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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
in non-shadow areas. After the classification, the shadow and
non-shadow pixels for each landuse class are combined again to
obtain one unique class for each type of landuse. The final
result of the classification algorithm for the whole test area is
given in Figure 13.
Fig. 12. Classification using shadow areas.
Fig. 13. Classification result based on CIR imagery and laser
scanning using predefined shadow areas.
Our application of the classification is twofold. Firstly, it can be
used for initial data capture; secondly, it is applied for
verification of existing GIS. Since the manual revision of GIS
data is very costly and time consuming, procedures are required,
which enable a fully automated verification of existing GIS
objects. Figure 14 shows an existing map of scale 1:5000 and
the buildings classified by our approach, which are represented
by the black polygons. At the upper left of the area, differences
between the map and the result of the classification, which
represents the actual situation, are clearly visible. Even though
the result of the classification might not be detailed and precise
enough especially for the automatic reconstruction of buildings,
the automatic detection of inconsistencies can be applied to
guide an operator-based revision of the GIS data.
Fig. 14. Comparison of existing map using classification result.
The second application we have in mind is the initial mapping
of urban vegetation like tree regions. Single trees are usually not
represented in standard GIS. However, they are required for
tasks aiming at visualization or simulation in urban
environments. For this type of applications, our classification
result can be favorably exploited.
The paper demonstrated the benefit of combining laser data and
colour aerial imagery for automatic scene labelling in an urban
environment. The main advantage of our approach is that the
problem of dealing with the mutual complementarity of the
different data sources is solved implicitly by combining height
and colour information in a classification step. The mutual
weighting of the different data is represented by the cluster
centres and the distances of the feature vectors to these cluster
centres. Within a supervised classification, these parameters are
provided for each channel and object class by the analysis of the
training areas.
Due to atmospheric effects, different spectral diffusion
depending on the sunlight, different spectral characteristics of
vegetation depending on season or soil, etc., new training areas
have to be digitized for each dataset, if multispectral data is