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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
4.2 Case Study 2: MATCH-T DSM from ADS40 compared 
to ALS 50 First Pulse point cloud 
This case study estimates the accuracy of the MATCH-T DSM 
point cloud from a reference surface. This surface model was 
generated from the LIDAR first pulse point cloud using the 
software SCOP++. The information about the project can be 
found in the table 3. 
From the filtered MATCH-T DSM point cloud a reduced point 
cloud was obtained. For each point a height difference to the 
interpolated LIDAR surface is computed, from those 
differences the accuracy of the MATCH-T DSM point cloud 
has been estimated. 
Sensor 
ADS 40 
ALS 50 
GSD 
15 cm 
Orientation 
Adjusted 
Adjusted 
Spectral 
characteristics 
Forwards and 
Backward 
panchromatic 
Nadir Green 
channel 
DSM 
representation 
Point cloud 
Point cloud from 
First Pulse 
Point density 
4 pts/m 2 
2 pts/m 2 
Table 3. Input information of case study 1 
LIDAR data are used as reference because at this image scale 
the accuracy of the interpolated surface from the LIDAR points 
is higher than the MATCH-T DSM point cloud. The result can 
be found in the table 4. 
Surface type 
Textured Roof 
surfaces 
Flat Terrain 
Number of MATCH-T 
DSM checked points 
26782 
57850 
Percent of validated 
points 
96,3% 
99,8% 
Standard deviation of 
validated points 
26,4 cm 
19,0 cm 
Mean Z shift 
- 25,6 cm 
- 6 cm 
Table 4. Summary of the results of case study 2 
As it can be expected the MATCH-T DSM point cloud is more 
accurate on the flat terrain than on the roof surfaces. But height 
differences on sloped surfaces like roofs do not directly 
correspond to the residual error, which is measured 
perpendicular to the sloped surface. Furthermore MATCH-T 
DSM delivers a point cloud that contains approximately twice 
as many points as the comparable LIDAR flight. Thus some 
deviations in the comparison between LIDAR and MATCH-T 
DSM result from small structures like chimneys and jutties 
which are not always completely represented in the LIDAR 
point cloud or the MATCH-T DSM. 
The high percentage of accepted points shows that the 
MATCH-T DSM point cloud is very well filtered. The few 
gross errors can be eliminated through a second filtering 
process. 
The achieved mean accuracy corresponds to a matching 
accuracy better than half of a pixel. Then, the accuracy of the 
MATCH-T DSM point cloud is well suited for automatic 
building generation or high precision DTM production from 
high resolution images. 
5. CONCLUSION 
This paper has shown that MATCH-T DSM delivers a highly 
reliable and highly accurate result. The point cloud extracted 
with MATCH-T DSM from high resolution images delivers a 
better 3D representation than a traditional raster. The point 
cloud extracted with MATCH-T DSM is well suited for 
building extraction, high accurate DTM production and object 
recognition. The studies show that MATCH-T DSM is 
competitive to LIDAR for large surface DSM production 
especially if coupled with high resolution orthophoto 
production. One can consider MATCH-T DSM as a passive 
point scanner, the measurement speed only depending on office 
computing resources. 
Figure 3. Extracted building from MATCH-T DSM point cloud 
REFERENCES 
Cramer, M., 2007. The EuroSDR Performance Test for Digital 
Aerial Camera Systems, Photogrammetric Week '05, 
Wichmann, Heidelberg, pp. 79-92. 
Cramer, M., 2005. 10 Years ifp Test Site Vaihingen/Enz: An 
Independent Performance Study, 50 Photogrammetric Week, 
Stuttgart, Wichmann (.ed),pp. 89-106. 
Ackermann, F. & P. Krzystek, New Investigations into the 
Technical Performance of Automatic DEM Generation, 
Proceedings 1995 of ACSM/ASPRS Annual Convention, 
Charlotte, NC, Vol. 2, pp. 488-500. 
ACKNOWLEDGEMENTS 
We would like to acknowledge the contributions of many 
companies who have provided aerial project and LIDAR data. 
This paper would not be possible without their contribution. 
Last but not least: thanks to Microsoft Photogrammetry Graz 
Austria, who provided the data set of the case study 1, Astec 
GmbH, Kreba-Neudorf, Germay, who provided the data set of 
the case study 2, InterAtlas, Clamart, France, and FMM Ges. m. 
b. H, Salzburg Austria, for their input of high resolution images. 
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