The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
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sub-pixel accuracy in all directions. SAT-PP performs better 
than ERDAS (differences up to 1/5 of the ground pixel size), 
while the rigorous model of PCI needs more GCPs in order to 
perform well. The residual distributions of Figure 4 after 
orientation with PCI rigorous model show some inconsistencies. 
There is a clear systematic deformation visible in the 
planimetric residuals of the CPs, which is however not present 
in the GCPs. Therefore the results of the rigorous model should 
be analysed more in detail in terms of statistical behaviour of 
each unknown parameter. In particular self-calibration and 
external orientation modelling should be studied more in detail. 
Unfortunately this is not possible with the PCI software. The 
rigorous models developed at ETH Zurich (Poli, 2005, 
Kocaman and Gruen, 2007) and Sapienza Université di Roma 
(Crespi et ah, 2007) will be extended in order to process 
Cartosat-1 scenes and complete the investigations in this respect. 
In addition more tests are required for the analysis of GCPs 
distribution, which seems to have an influence on the accuracy 
of Cartosat-1 stereoscenes, especially in planimetry (Kocaman 
et ah, 2008). During the investigations the accuracy assessment 
will be performed using the Leave-One-Out method too, as 
proposed in Brovelli et ah, (2008). In fact the leave-one-out 
method is a well known statistical estimation technique 
currently applied in different fields such as machine learning, 
bioinformatics and generally in any other field requiring an 
evaluation of the performance of a learning algorithm (e.g. in 
geostatistics) and can give a good contribution for accuracy 
assessment, avoiding to draw conclusions which are constrained 
to a particular set of CPs. 
5 DSM GENERATION 
The image matching was carried out independently with the 
software packages PCI-Orthoengine and SAT-PP, since they 
contain different matching strategies. PCI-Orthoengine uses 
crosscorrelation, whereas SAT-PP uses a multi-image matching 
approach especially designed for Linear Array sensors, based 
on the extraction of three kinds of features (feature points, grid 
points and edges), as explained in (Baltsavias et al, 2006, Gruen 
and Wolff, 2007) 
Clouds are not part of the requested surface model. Therefore 
they were masked out by manual measurements in the 
images.The same was done for water surfaces, because they are 
also disturbing elements for the image matcher. 
Taking into account that the different radiometry between the 
two images, due to the variations in the sensor view angles, 
may influence the matching results, the Cartosat-1 images were 
filtered to enhance the edges and then to reduce the radiometric 
errors. The images used for DSM generation in PCI have been 
radiometrically processed with a) a smoothing moving average 
filter to reduce the high frequency noise, and then b) a Sobel 
filter, that transformed the images into gradient maps. In case of 
SAT-PP, the matching algorithm applies a Wallis filter 
internally. After matching the images, from the 2D coordinates 
of the homologous points the corresponding 3D point clouds 
were generated using the corresponding orientation parameters 
previously estimated, and interpolated to generate a 5m grid 
surface model. In SAT-PP masks were manually measured in 
order to exclude clouds and water surfaces (i.e. lakes) from the 
DSM computation. 
Figure 5. Zoom into the SAT-PP DSM, visualized in shading 
mode. The white area is part of the mask. 
6 ACCURACY ANALYSIS 
At this stage, the corresponding DSMs were interpolated and 
compared to the reference DSM over an area of only 2km x 
2km with urban and open zones and free from clouds (Figure 
6). 
Figure 6. Area used for DSM comparison (BAND A). 
The 3D differences were computed through a co-registration 
with the LS3D (Least Squares 3D Surface Matching) software, 
developed at ETH Zurich, which includes an algorithm for least 
squares matching of overlapping 3D surfaces (Akca and Gruen,