The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B7. Beijing 2008 
988 
The statistical analysis of obtaining result showed that the 
performance of implemented system is desirable to detect 
building changes, while its performance have some limitations 
in buildings with no changes. This is caused by detecting unreal 
edge because of shadow, difference in exposure condition or 
losing actual edge made by shadow or vegetation coverage. 
3.1.2 Precision assessment of developed least square 
matching algorithm 
Precision refers to how close together a group of measurements 
actually are to each other. So the accepted criteria to precision 
assessment of proposed algorithm results, is shift dX. This 
parameter is calculated from the equation 7. The output result 
of this equation for two matched point after several iteration, is 
residual shift which is considered as algorithm precision. 
Indeed, the shift value is represented the ability of implemented 
system to separate two edge with no change. So the shift value 
of more precise system is smaller than the imprecise one. In an 
ideal case of a perfect match dX=0, therefore the shift value 
differences between real case and ideal one is defined the 
precision of our developed system. So the precision of our 
developed algorithm is calculated by averaging 10 matched 
points shift values for convergence case. Experiments showed 
the shift value of 0.45 pixels as algorithm precision. 
4. CONCLUSIONS 
The algorithm we presented in this paper extends the concept of 
least squares template matching to identify object outline 
changes. By using image orientation parameters and positional 
data we can reduce the problem of 3-D object monitoring to a 2- 
D image-space matching problem. Analysis of semantics within 
a template, before the actual matching taken place, improves the 
accuracy and reliability of the presented technique. 
The accuracy assessment showed that the change percentage of 
the regions that the algorithm can detect them correctly is 70%, 
the change percentage of the regions that the algorithm can not 
detect their changes is 10% and the change percentage of the 
regions with no changes while our algorithm detects changes in 
these regions is 20%. The statistical analysis of obtained result 
showed that the performance of implemented system is 
desirable to detect building changes, while its performance has 
some limitations in buildings with no changes. This is caused by 
detecting unreal edges because of shadow, difference in 
exposure condition or losing actual edges made by shadow or 
vegetation coverage. 
The ability of implemented system to separate two edges with 
no change is considered as precision criteria. Indeed, the 
average of shift values represents the precision of our 
implemented system. Experiments showed that the algorithm 
precision is 0.45 pixels. 
The capability of this novel approach is representing the image 
and object coordinates and change percentages of urban area 
and also each building in it. 
Bohuslav,V.,2004, "Image matching and its applications in 
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Gruen A., 1985,"Adaptive Least Squares Correlation: A 
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3, pp. 175-187. 
Agouris P.,1992, "multiple image multiple matching for 
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Baltsavias , E.P. , 1991,",multiphoto geometrically constrained 
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photogrammetry.Zurich. 
Habib, A., and Kelley, D. ,2001,"Automatic relative 
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Kraus, K., 1997, "Photogrammetry", Vol.2, 4 edition, Fer. 
Diimmlers Verlag, Bonn, ISBN 3-427-78694-3. 
Grien, A., and Akca, D., 2005, "least square 3D surface 
matching", ASPRS annual conference, March 7-11. 
Phalke, S., May 2005, "Change Detection of Man 
made Objects Using Very High Resolution Images", 
university of CALGARY, Department of Geomatics 
Engineering. 
REFERENCES 
Geodaetischas seminar ss/2000. "Matching methods for 
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www.photogrammetry.ethz. ch/general/person/maria/matching.p 
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