The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part BI. Beijing 2008 
305 
In the first part, the results for each strip over each individual 
building point out to some learning (Figure 3). Redundancy 
(observations) can enhance the results namely buildings with 
many observations (points) give better results. Buildings with 
very small or big stdv, in their individual solutions, give 
extremely different weights from the mean stdv to the solution. 
They should be avoided from the total solution setting up a 
threshold for big and small buildings, so only the medium size 
buildings remain in the solution. The following thresholds were 
used: (LiDAR 500-2000 points) and (TINs 500-1500). 
Although the algorithm is capable of detecting these types of 
errors (o x , a y , a z ), some buildings with errors have been located. 
Possible errors can originate due to the photogrammetric 
restitution (e.g. building 12). Through data snooping procedure 
LiDAR points, 4-5% outliers are removed and the parameters 
are stabilized. It is illustrated in Figures 5, 6 e.g. for angle 
omega. The boresight offset components cannot be detected 
accurately in this type of data due to high correlation of 
parameters and noise. Thus in case of a bigger offset, it could 
be detected (a x , a y , a z > b x , b y , b z ). 
In the second part, the results of the total solutions were 
analyzed, indicating that the algorithm can absorb the existence 
of at least 15% ‘problematic-out of threshold’ buildings. This 
should be considered as a restriction of this algorithm. Through 
many tests with differently distributed buildings, it can be 
concluded that positions similar to the Gruber positions, widely 
used in photogrammetry, (6-8 buildings) are the optimum 
(Figure 4). It can be noticed that the shape of buildings don’t 
affect the results. This type of distribution of the buildings has 
been actually confirmed in LiDAR boresight misalignment 
solution (Csanyi and Toth, 2007). Combinations of strips are 
necessary: at least, 2 strips flying in opposite directions are 
needed to recover the signs of the parameters. Also a 3rd strip, 
in a crossing direction, is preferred for enhancing the 
incompleteness of the parallel strips. This cross strip could 
decrease the effects of possible systematic errors which could 
arise from many sources e.g. different flying height between 
strips. 
6. CONCLUSIONS 
The feasibility of using urban areas for boresight misalignment 
has been investigated. The influence of the number and 
distribution of the necessary ‘building-positions’ on boresight’s 
misalignment parameter estimation is evaluated. Experiments 
with various number and distribution of ‘building-positions’ are 
presented, analyzed and evaluated through QA/QC statistical 
tests. 
Under operational circumstances, the real accurate values of the 
boresight misalignment are never accurately known and could 
only be estimated. Furthermore, boresight misalignment 
parameters could change over a relatively short time period. 
Therefore, having a mechanism to almost continuously check it 
is a valuable tool. In other words, the detection of possible 
changes in the values (in the remaining boresight misalignment) 
through a QA/QC validation process, can assure a sustained 
product’s quality. These algorithms can be considered as a good 
and fast tool for estimating parameters and detecting any 
changes. 
It is noticeable that this algorithm in not restricted by detailed 
restitution, as only the main skeleton of a building is needed. 
This is different from some other algorithms where, for instance, 
the availability of roof planes is a prerequisite. In this algorithm 
buildings should include points within the threshold range, and 
at least 2 strips flying in opposite directions are necessary; 
obviously a good distribution of the buildings is the necessity to 
reach an optimum. 
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