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synthetic data can be segmented and structured as original image 
data. At image matching, synthetic data can be used independently, 
combined, or merged with the original (or pre-processed) data. They 
provide redundancy which can be helpful in difficult situations. 
9. Image analyses 
Analyses can serve for specifying (or.tuning) control parameters (to 
be used in later stages), for establishing a powerful matching stra- 
tegy, and for feature (or signature) extraction. Analyses can usual- 
'ly be restricted to target segments. 
A differentiation should be made between methods of analysis and 
image properties to be analysed. 
Methods can be statistical or structural. Examples of statistical 
methods are clustering, principal component transformation, etc. 
They do not imply external (a-priori) data, and have not been used 
in photogrammetry. 
Structural (or syntactic) methods are based on the context informa- 
tion (in space or in time), and they can exploit external (a-priori) 
information. Such methods can be based on different strategies, 
1.e., implying sequential and/or parallel processing, and using 
single or multiple algorithms. 
Properties to be analysed are image intensity variation, its signa- 
ture, and neighbourhood. Intensity variation is decisive for the 
acceptance of a target segment; if not acceptable, target size 
should be increased or it should be bypassed. 
Image Signature refers to contrast, texture, directionality, etc. 
Neighbourhood concerns properties such as connectivity and some 
other ad jacency relationships. 
10. Feature extraction 
Feature or signature extraction implies image analysis. Techniques 
vary from relatively simple to complex. The simplest variant is 
extraction of image "primitives" (or tokens) such as edges, narrow 
parallel bands, corner points, crossings, etc. More involved are 
syntactic ("rule based") approaches, whereby primitives are assem- 
bled into 'objects'. Automatic feature classification and self-lear- 
ning systems, e.g., for pattern recognition, have been favourite 
topics of research; nevertheless, success to date has been limited. 
For a two-stage matching strategy (figure 2), however, extraction of 
a few primitives (e.g. edges, corner points) suffices. 
1l. Integration of external information 
If properly selected and integrated, external (a-priori) information 
contributes to accuracy and reliability of matching and thus of the 
DTM. Such information also provides a reference for assessment of 
performance. 
External information refers to supplementary data (e.g., mean height 
of trees or houses), geometric conditions, constraints and criteria, 
and control and check data. Geometric conditions concern lines 
(straight, curved, parallel), planes (horizontal, tilted), angles 
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