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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part B4. Beijing 2008 
the line. The constraint in Equation 4 indicates that these three 
vectors are coplanar, and can be introduced for all the 
intermediate points along image space linear features. 
(V,*v)'V, = 0 <4) 
Point-based Incorporation of Linear Features 
Another technique is presented here for the incorporation of 
linear features for photogrammetric georeferencing. This 
technique uses a point-based approach in which a line is defined 
in image space, by selecting any two points along the same line, 
in overlapping imagery (Figure 4). Then, the corresponding line 
is extracted from LiDAR data using the procedure described in 
section 2.2.1, in which the extracted LiDAR-derived line is also 
represented by two points. One should note that none of the 
endpoints, whether in image space or object space, are required 
to be conjugate points (Aldelgawy et al., 2008). The only 
requirement is that the selected points should be along the same 
line. This approach is based on restricting the weight matrix of 
the points in the line direction. Consequently, the behaviour of 
these points will be fixed in all directions except for the line 
direction. This means that the points are free to move only 
along the line, which is considered as a constraint. The 
collinearity equations are used as the mathematical model. 
Figure 4: Point based incorporation of linear features. 
In this work, the weight restriction is performed in the image 
space. Therefore it uses a 2x2 weight matrix, where the weights 
of the points along the linear features are set to zero. For this 
procedure, a minimum of two non-coplanar line segments is 
needed (Habib, 2004). Having outlined the methodologies for 
the various georeferencing techniques, the remainder of the 
paper will focus on experimental results and analysis of the 
different methods. 
3. EXPERIMENTAL RESULTS 
Experimental work was conducted to validate the feasibility and 
applicability of the above approaches, and to compare the 
performance of each method. A bundle adjustment was 
performed using overlapping photogrammetric and LiDAR data 
captured over the University of Calgary campus. Nine photos in 
three strips were used. The photos were captured by an RC30 
frame analogue camera, with an average flying height of 770m, 
and a focal length of 153.33mm. The photos were then digitally 
scanned at 12 microns resolution, obtaining a 6cm GSD. Based 
on these specifications, the expected photogrammetric 
horizontal accuracy is around 0.09m, and vertical accuracy of 
about 0.30m (assuming an image measurement accuracy of 1 
pixel). Ten LiDAR strips were captured in two flight missions 
over the study area (six strips in the first day and four strips in 
the second day), with an Optech 3100 sensor. The data was 
capture with a flying height of 1000m for the first flight mission, 
and 1400m for the second. The LiDAR system provided a 
0.75m ground point spacing, and a vertical accuracy of 15cm 
for both flight missions. The horizontal accuracy for the frist 
flight mission is 50cm, and 70cm for the second. 
The experiment was conducted by applying all the alternatives 
mentioned above using control points, control patches, and 
control lines. The number of control points was 24, the number 
of control lines was 50, and the number of control patches was 
42. In the experiments using control patches and control lines, 
the number of tie points was 48. The comparative performance 
of the introduced methodologies was evaluated through 
quantitative and qualitative analyses. The following section, 3.1, 
provides a quantitative analysis on the experimental work 
performed using mean, standard deviation, and RMSE values, 
while Section 3.2 provides a qualitative analysis using 
orthoimage generation. 
3.1 Quantitative Analysis 
The quantitative analysis is performed for the three sources of 
control information as per the following sub-sections. 
3.1.1 Georeferencing Results Using GCPs: Out of the 24 
independently collected GPS surveyed points, 8 points are used 
as the ground control points, while the remaining 16 are used as 
check points. The results are summarized in the second column 
of Table 1. With a pixel size of 12 microns and an image 
measurement accuracy of 1 pixel, the expected horizontal 
accuracy is around 0.09m, while the expected vertical accuracy 
is around 0.30m. From Table 1, it can be seen that the expected 
accuracies match closely with the results computed in this 
experiment (RMSE X , RMSE Y , RMSE Z ,). 
3.1.2 Georeferencing Results Using Areal Features: 
The results from the georeferencing of the imagery using 
LiDAR-derived planar features are presented in columns 3 and 
4, in Table 1. A relatively large amount of bias is present in the 
results (Meanax, Mean A Y, MeanAz), which is not present in the 
results from Section 3.1.1 This is because a bias was observed 
between the LiDAR reference frame and the used GPS 
coordinate system. Moreover, a bias in the LiDAR system 
parameters is suspected as well. However the error amount (oy, 
ay, erf) is reasonable. The horizontal standard deviation is 
similar to the results from Section 3.1.1, while the vertical 
standard deviation is improved compared to Section 3.1.1 
results. A possible reason for this is that many more areal 
control features were used in comparison to the number of 
ground control points used in Section 3.1.1. That is, the 
improved vertical accuracy may be due to the higher 
redundancy. This bias value has affected the final values of the 
root mean square error (RMSE X , RMSE V , RMSE Z , RMSE Tota i), 
which are larger than those presented in the second column of 
Table 1. The two methods of incorporating areal features yield 
similar results. However, it was observed from the experiments 
that the weight restriction method is more sensitive to blunders 
than the coplanarity method. This can be explained by the fact 
that blunders in the planar patches will affect the estimated 
plane parameters, which might cause singularities. In the 
coplanarity method, on the other hand, planar patches are 
incorporated in the bundle adjustment by forcing the point 
patches to lie on the plane defined by three photogrammetric 
vertices. In other words, each point in the segmented patch 
provides one condition equation. The high redundancy 
promotes higher robustness against possible blunders.