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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
urban environment the classification results can be improved
considerably, if the normalized DSM representing the local
height above terrain is introduced as an additional database
during classification.

Fig. 8. Maximum likelihood classification based on CIR
imagery and laser data.
3.4. Further Improvement by Shadow Analysis
One problem, when classifying areas with rough surfaces and
steep slopes as they can occur in urban areas, is the change of
spectral reflectance due to the presence of shadow areas. In
order to avoid these problems, the shadow areas can be derived
automatically based on the given DSM and used as an
additional source of information during the classification
process. The result of the automatic generation of shadow areas
is given in Figure 9.
Fig. 9. Shadow areas as derived from DSM.
For the automatic detection of shadow areas, the local height,
which is provided by the laser DSM as well as the elevation and
azimuth of the sun at the time of image acquisition is required.
The elevation and azimuth of the sun can either be determined
manually, by an interactive measurement of an edge of a
shadowed area in the image and the corresponding object height
in the normalized DSM, or derived automatically from the
geographical latitude and longitude of the captured area and the
time of image acquisition.
The improvement of the classification when using predefined
shadow areas is demonstrated in Figures 11 and 12, using the
test area depicted in Figure 10. In Figure 11, the
misclassifications in shadow areas are clearly visible. These
misclassifications can be avoided, if the class shadow is
determined in advance based on the analysis of the given DSM
and excluded from further classification like it is demonstrated
in Figure 12.
Fig. 10. Ortho-image.
Not classified
Fig. 11. Classification without using shadow areas.
In order to avoid the shadow class in the final result, the
approach can be further refined by splitting each of the landuse
classes into one separate class for shadow areas and one landuse
class for non-shadow areas. This leads to a significant
improvement of the results because the pixels in shadow areas
have completely different spectral characteristics than the ones