ISPRS Commission III, Vol.34, Part 3A „Photogrammetric Computer Vision“, Graz, 2002 
  
algorithm requires no interpolation between the laser points. 
This can be achieved by fitting an analytical model of the ditch 
height profile to the laser points in both strips (figure 3). In this 
way the height gradients that are required for the fitting can be 
taken from the analytical model instead of from the laser data. 
  
Figure3: Point cloud of the same ditch as in figure 2 viewed 
longitudinally and fitted to an analytical profile. 
Because of the relatively low point density, the detection of 
such linear features can not be done by a standard edge 
detection in a height image. As can be understood from figure 2, 
this would result in very fragmented edges. Clustering-like 
techniques seem to be more suitable for this task. 
Combining the information of several linear structures and 
planar faces results into the same information on the offsets 
between strips as would otherwise by gathered by the estimation 
of corresponding locations. Still, it may be questionable 
whether the height data alone can always provide sufficient 
information for a three-dimensional strip adjustment. 
3. MATCHING REFLECTANCE DATA 
Most laser sensors nowadays have the possibility of recording 
the intensity of the reflected laser pulse. Several authors 
suggested methods to make use of this data for the estimation of 
planimetric offsets between strips [Burman 2000, Maas 2001]. 
Indeed, reflectance data often contains much more detail that 
can be used to determine offsets (figure 4). The usage of 
reflectance data, however, also has some inherent problems. In 
the next paragraph several aspects of the noise characteristics of 
reflectance images are discussed. We then present a more 
detailed look on how edges are represented in reflectance 
images and how this affects the edge location. Finally a 
procedure is suggested to partially overcome the noticed 
problems. 
  
Figure 4: Height and reflectance data of a road crossing. 
3.1 Noise characteristics of reflectance images 
Reflectance images are known to be relatively noisy. Several 
reasons can be identified for this property: 
e The way most laser scanners measure the intensity of the 
reflected pulse is by quantising the intensity at some point 
of time, instead of integrating the intensity over a small 
period around that point. 
e Compared to the distance between the laser points the 
amount of detail may be very high. Images of urban scenes 
generally look noisier than images of rural areas (figure 5). 
e If a laser beam hits multiple objects at different heights, 
only the energy that is reflected by one of these objects is 
used for the determination of the intensity of that pulse. 
e Finally, the footprint of a laser scanner is usually much 
smaller than the distance between two laser points. Hence, 
the intensities only represent the reflectance properties of a 
small part of the terrain. The difference between the 
footprint size and the distance between two laser points can 
be quite large. E.g. a typical scanner has a footprint size of 
0.3 m at a flight height of 1000 m. Scanning with an 
opening angle of + 20° and a pulse rate of 25 kHz, the 
average point distance recorded with this scanner at a flight 
height of 1000 m and an aircraft speed of 60 m/s would be 
1.3 m. In such a configuration, the footprints only cover 
about 4% of the surveyed area. This amplifies the noisy 
appearance of reflectance imagery. 
  
  
Figure 5: 
Reflectance images of an urban and an agricultural 
scene. 
3.2 Edges in reflectance images 
In the ideal imaging case the grey value of a pixel represents the 
average grey value of the area that is covered by that pixel. 
When generating a reflectance image the grey value of a pixel is 
based on the reflectance properties of only a small fraction of 
the pixel area. 
This characteristic has an impact on the location accuracy of 
edges in reflectance imagery. In the extreme case of an infinitely 
small footprint the measured reflectance intensity will be 
representative for the surface properties at only one side of the 
edge. As in the case of height jump edges in height imagery, an 
edge in such a reflectance image should be considered as an 
edge in a binary image with the edge location properties as 
described in [Fórstner 1986]. Both the bias and the standard 
deviation of edge location depend on the orientation and length 
of the edge. 
A - 3T]