The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
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The unrealistic threshold of R=0.9 for the correlation 
coefficient of the least squares matching leads to a not 
negligible loss of accepted points. On the other hand a small 
step width of just 1 pixel increases the percentage of accepted 
points very slightly, but requires a quite higher processing time. 
The study gave optimal results for the sub-matrix size for 
matching of 10 x 10 pixels. 
Optimal results have been achieved in the area with man-made 
objects with the threshold for the correlation coefficient R=0.70 
of the least squares matching, the sub-matrix of 10 xlO pixels 
and the step width of 3 pixels. A lower threshold for the 
correlation coefficient may lead to a lower accuracy, requiring a 
compromise between both. 
Area with man-made objects and trees: 
The area includes complex objects such as small and large 
buildings and trees close to roofs. Problems are caused by 
shadows, repetitive objects, occlusions and poor texture. Few or 
no points were extracted in shadow areas close to the base of 
buildings. 
The matching in dense urban area may not be sufficient to 
model discontinuities such as buildings. Buildings often 
occlude each other. Furthermore, man-made objects are usually 
made with homogenous materials causing large areas of poor 
texture. Figure 5 shows the frequency distribution of the 
correlation coefficient in a complex area. 
parameters 
R= threshold for accepted correlation 
matching success 
R = 0.90 , Step width = 3 
sub-matrix 10 xlO pixels 
48% 
R = 0.80 , Step width = 3 
sub-matrix 10 * 10 pixels 
61% 
R = 0.70 , Step width = 3 
sub-matrix 10 xlO pixels 
74% 
R = 0.70 , Step width = 1 
sub-matrix 10 xlO pixels 
76% 
R = 0.70 , Step width = 3 
sub-matrix 7 *7 pixels 
35% 
The template must have a sufficient size, but a larger size is 
corresponds to a low pass filtering, requiring also a compromise. 
The identification of points located on top of building may fail 
if too large templates are used and the objects may look like 
smooth hills and not like buildings. Beside this, noise is caused 
by trees close to buildings. 
Some “noise*’ of the DSMs has been eliminated by filtering 
with the Hannover program RASCOR (Passini et al 2002). 
Figure 4 shows the effect of filtering. 
Figure 4: Effect of noise filtering on ground and buildings 
(a) Before filtering - (b) after filtering 
Table2: Matching completeness in area with man-made objects 
depending upon chosen parameters 
motel ie«l point 
□ RHO > 0.95 
¡a 0.90 < RHO < 
0.95 
□ 0.85 < RHO < 
0.90 
□ 0.80 < RHO < 
0.85 
■ 0.75 < RHO < 
0.80 
□ 0 70 < RHO < 
0.75 
■ 0.65 < RHO < 
0.70 
□ 0.60 < RHO « 
0.65 
■ 0.55 < RHO < 
0.60 
■ 0.50 < RHO < 
0.55 
□ 0.45 < RHO < 
0.50 
□ 0 40 < RHO < 
0.45 
■ 0.35 < RHO < 
0.40 
■ 0.30 < RHO < 
0.35 
■ 0.25 < RHO < 
0.30 
■ 0.20 < RHO < 
0.25 
□ 0.15 < RHO < 
0.20 
□ 0.10 < RHO < 
0.15 
□ 0.05 < RHO < 
0.10 
□ 0.00 < RHO < 
0.05 
■ RHO « 0.00