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Fusion of sensor data, knowledge sources and algorithms for extraction and classification of topographic objects
Baltsavias, Emmanuel P.

International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999
into straight line segments and arcs (curvilinear segmentation,
obtained with the ? -s method), straight lines are grouped to
polylines, and polylines with 90 degree vertices are singled
out as they play an important role in many man-made objects.
Similarly, surface patches within a hump, that can be
analytically approximated, are found with various methods. A
particularly efficient method is the Hough transform for
finding planar surface patches. Here, the parameters of planes
defined by the triangles that have been determined by a
Delauney triangulation, enter the accumulator array. A cluster
analysis of the accumulator array indicates triangles that lie in
the same plane. As an optional step, all points found to be in a
plane are used in a least-squares adjustment for determining
the parameters more precisely. Again, the motivation for this
step is related to the observation that man-made objects
consist of planes that have certain preferred orientations.
We have described a conceptual multisensor fusion system for
the purpose of recognizing objects of urban scenes. The
system is characterized by a multi-stage fusion approach that
includes laser scanning data, aerial and multispectral imagery.
Experimental results confirm the proposed architecture
(Figure 6). The experiments are performed with the test
dataset of Ocean City. The test site is a rather complex urban
area, including residential and commercial areas with
buildings of different size and shape, live and dead
vegetation, and trees right next to buildings.
In light of an automatic system, we performed unsupervised
classification of the multispectral data with the number of
classes and the band selection/combination as parameters.
The classification results are remarkably robust. That is, the
parameter selection does not appear to be very crucial. The
results also clearly demonstrate the usefulness of multispectral
data for object recognition. Moreover, they confirm that
object recognition in complex urban scenes cannot be reliably
solved with one sensor only.
Most fusion processes are carried out in the 3-D object space.
This requires that for every sensor a relationship between the
sensor space and object space is established. Another
interesting challenge for fusion is related to the problem that
sensors have different resolutions. For example, aerial
imagery and multispectral imagery of the test site differ in
resolution by a factor of ten. We solve this problem with a
scale space approach.
Future research will address the problem of object modeling,
taking into account the features that can be extracted from
different sensors. By the same token, more research will be
devoted to inference processes and identity fusion.
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