Full text: Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
Fig. 5. This stereo pair shows a small image patch from the low altitude flight. Laser points from the same region are projected back 
to the two images with their exterior orientation parameters. Viewed under a stereoscope, a vivid 3-D scene appears with the 
laser points on the top of the surface that is obtained by fusing the stereopair. The colored dots indicate different elevations, 
with red the lowest and blue the highest points. Note that blue points are on top of buildings. 
employed in aerial stereopairs, we may obtain surface 
discontinuities directly. This is because edges in aerial images 
may have been caused by breaklines in object space. Not all 
edges correspond to breaklines, but there is hardly a breakline 
that is not manifest as an edge in the image. Fusing surface 
features is actually a two step process. First, the images 
covering the same scene are processed using multiple image 
matching (Krupnik, 1996). Next, the surface obtained during 
image matching is fused with the laser surface. Obviously, the 
aerial imagery must be registered to the same object space the 
laser points are represented in. In turn, this requires an aerial 
triangulation. To achieve the best fit between the visible and 
laser surface, the aerial triangulation should be performed by 
incorporating the laser data (Jaw, 1999). 
Figure 5 illustrates the registration of the visible and the laser 
surface. Here, a small image patch from two overlapping 
photographs is shown, together with laser points that have 
been projected back from object space to the images based on 
their exterior orientation. Viewed under a stereoscope, one 
gets a vivid 3-D impression of the surface. The figure also 
demonstrates the distribution of laser points, which is rather 
random with respect to surface features. For example, features 
smaller than the (irregular) sampling of laser point may not be 
captured. Moreover, Figure 6 clearly supports the claim that 
breaklines, such as roof outlines, should be determined from 
the aerial imagery. 
The refined surface, obtained in a two step fusion process, is 
now analyzed for humps in an attempt to separate the 
topographic surface. Then, the difference between the refined 
and the topographic surface would result in what we call 
hump-objects of a certain vertical dimension. The prime 
motivation is to partition the object space, such that the 
subsequent surface segmentation is only performed in areas 
identified as humps. The segmentation is a multi-stage 
grouping process aimed at determining a hierarchy of edges 
and surface patches. As an example, breaklines are segmented 
Fig. 6. Superimposed on the aerial image are the results from 
classifying the multispectral imagery and from 
segmenting the laser surface. Pink areas indicate dark, 
non-vegetated regions, and yellow are bright, non- 
vegetated regions. Green refers to woody vegetation. 
Finally, blue are shaded areas. The red contours are 
derived from laser data and they indicate humps. The 
combination green (from multispectral) and hump 
(from laser) triggers the hypothesis for a tree, for 
example. A hump with planar surface patches and a 
non-vegetated region is used for a building 
The fusion of surface information from aerial imagery and 
laser scanning systems ought to take into account the 
strengths and weaknesses of the two sensors. The major 
advantage of laser measurements is the high density and high 
quality. However, breaklines and formlines must be extracted 
from irregularly spaced samples. If feature-based matching is

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