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used to derive the EOP of the involved imagery. The 
advantages of such an approach include eliminating the need for 
costly ground control points and allowing a direct integration of 
LiDAR and photogrammetric data for the purpose of, for 
instance, orthoimage generation and 3-D city modeling. In 
addition, any bias that exists in the LiDAR data will not be 
visible in the final orthoimages, when the source of control data 
and the digital surface model have both been obtained from the 
same (although biased) data source. Different alternatives of 
incorporating both linear and areal LiDAR-derived features into 
a photogrammetric georeferencing procedure were outlined. An 
approach that adds a coplanarity constraint (for both areal and 
linear features) into the existing bundle adjustment procedure 
was explained. A second approach for incorporating LiDAR- 
derived control was outlined, in which the regular collinearity 
equations are used to incorporate areal and linear features, after 
applying weight restrictions along the features. 
A comparative analysis of indirect georeferencing using ground 
control points, LiDAR areal features, and LiDAR linear 
features was performed using real data. A semi-automated 
approach for the extraction of patches and lines from LiDAR 
data through planar patch segmentation and intersection was 
illustrated, and the mathematical models for incorporating these 
features with imagery for georeferencing were explained. A 
quantitative analysis of the georeferencing results was 
performed for each method using a check point analysis. Based 
on the experimental results, the use of LiDAR features and 
GCPs for georeferencing appeared to give compatible 
horizontal accuracies. On the other hand, LiDAR features 
seemed to give better vertical accuracies. A possible reason for 
this is that many more areal and linear control features were 
used in comparison to the number of ground control points. 
That is, the improved vertical accuracy may be due to the 
higher redundancy. It was found that the methods that use 
LiDAR-derived features as the source of control yield 
compatible results. However, when using planar patches it is 
important that planes varying in slope and orientation be 
available in the dataset, and when using linear control features, 
a minimum of two non-coplanar line segments are required. 
A qualitative analysis was then performed, by comparing the 
quality of the generated orthoimages. The orthoimage generated 
using GCPs appeared to be less accurate than the orthoimages 
generated using LiDAR areal or linear features, as more traces 
of building boundaries were visible in the former orthoimage. 
The reason for the inferior quality of the orthophotos generated 
using GCP, is that the EOP were not as accurately derived in 
this triangulation procedure, due to the fewer number of 
available control points. In addition, when using the GCP as the 
source of control, the derived EOP are in the GPS reference 
frame, while the DSM used to produce the orthophoto is in the 
LiDAR reference frame. The bias between these reference 
frames contributes to the less accurate EOP that were obtained 
using the GCP. In comparing the orthophotos produced using 
LiDAR areal and linear features, using either one seemed to 
give compatible results. This observation was assured by the 
close similarity between the two orthoimages generated using 
LiDAR areal and linear features (Figure 5). Future research 
will focus on automation of the extraction of lines and areal 
features from the imagery. In addition, the performance of the 
presented methodologies for LiDAR calibration and camera 
calibration will be investigated. 
ACKNOWLEDGMENT 
The authors would like to thank the GEOIDE Network of 
Centers of Excellence of Canada for the financial support of 
this research (SII#43), as well as NSERC. In addition, the 
authors thank the University of Calgary Information 
Technology for providing the LiDAR/image data and the 
valuable feedback. 
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