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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
applied. As the digitizing of the new training areas is very time
intensive, we are aiming at the derivation of the training areas
from already existing GIS data. This can be realized, if we
assume that the number of wrongly captured GIS objects are
substantially less than the number of all GIS objects of the
Another open problem is the homogeneous quality of the
different data sources, which is required over the whole test area
for the approach. This premise can for example be violated for
the spectral data due to occlusions near buildings. At these
regions no spectral information is available. Additionally, as
discussed above, insufficiencies of the laser scanner can result
in small areas, where the laser beam is not reflected. So even
though very promising result could be achieved, additional
effort, especially at the above mentioned regions, for data
acquisition and processing is required.
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