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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
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Fig. 3 a. False color composite of NIR (red), red (green) and green (blue) bands; b. ISODATA clustering with 6 classes; c. ISODATA
clustering with 10 classes (weak boundaries between classes 4 and 5, 6 and 7, and 8 and 9). For explanation of the classes
see text.
Fig. 4. Edges extracted from the visible (blue), NIR (green)
and thermal (red) multispectral images were combined
into a color composite and then superimposed on the
aerial photograph. The colors indicate which spectral
image has the strongest discontinuity along the edge.
4.2. Laser scanning data and aerial imagery
Laser scanning data. Laser scanning systems are
increasingly being used in photogrammetry, mainly for
generating DEMs. Applications are as diverse as determining
the topographic surface and the canopy of forested areas or
establishing city models. Usually, the laser system is the only
sensor used on the platform. This limits the range of problems
that can be solved. More complex applications require several
sensors to be used in concert. As briefly described in section
3, the test site in Ocean City includes laser scanner data,
aerial, and multipectral imagery.
Laser scanning systems provide a fairly dense set of points on
the surface. The accuracy in elevation is about 1 dm and
footprint sizes are 1 m or less. The platform orientation
system determines the positional accuracy. The critical
component is the attitude. While the errors resulting from
GPS and ranging are virtually independent of the flying
height, the attitude error propagates linearly and thus restricts
the flying height. Current airborne laser systems hardly
exceed flying heights of 2000 m.
Refined and segmented surface. In our attempt to recognize
objects from multisensor data, the information the laser
system provides is used for surface reconstruction and
generation of hypotheses of man-made objects, such as
buildings. It has long been realized that surfaces play an
important role in object recognition. The laser points are not
directly suitable for representing the surface. For the purpose
of object recognition, we need an explicit description of
surface properties such as breaklines and surface patches that
can be analytically described. We distinguish between the
raw, refined, and the segmented surface (Schenk, 1995). The
irregularly distributed laser points describe the raw surface.
The refined surface includes surface information obtained
from aerial imagery. It is the result of fusing features from the
two sensors. The fusion process also includes the resolution
of conflicts that may exist between the laser surface and the
visible surface. The next step is concerned with segmenting
the refined surface, resulting in an explicit description that is
much more amenable for object recognition than the raw
surface, where important surface properties are only implicit.