describe some illustrative systems for some specific tasks and 
discuss how these choice were made. 
2. ISSUES IN MATCHING 
Several important issues need to be considered in making the 
choice of a matching strategy for a particular task. These 
include the choice of representation level for matching, 
whether the matching is done in 2-D or 3-D, whether it is 
local or global and the method of matching itself. These are 
discussed below. 
2.1 Representation Level: 
Perhaps the most important consideration in matching is to 
determine the level (or levels) of representation at which 
matching is to be performed. Some possible levels are: 
i) Direct pixel intensity (or color) values 
ii) Point features such as local variance or edges 
iii) Grouped features such as curves, sets of curves or regions 
iv) High level features such as surfaces and volumes 
Clearly, the features to be matched must be computable from 
the input data, must be invariant or quasi-invariant, and must 
be in the same form as the model or forms that can be derived 
from the model. The appropriateness of the level will depend 
on factors outlined in the introduction earlier, however, we 
can make some general observations about the choice: 
i) Higher levels of representation require more complex 
algorithms: e.g. for intensity matching, simple correlation 
may be used but matching of surfaces may require graph 
matching procedures. Computational requirements at 
higher levels may be less due to the lower number of items 
to be matched; however, this may be compensated by the 
computational requirements of obtaining the higher level 
representations. 
ii) Higher levels of representation are more distinct and thus 
the matching is likely to give less ambiguous results. 
However, more ambiguities may be present in the process 
of constructing the higher levels from the given image data 
in the first place. 
2.2 2-D vs. 3-D Matching: 
In general, matching in 3-D is more constrained and the 3-D 
features are more distinctive. Thus, if the input data is in a 3- 
D form, there are obvious advantages to performing the 
matching in 3-D. However, 3-D is not explicitly available in 
intensity images, in fact, extraction of 3-D may be an explicit 
goal of the matching process. 
2.3 Local vs. Global Matching: 
Another issue to consider is the extent over which matching 
  
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International Archives of Photogrammetry and Remote Sensing. Vol. XXXI, Part B3. Vienna 1996 
procedure should be applied at a time. Local matching can be 
very precise, but ambiguous and the various local matches 
may not be consistent with each other. Global matching is 
more robust but not necessarily accurate in local areas unless 
a single transformation relates the sources to be matched. In 
general, we may need to make a compromise between the two 
extremes or proceed from local to global matches (or vice- 
versa) in stages. 
2.4 Method of Matching: 
Several techniques of matching are available, corresponding 
to the kinds of representations used. Three kinds of methods 
are listed below: 
i) Area Correlation: here a single measure of match is 
computed by applying a transformation and matching the 
similarity between two sources. 
ii) Feature Matching: This is a modification of the area 
correlation method. À certain transformation is applied to one 
source and the number (or amount) of matching features is 
computed. Determination of what constitutes a match is now 
more complex (i.e. when can two line segments be considered 
to match and to what degree). 
iii) Structure Matching: Here a group of features is 
matched together, by considering not only individual feature 
properties but also some explicit relations between them 
(such as certain kinds of alignments, say parallelism). In 
general, these methods employ graph matching techniques. 
A good survey of these issues and approaches can be found 
in ([6], [10]). 
We now consider two specific kinds of tasks to illustrate the 
specific issues in matching and some kinds of techniques that 
have been used. First task is that of scene registration where 
one (or more) of newly acquired images need to be matched 
with a model (or map) of the site (constructed from earlier 
images or other sources); this task is important for purposes 
of change detection and model (map) updating. Second task 
is that of matching two or more images for the purpose of 
extracting 3-D models (maps). We will focus on cases where 
the input data consists of panchromatic intensity images and 
the scenes contain significant amount of man-made features 
rather than just natural terrain. 
3. SCENE REGISTRATION 
In this task, a new image needs to be registered with maps or 
models constructed from previous images. This operation is 
needed for several tasks such as detecting changes in the 
scene from the last time the models were constructed. Change 
detection is important for many civilian tasks such as map 
updating, urban monitoring and earth resource surveys and 
also for military tasks of observing significant infra-structure 
changes. 
The complexity of this task varies greatly with the kind of 
  
   
       
  
    
     
   
    
  
    
   
    
   
    
   
    
    
   
  
   
    
   
   
    
     
   
    
  
    
    
   
  
    
   
   
  
  
   
    
  
     
    
   
  
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