The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
2.3 Grid correction 
Insito calibration is more and more done to minimise the effects 
of the sensor instability. For lower precision photogrammetric 
production the insito camera calibration is not mandatory, as the 
achieved correction is within sub pixel range. For high accurate 
matching with high overlapping images this correction reduces 
the noise of the point cloud because remaining image errors 
caused by the sensor instability are better compensated [Cramer 
2007]. Usually, the benefits of the self calibration are mostly 
visible at the model border and comers. 
2.4 Ground sampling distance 
As the large frame and push broom digital cameras have a fixed 
focal length the only way to modify the GSD is to change the 
flying height but this also changes the perspective of the images. 
Therefore high resolution digital image capture is traditionally 
flown at low heights, but here the amount of occluded areas 
rises quickly. 
Of course a strong overlap of 80% reduces the amount of 
excluded areas. Nevertheless, because of the perspective 
changes, the image features are less similar than if it were 
captured from a higher altitude. This reduces the matching 
accuracy and augments the risk of miss matching. In general it 
can be said that DSM extraction from high resolution images is 
more complicated than DSM extraction from middle resolution 
digital imageries. The situation may change with the 
introduction of digital cameras with a smaller angle of view. 
3. DSM EXTRACTION METHOD 
INPHO’s automatic DTM derivation tool MATCH-T DSM has 
been redesigned to produce very dense DSM data. The most 
important improvement was the introduction of the sequential 
multi-image matching and a new robust algorithm for point 
filtering. 
3.1 Short review of the MATCH-T method 
The automatic DTM generation approach in MATCH-T is 
mainly characterised by the feature-based matching technique 
being hierarchically applied in image pyramids and a robust 
surface reconstruction with finite elements. 
For DTM extraction the measured 3D points, together with 
curvature and torsion constraints are introduced as observations. 
The weights for the curvature and torsions observations both 
regularize and smooth the DTM. 
A complete description of the MATCH-T design can be found 
in Krzystek, P. and Ackermann, F., 1995. 
3.2 Introduction to the MATCH-T DSM method 
The key idea of the MATCH-T DSM method is the automatic 
measurement of an extremely large number of irregularly 
distributed surface points. Robust statistics can successfully 
eliminate gross error to reduce the noise of the point cloud, as 
long as most of those points represent the surface and outliers 
caused by mismatches or displacement in the scene deviate 
from the majority of “good” points in a statistical sense. 
3.3 Sequential Multi-Matching 
In order to increase the amount of 3D points, the point 
extraction is no longer based on static models, but on 
computation units. Each computation unit in MATCH-T DSM 
chooses the best suited image pairs. Each image pair delivers a 
point cloud. The combined point clouds are filtered by a robust 
analysis. INPHO calls this extraction method sequential multi 
matching. 
3.4 From FBM to LSM 
The previous MATCH-T versions used feature based matching 
for the auto-correlation, where sub-pixel precision is up to one 
third of a pixel. In order to improve the matching precision, 
LSM can be optionally selected in the new MATCH-T DSM 
version. The improvement in height accuracy of the raster is 
about 20%, but computation time increases by a factor of two, 
thus LSM is optional. The user can decide himself if the 20% 
accuracy improvement is worth spending that extra time. 
3.5 Model Selection 
The selection of the best suited image pairs is based on the 
analysis of the DSM slope. The algorithm chooses images that 
have the best viewing angle of the matching unit. The algorithm 
allows a limitation of the number of models which are used for 
the DSM extraction in one matching unit. Indeed with high 
overlapping images, the amount of image pair combinations 
increases quickly by '/2*(n-l)(n) with n the number of images. 
As a significant parameter, the model azimuth direction has 
been selected. The point extraction is made in 6 main directions. 
If one model delivers not enough 3D points then MATCH-T 
DSM selects the next best suited model for this azimuth. 
It is possible that some matching units do not have any texture. 
For this reason, MATCH-T DSM analyses the quantity of 
extracted 3D points and recognizes if the image area has poor or 
no texture. Hence, MATCH-T DSM tries up to 20 models 
combinations per matching unit. 
Figure 1. True 3D filtered MATCH-T DSM 
point cloud from aerial images