International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol XXXV, Part B2. Istanbul 2004 
> 
4 
Expected Singularity Optimum Configuration 
Figure 6. Singular and optimum configuration to recover scale 
and shift components 
At this point, the components of the registration paradigm have 
been addressed. Straight line segments are chosen as the 
registration primitives along with a 3D similarity transformation 
function. Also, the similarity measure is formulated based on 
the selected primitives and transformation function. The quality 
of fit, represented by the resulting variance component from the 
similarity measure as well as the residuals and discrepancy 
between conjugate features, will be used to validate and check 
the quality of the calibration parameters associated with the 
imaging and ranging systems. 
3. EXPERIMENTAL RESULTS 
In order to verify the methodology and procedure, imagery and 
laser data over an urban area were collected, Figure 7. 
  
Figure 7. Aerial image of area under consideration 
CANON EOS 1D digital camera (pixel size = 11.5um, focal 
length = 28.469051 mm) was used to capture twenty-three 
overlapping images in three flight lines. Based on a flying 
height of 200 m, a base of 70 m, and assuming a one pixel! 
measurement error, the expected planimetric accuracy is 
estimated as 0.09 m while the vertical accuracy is expected to 
be 0.36 m with an overall spatial accuracy of 0.37 m. From the 
laser scanner hardware and mission specifications, the spatial 
laser data accuracy is expected to be in the range of 0.35 m. 
With the above anticipated accuracies, the surfaces are expected 
to have a discrepancy in the range of 0.5 m. 
Straight line segments as well as some tie points were measured 
as described earlier and then incorporated in a bundle 
adjustment procedure with an arbitrary datum. The output of the 
bundle adjustment included the ground coordinates of tie points 
in addition to the ground coordinates of points defining the 
c 
object space line segments. 
In the laser data, homogeneous patches have been manually 
identified to correspond to that of selected features in imagery. 
174 
Planar surfaces are then fitted through the selected patches, 
from which neighbouring planar surfaces are intersected to 
produce object space line segments. A total of twenty-three well 
distributed 3D edges within the area of interest have been 
identified along ten buildings from three laser strips. 
Least-squares adjustment is then used to solve for the 
parameters of the 3D similarity transformation function and the 
results are shown in Table 1. A visual presentation of datasets 
after transformation is shown in Figure 8. 
  
  
  
"Scale | 1.008609 |£0.002245 | 
ES 
YXrmk.k24.24241045 | 
7,00) 3908. ] +044 | 
| Q(*) | 1.892336 |+0.132785 
| (9?) 1315345 |+0.354789 
K(°) | 0.320431 |40.094157 
  
  
  
  
Table 1. 3D similarity parameters between laser and 
photogrammetry models. 
LU 
  
  
  
P 
“| vor = ; . 
| After Transformation. 
s / 
| / 
i 
3 =. 2 
mA oig 
£a m 
i tw 
| 
99 
| DD Co 
| 
a / 
| 
| Top View 7 
y en SERRE d» T T 1 2 ik B sx 
Tiworcod Less 
"e 
wr = XQ ame ^ 
9r Side View > I - [^ E ed | 
km an v6 5 T = 36 
Figure 8. Aerial photogrammetric and laser datasets after 
transformation 
To assess the quality of fit, the mean normal distance between 
the laser and transformed photogrammetric line segments 
turned out to be 3.27 m, a surprisingly poor result considering 
the camera, flight mission, and laser scanner specifications. The 
expected surface fit was in the range of a sub-meter. 
A closer look at the side view in Figure 8, the discrepancy 
revealed that the pattern of deviation between the laser and 
photogrammetric features is similar to deformations arising 
from ignored radial lens distortion. To determine the radial lens 
distortion of the implemented camera, two alternatives were 
followed. The first alternative implemented the laser features as 
control information within the bundle adjustment procedure in a 
self-calibration mode allowing for the derivation of an estimate 
for the radial lens distortion. The estimated radial lens distortion 
coefficient turned out to be -6.828x10?mm '. The second 
alternative determined an estimate of the radial lens distortion 
through a bundle adjustment with self-calibration involving 
imagery captured from a test field with numerous control 
points, which had been surveyed earlier. The estimated radial 
lens distortion coefficient turned out to be -6.913x10^mm , 
which is almost identical to the value determined by 
implementing the laser features as control within the 
photogrammetric — bundle — adjustment. — Afterwards, the 
Internatioi 
registratio 
radial lens 
function ai 
After con: 
distance b 
line segm 
expected 
deviations 
place as « 
overall n 
introducin 
Table 2. 3! 
al 
4€ 
Analyzing 
extracted 
suggested 
discrepanc 
at the dis 
cause and 
errors. Al 
establish 
photogram 
prior resez 
section 1 
photogram 
calibrate t 
biases. In : 
reference 
between tl 
rendered « 
model of t 
Further re: 
of differer 
Developin 
derived z 
extension. 
(MIHT) . 
correspon« 
surfaces : 
transforme 
will also t 
been assi 
relating o 
discrepanc 
GPS/INS/| 
of the invc