International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXIX-B4, 2012 
XXII ISPRS Congress, 25 August - 01 September 2012, Melbourne, Australia 
303 
bias nor drift are considered. This modification of the stochastic 
model allows to fit the HRSC object points to the MOLA DTM, 
which is introduced with cto,dtm = 100m for the terrain points. 
In this part the absolute position of the image strip is adjusted in 
an iterative way. 
3 MULTI ORBIT BLOCKS 
For the processing of photogrammetric blocks containing images 
from more than one HRSC strip an adaption of the same work- 
flow for single orbit strips (Fig. 2) can be used (Spiegel, 2007b). 
Both parts use blunder detection. In the first part blunders in the 
tie points are detected iteratively by analyzing the residuals. In 
the second part the difference between the MOLA surface and the 
HRSC points is used to find non-fitting points (Spiegel, 2007b). 
2.4 Evaluation 
To evaluate the performance of the bundle adjustment, the quality 
of the nominal and adjusted exterior orientation data is compared. 
Because of the large amount of data, a single value describing the 
geometric quality is easier to handle for systematic analysis of 
the whole data set. The mean forward ray intersection error sum 
marizes the accuracy of all object points and is a good measure 
for the internal consistency of the data (Gwinner et al., 2009). 
The evaluation step calculates the forward intersection error for 
all object points twice. Both times it uses the same set of tie point 
coordinates and only switches between nominal and adjusted ori 
entation data, so that the resulting mean intersection error reveals 
the gain in quality. 
To efficiently process large image blocks a suitable strategy has 
to be set up. It is not reasonable to process all image strips of 
the entire block together because only neighboring image strips 
overlap. Therefore, the whole image block is broken down into 
smaller sub-blocks which additionally opens up the advantage of 
parallel processing. If a sufficient number of server nodes re 
spectively CPU cores is available, it is possible to run three of the 
four processing parts (image pre-processing, tie point matching 
and evaluation) for all sub-blocks simultaneously. This drasti 
cally saves computation time. 
For the tie point matching each sub-block needs a reference im 
age which is matched against all other overlapping images. Every 
strip of the entire block has to supply this reference image once. 
If e.g. the block consists of two overlapping strips with ten sin 
gle images in total the nadir channel of the first strip is matched 
against all other nine images. The second sub-block in this exam 
ple would simply consist of the second strip. At this point merely 
the remaining area which was not captured by the first strip is 
covered. 
entire block 1. sub-block 2 sub-block 3. sub-block 
Additionally, both the nominal and the adjusted orientation in 
formation is used to generate dense 3D point clouds as used for 
DTM generation (Gwinner et al., 2010). The intersection error 
for all these points is used to plot two color coded error maps. 
This allows a visual inspection of the spatial distribution and ac 
curacy of the HRSC points. Fig. 3 shows three examples of er 
ror maps. Strip h7295_0000 displays large black areas where the 
dense matching algorithm was not able to find conjugate image 
points. The color of the small number of points, shows a low 
accuracy. There is no significant improvement through bundle 
adjustment. This is also indicated by the values for the mean in 
tersection error (38.6m and 37.7m). In contrast, the error maps 
and mean intersection error of h2091 _()()()() and h85()(L()()()0 show 
considerable improvement after bundle adjustment despite of the 
high frequent oscillations visible in strip h8500_0000. 
intersection 
error 
higher 
JJI lower 
mean values 
«J 
Ti 
Figure 4: Concept of sub-blocks using the example of three strips, 
green: 5-ray points, red: 10-ray points 
This strategy on the basis of three overlapping strips is depicted in 
Fig. 4. Three sub-blocks are built up in the combinations 1 & 2, 2 
& 3 and 3 separately. An advantage of this approach in particular 
with strips of inhomogeneous geometry is that for each sub-block 
an optimal average matching scale can be defined. In case of 
processing all image strips simultaneously an average matching 
scale for the complete block would have to be found. 
Concerning computational limitations the number of tie points 
per sub-block has to be limited. Tests have shown that if a suf 
ficient number of tie points in all overlapping areas is available, 
a further increase of the number of points does not improve the 
results. While it is clear that this strategy will fail for crossing 
strips as to be found e.g. on the poles it is good compromise with 
regard to computing power. 
In the subsequent bundle block adjustment the results of the sub 
blocks are imported and processed simultaneously. As approxi 
mate values of the exterior orientation for the pre-rectification of 
the images the results from single orbit strip processing are used. 
The reason is that while images of one strip normally exhibit a 
high relative accuracy, the discrepancy between neighboring im 
age strips can reach several hundred meters which would require 
very large search spaces for matching. When employing adjusted 
image strips the orbits fit much better among each other so that 
smaller search spaces can be used. 
Figure 3: Error maps with mean intersection error of the object The iterative blunder detection during bundle adjustment is con- 
points (black: no points) ducted initially for all strips independently as decribed above, and