International Archives of Photogrammetry and Remote Sensing, Vol. 32, Part 7-4-3 W6, Valladolid, Spain, 3-4 June, 1999 
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oblique looking bands. Furthermore, the urban/man-made 
construction class often appeared overestimated in the 
combined approach, which, in relation to the failure of the 
anisotropy approach to distinguish accurately the bare soil 
signature from the urban area signature, could lead to the 
conclusion that this phenomenon is a result of the synergy effect 
between multispectral and anisotropic information. 
Among the classes that were common in all three approaches, 
the forest types could be well recognised in all of them. An 
information that is missing in the anisotropy approach is the 
presence of grass at the forest edges, which is presented 
accurately by the other two approaches. 
The class "peas" was more accurately estimated by the MS and 
the combined approaches. Slight differences between the MS 
and the combined approaches could be noticed through a 
detailed examination of the whole classified scene. These 
differences concern the capability of the maximum likelihood 
classifier to classify all pixels of the same plot to the same class 
(cultivation). In this respect, better performance was observed 
by the combined approach. 
The overall accuracy of the combined and the MS approach 
classification was more or less at the same level, with the 
combined approach being slightly better. The accuracy of the 
anisotropy approach was lower than the other two, which was 
expected, since the other two approaches, apart from bands 1 
and 4, use part or most of the information that the anisotropy 
approach uses. 
5. DISCUSSION 
Due to time constraints, the investigations on the Diimast 
dataset could not be performed with the completeness and 
accuracy, which are requested for reliable conclusions. The 
rectification was performed without DEM. No atmospheric and 
terrain relief corrections were applied to the dataset. 
Additionally, the evaluation of the potential of the anisotropy 
approach was limited due to the unfavourable illumination-to- 
sensor geometry. In spite of these facts, the false colour 
composite of the stereo data (6/7, 6, 7) showed a surprisingly 
high information content. In any case, the results allow 
discussing some trends. 
The "anisot" FCC displays texture and structure elements more 
clearly, improving the detection of infrastructure and 
settlements. The detailed analysis showed partially different 
features in the two FCCs. 
The example of winter wheat indicates that during specific 
stages of crop development, stand structure and/or individual 
plant architecture are more universal object characteristics than 
the multispectral ones. 
The example of mowed meadows demonstrates a situation, 
well-known from the side-looking perspective of a walker, 
which is also valid for remote sensing data analysis, i.e. that a 
plot looks green despite a ground coverage of almost 5%. With 
the anisotropy approach, it seems that is possible to detect 
sparse vegetation in a very early development stage. 
A point that should be noticed in the anisotropy approach is the 
hot spot and the shadow effect. At object boundaries where also 
significant height differences are observed, a buffer zone 
appears, where pixels have a distinct radiometric behaviour, as a 
consequence of the particularity of the irradiation at these 
regions. The result is that these pixels are often misclassified. In 
this case, they were often classified as urban/man-made 
construction class. 
An analysis of the results of the multispectral versus the 
anisotropic evaluation, both the visual as well as the computer- 
based one, indicates that the classification is determined by 
different biophysical parameters. In case of vegetation, the 
multispectral approach explores mainly the absorption of 
incoming radiation by pigments and water, while the anisotropy 
information is due to stand structure and plant architecture, 
which are often an effect of phenologic and physiologic status. 
The hypothesis that multispectral and anisotropic information 
are complementary could be proven at least for the main 
landcover classes forest, agricultural areas, settlements, water 
bodies and infrastructure. 
The synergistic potential of the combined use of multispectral 
and anisotropic information could not be demonstrated as 
clearly as in the investigations described by Schneider et al. 
(1999). For the evaluated dataset, the increase in classification 
accuracy for the combined multispectral and stereo band 
analysis is not significant. The low difference between the 
backscatter signal of the two stereo bands should be the reason. 
Despite this result of the computer-based analysis, the results of 
the visual interpretation let us hope that a further substantial 
increase can be expected using common classification routines, 
which consider also textural and context information like the 
DELPHI2™ approach (deKok et al., 1999). 
For more detailed investigations on this topic, field 
measurements, approximating the BRDF, have to be performed, 
preferably simultaneously to stereo data acquisition from space 
or airborne sensors. 
Last but not least, it is worth noting, that the mode D band 
combination of MOMS-2P has been proven to lead to better 
results than the mode 3 data of the D2 mission. The concept of 
optimised spectral coverage in the visible range by substituting 
band 3 (red) by band 1 (blue) led to a real improvement, 
especially for visual interpretation. By combining the 
multispectral bands 1 and 4 with the panchromatic stereo bands, 
which provide the spectral information from blue to red, almost 
true colour images can be produced.