The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. Vol. XXXVII. Part Bl. Beijing 2008 
The answer to the first question has been already established in 
section 3.2, where it has been verified through a simulation 
procedure that conjugate points in overlapping strips are related 
to each other through a transformation function involving 
constant shifts and a rotation angle across the flight direction. 
Therefore, a six-parameter rigid-body transformation (three 
shifts and three rotation angles) can be used as the 
transformation function relating overlapping strips in the 
presence of the bore-sighting spatial and angular biases. The 
answers to the remaining questions depend on the nature of the 
utilized primitives. The following subsections present the 
answers to the above questions as they pertain to the selected 
primitives. 
4.1 Primitives Extraction and Matching 
Since the LiDAR footprints are irregularly distributed, no point- 
to-point correspondence can be assumed between overlapping 
strips. In this regard, other primitives must be investigated. In 
this work, the use of linear features derived from the 
intersection of neighbouring planar patches is proposed. LiDAR 
provides high redundancy in planar surfaces. Therefore, the 
plane parameters can be derived with high accuracy using an 
adjustment procedure (e.g. plane fitting). The larger the planar 
surface, the greater will be the point cloud noise reduction. 
Therefore, high accuracy linear features can be extracted by 
intersecting neighboring fitted planes. To do so, an environment 
for the extraction and matching of linear features in overlapping 
strips was developed. The process starts by displaying the 
LiDAR intensity images for overlapping strips where the 
operator selects an area where linear features might exist (e.g. 
roof ridge line). The user clicks on the centre of the area after 
defining the radius of a circle, within which the original LiDAR 
footprints will be extracted. It should be noted that the LiDAR 
intensity images are only used for visualization purposes. The 
user needs to establish the area of interest in one of the strips 
and the corresponding areas in the other strips are automatically 
defined. Figure 2a shows the specified area in one of the strips 
as well as the original LiDAR footprints in that area. Then a 
segmentation technique (Kim et al., 2007) is used to identify 
planar patches in the point cloud within the selected area. This 
segmentation procedure is independently run on the point cloud 
for all the overlapping strips. The outcome from such 
segmentation is aggregated sets of points representing planar 
patches in the selected area (bottom right portion in Figure 2b). 
For the linear features extraction, neighbouring planar patches 
are identified and the plane parameters determined. Then, the 
neighboring planes are intersected to produce an infinite 
straight-line. Using the segmented patches, the infinite line and 
a given buffer, the end points for the intersected line can be 
defined (top left portion in Figure 2b). This procedure is 
repeated for several areas within the overlap portion in the 
involved strips. 
The outcome of the extraction procedure is a set of linear 
features in overlapping strips. Due to the nature of the LiDAR 
data acquisition (e.g., scan angle, surface normal, surface 
reflectivity, occlusions), there is no guarantee that there is one- 
to-one correspondence between the extracted primitives from 
overlapping strips. To solve the correspondence problem, one 
has to utilize the attributes of the extracted primitives. 
Conjugate linear features can be automatically matched using 
the normal distance, parallelism, and the percentage of overlap 
between candidate lines in overlapping strips (Figure 3). A 
graphic visualization of matched linear features is presented to 
the user for final confirmation of the validity of the matched 
primitives. 
(a) (b) 
Figure 2. Area of interest selection and LiDAR point cloud 
extraction (a), and extracted linear features by intersection of 
segmented planar patches in the area of interest (b) 
Figure 3. Matching of conjugate linear features in overlapping 
strips 
4.2 Similarity Measure 
In this section, the similarity measure, which incorporates the 
matched primitives together with the established transformation 
function to mathematically describe their correspondence, is 
introduced. Conjugate lines will be represented by their end 
Figure 4. Underlying concept for the incorporation of linear 
features in a line-based approach for the determination of the 
transformation parameters. 
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