The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Pari B4. Beijing 2008 
3. IMAGE MATCHING AND INVESTIGATION 
For the automatic image matching of the DMC-images the 
Hannover program DPCOR has been used, which is based on 
least squares matching. The approximate corresponding image 
positions are determined by region growing, published by 
(Heipke 1996). Object coordinates are computed by intersection. 
Figure 2 Workflow of matching procedure 
Figure 2 shows the work flow of the data handling. It mainly 
has following steps: 
• Manual measurement of few seed points if not enough 
control points already available; the number of required 
seed points depends on image similarity and decrease with 
smaller time difference of taking the both images of a 
stereo model. 
• Automatic matching in image space by least squares 
method. 
• Transformation of pixel to photo coordinates 
• Computation of ground coordinates by intersection. 
• Generation of Digital Surface Models (DSMs), 3D view, 
contour lines, visual inspection. 
The point spacing of matching can be specified in the used 
program DPCOR. By default every third pixel in line and also 
column direction will be matched. A matching of every pixel 
leads to high correlation of neighboured points. 
Figure 3 shows an example of matched points in an urban area, 
which leads to a very dense DSM. The small separated building 
with some trees around, did not lead to a clear building shape. 
Dark parts, caused by shadows, may lead to gaps. 
Figure 3 overlay of matched points to DMC-image 
(Matched points = white 
dark parts or marked in red = matching failed) 
Following situations may cause problems of image matching: 
Smooth and sleek surfaces 
Poor or no texture 
Occlusion 
Moving objects 
Surface discontinuities 
Shadows 
repetitive objects 
This may cause failed matching or reduced matching accuracy. 
Because of expected problems, the investigated area has been 
separated into two classes: 
• Open area - not disturbed by remarkable vegetation 
and buildings 
• Area with man-made objects and trees 
Forest areas were not included. 
Open area: 
The first investigation has been made with different settings - 
the tolerance limit for the correlation coefficient (R) has been 
varied between 0.7 and 0.9; the step width of matching mainly 
was 3 pixels in each direction, but also 1 pixel step width has 
been used; finally the window size of the sub-matrix for 
matching has been changed from 10 x 10 to 5 x 5 pixels. 
parameters 
R= threshold for accepted correlation 
matching success 
R = 0.70 , Step width = 3 
sub-matrix 10x10 pixels 
87% 
R = 0.80 , Step width = 3 
sub-matrix 10x10 pixels 
84% 
R = 0.90 , Step width = 3 
sub-matrix 10 xlO pixels 
75% 
R = 0.80 , Step width = 1 
sub-matrix 10 x 10 pixels 
85% 
R = 0.80 , Step width = 3 
sub-matrix 5 x5 pixels 
62% 
Table 1: Matching completeness in open area depending upon 
chosen parameters 
In open areas with sufficient object texture, the image matching 
has no problems. However, some problems are caused by 
missing texture, isolated trees and sometimes repetitive objects. 
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