image is then usually an artificial and ideal version 
of the signal to be located in the other. 
The matching was done off-line and interactively 
using a window-based interface to the algorithm. 
This allows all algorithm parameters to be set to 
optimum values for a particular matching problem, 
plus assessment of the result by visual inspection. 
The program transforms matched pixel coordinates 
directly to the photograph frame if the appropriate 
level of transformation exists. 
Different templates were used for the fiducial 
marks and the signalised points. Both were 
approximations of Gaussian signals and differed 
only in size. Consequently, different patch sizes 
were also used: 7x7 and 5x5 respectively, such 
that no background signal was used in the 
matching. One set of parameters for the matching 
was selected empirically and thereafter the aim was 
to use the chosen set in as many cases as possible. 
For each photograph, the fiducial marks were first 
matched and the inner orientation determined as an 
affine transformation. The fiducial mark signals 
are usually well defined and the matching can be 
done without problem. In one or two cases, dirt on 
the edge of the signal disturbed the solution, but it 
was possible to overcome this by selection of a 
different parameter set. 
The signalised points were not so straightforward 
to match; the images displayed a great variety in 
size, shape and contrast. On the average, the points 
occupied a circle of 3 pixels diameter (original size 
on the ground 40 cm). Quality control in this 
investigation was done by visual inspection: if a 
point could not be matched satisfactorily with the 
chosen parameters, then an alternative optimum set 
was found - usually a smaller patch size or 
restricted image shaping transformation. Of the 
279 points, 74 (27%) were so matched. The final 
solution was assigned a quality rating between 1 
(bad) and 5 (good), which related only to the bias 
of the solution from the point centre. These classes 
were reduced to three for the final analysis. For 
each point, the various statistics resulting from the 
least squares matching were stored and so a 
comparison was possible. The results can be 
summarised as follows: 
Table 1. LSM statistics mean values by class 
Class 
# of 
points 
Sigma 
<?o 
Standard Devns 
a x (pel) <7 y (pel) 
Corr’n 
coeff 
1/2 Bad 
19 
16.8 
0.112 
0.121 
0.90 
3 OK 
69 
14.1 
0.081 
0.082 
0.94 
4/5 Good 
191 
12.2 
0.064 
0.064 
0.96 
Although an expected improvement in the values 
does exist, the differences are not distinct. When 
the distribution of the values is also inspected, it is 
found that some points classed as 4/5 have values 
characteristic of class 1/2, and vice-versa. The 
problem lies in the main with asymmetric signals, 
where the final matching solution will inevitably be 
pulled away from the visual optimum. The bias 
cannot be directly determined from the matching 
statistics. Asymmetry was found to be due to three 
sources: noise in the photography (eg dirt); shape 
and layout of the signal, particularly if the location 
stripes were too close to the target (see example 
images); or perspective distortion. The latter of 
these was not the most significant. It should be 
stressed that no point could not be matched. 
Complete failures can usually be trapped, using 
characteristics such as many iterations or unusually 
low correlation coefficients, for example. Due to 
the interaction in the matching, no points were so 
rejected; the problem here concerns the quality of 
successful matches, in the sense that matching was 
possible. 
Figure 3. Example signalised point images: on 
the left a good one, on the right a bad. 
4.5 Bundle Adjustment of the Observations 
4.5.1 The Datasets and Configurations 
The results of bundle adjustment on two datasets 
will be presented: the manual observations and the 
digital observations including all matched points. 
Both datasets had no distortion corrections applied. 
Two control point configurations were selected: 
“PPO” with all points as full control points; and 
“PP2” with 8 full control points around the block 
perimeter and 4 height points in the centre (see 
figures 4a and 4b). The block had a certain 
asymmetry: the larger size of the original meant 
that control points on the eastern side of the block 
were in general only visible in one strip, ie had only 
two rays. On the opposite side, the perspective 
centres formed the control point boundary and 
therefore most points existed in two strips. 
Additional parameters were used in the 
adjustments. Versions with 0, 12 and 44 were 
computed, using the program Bund of the ETH. In 
all cases, a priori values of 2.5 pm for the image 
points, and 1 cm in plan, 2 cm in height for the 
ground control points were used.