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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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