is depicted in 
Figure 7. For this and all other classification examples, the 
training areas required in order to obtain the spectral 
characteristics of the different landuse classes were digitised 
manually. Based on this information, the pixels are assigned to 
one of the predefined classes in the classification stage. 
Alternatively, the training can be derived automatically from 
already existing GIS databases (Walter (1998)). 
Buildings HH Trees 
Roads 1 1 Grass-covered 
Fig. 7. Maximum likelihood classification based on CIR 
imagery. 
Figure 8 depicts the classification result again by a standard 
maximum-likelihood classification. However, in this case, the 
normalized DSM was introduced as an additional channel in the 
classification and thereby combined with the multispectral 
channels. Figures 7 and 8 demonstrate very well that in an