Full text: Proceedings, XXth congress (Part 7)

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International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B7. Istanbul 2004 
  
    
  
    
     
      
    
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matching results in Alpine area. 
      
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Figure 6. Shaded 5m DSM generated from IKONOS. The city 
of Thun in the upper left seen from North-West. 
     
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The procedure mainly contains the following characteristics: 
I) It is a combination of feature point, edge and grid point 
matching. The grid point matching procedure uses 
relaxation-based relational matching, and can bridge-over 
areas with no or little texture through local smoothness 
constraints. The matched edges are introduced to control 
the smoothness constraints in order to preserve the 
surface discontinuities. 
2) The adaptive determination of the matching parameters 
results in a higher success rate and less blunders. These 
parameters include the size of the matching window, the 
search distance and the threshold value for cross- 
correlation and MPGC. For instance, the procedure uses a 
smaller matching window, larger search distance and a 
smaller threshold value in rough terrain area and vice 
versa. The roughness of the terrain can be computed from 
the approximate DSM on a higher level of the image 
pyramid. 
Linear features are important for preserving the surface 
discontinuities. À robust edge matching algorithm, using 
the multi-image information and adaptive matching 
window determination through the analysis of the image 
content and local smoothness constraints along the edges, 
is combined into our procedure. One example of edge 
matching is shown in Fig. 5. 
4) Edges (in 3D) are introduced as breaklines when a TIN- 
based DSM is constructed. This DSM provides good 
approximations for matching in the next pyramid level. 
The computation of the approximate DSM in the highest 
pyramid level uses a matching algorithm based on the 
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"region-growing" strategy (Otto and Chau, 1988), in 
which the already measured GCPs and tie points can be 
used as "seed points". 
5) If more than two images are available, the MPGC 
procedure can use them simultaneously and matching 
results are more robust. Here, the resulting DSM from an 
image pair can be used as approximation for the MPGC 
procedure. 
6) Through the quality control procedure, e.g. using the local 
smoothness and consistency analysis of the intermediate 
DSM at each image pyramid, the analysis of the 
differences between the intermediate DSMs, and the 
analysis of the MPGC results, blunders can be detected 
and deleted. 
For each matched feature, a reliability indicator is assigned 
based on the analysis of the matching results from cross- 
correlation and MPGC. This indicator is used for assigning 
different weights for each measurement, which are used when 
a regular grid is interpolated. 
4.2 Test Results 
For Thun, we used for initial matching the images (and the 
respective triangulation results) of the triplet and stereopair 
separately and for the final MPGC all 5 images. The patch size 
varied from 7^ to 17^ for initial matching and was 11^ for 
MPGC. Some areas like lakes and rivers were manually 
defined as "dead areas" via a user-friendly interface. A regular 
grid DSM with 5m spacing was interpolated from the raw 
measurements. Fig. 6 shows a visualisation of the generated 
DSM. In spite of smoothing due to the large area used in each 
point measurement, the discontinuities are quite well 
preserved. 
Tables 9 and 10 show the DSM accuracy results, without any 
manual editing. The results are evaluated based on the 
differences between the heights interpolated in the reference 
laser DSM at the planimetric position of the DSM from 
matching and the heights from matching. 
The tables show that the DSM accuracy is in the 1-5 m range, 
depending on the landcover and terrain type. A very high 
accuracy can be achieved in open areas. In these areas, more 
than 80% of the differences are less than 2 m. In urban and 
vegetation areas, the accuracy is worse, which is due to the fact 
that the reference LIDAR measurements and the parallaxes 
determined in matching refer to partly different objects. 
Matching measures higher than LIDAR at trees (in addition, at 
tress LIDAR sometimes measures below the tree tops) and 
narrow low-lying objects (like streets). Apart from that, the 
time difference between LIDAR and IKONOS data acquisition 
was 3-4 years, and the triplet of IKONOS had snow, up to 2-3 
m in the mountains. Other factors that influenced matching 
were the long shadows (sun elevation was just 19 deg), 
occlusions, espec. in the W-E mountains, very low textured 
snow areas (which were improved with our preprocessing) and 
the patch size used in matching which unavoidably leads to 
smoothing of abrupt surface discontinuities. The accuracy 
values deteriorate also due to the high bias (see mean values 
espec. in Table 9), while height accuracy also gets worse due to 
the suboptimal base/height ratio (see sensor elevation and 
azimuth in Table 1). Taking all above factors into account, it 
becomes clear that IKONOS has a very high geometric 
accuracy potential and with sophisticated matching algorithms 
a height accuracy of 0.5 m — 1 m can be achieved in open areas 
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