4 
On the other hand it could be assumed that a SVM performs 
better, because it achieves the highest results when classifying 
single-sensor data. This assumption is confirmed by the 
experimental results: The separate training of the SVM and the 
fusion by another SVM further increases the classification 
accuracy up to 76.5%. 
Classifier 
algorithm 
SAR 
TM 
SAR+TM 
MLC 
55.5 
66.0 
70.1 
DT 
46.0 
63.5 
64.0 
DT boost 
61.5 
67.7 
75.6 
SVM 
62.7 
68.2 
74.2 
SVM Fusion 
- 
- 
76.5 
Table 2. Overall accuracies [%], using different classifier 
algorithms and data sets 
The achieved overall accuracies clearly illustrate the value of 
using multisensor imagery for classification. Comparing the 
multisensor classification by SVM, the accuracy is increased by 
up to 6% compared to the results of the Landsat image and up 
to 11.5% compared to the classification accuracy achieved with 
the SAR imagery. The positive impact of multisensor imagery 
on the classifier performance is also confirmed by the class- 
specific accuracies (Table 3 and Table 4). 
Land cover 
Class 
Input data 
SAR 
TM 
Arable crops 
51.2 
56.2 
Cereals 
73.4 
75.0 
Forest 
74.6 
89.4 
Grassland 
71.0 
56.8 
Orchards 
54.8 
47.6 
Rapseed 
58.2 
73.4 
Root crops 
58.2 
66.0 
Urban 
60.2 
81.4 
Table 3. Class-specific accuracies, using single-source imagery, 
bold numbers indicates best results 
Land cover 
Class 
Classifier algorithm 
DT boost. SVM Fusion 
Arable crops 
70.6 
70.8 
Cereals 
77.8 
76.6 
Forest 
93.2 
91.2 
Grassland 
74.2 
72 
Orchards 
63.6 
68.6 
Rapseed 
76.0 
75.2 
Root crops 
66.4 
73.8 
Urban 
83.0 
84 
Table 4. Class-specific accuracies, using a boosted DT and 
SVM Fusion, bold numbers indicates best results 
On one hand the single-source results show that the two data 
sources are differently reliable. Whereas grassland and 
orchards are more accurately classified using the SAR data, the 
other classes are better described by the Landsat image. 
Moreover all class-specific accuracies are improved by the use 
of multisensor imagery. The accuracies of classes that achieve 
relatively low values on both image sources (e.g., arable crops, 
orchards) are significantly increased by the multisensor 
approach. 
The comparison of the class-specific accuracies by a boosted 
DT and the proposed fusion strategy demonstrate that the 
results from the SVM Fusion are more balanced (Table 4). 
Whereas the accuracies vary between 63.6% and 93.2% for the 
boosted DT, the accuracies achieved by the SVM Fusion vary 
between 68.6% and 91.2%. 
The visual assessment of the classification results underlines the 
positive effect of multisensor fusion and the complementary 
character of the multitemporal SAR and the multispectral 
optical data (Figure 2): The resulting maps seem more 
homogeneous in some regions and errors for critical classes are 
decreased. Contrary to the visibly good performance of the 
multisensor fusion, the disappearance of the highway in the 
middle of the map subset, which was recognized in the TM data, 
shows possible drawbacks of the multisensor approach. 
Figure 2. Classification results, using different data sets 
6. CONCLUSION 
The challenge of classifying multisensor data sets, containing 
multitemporal SAR and multispectral imagery has been 
addressed. In experiments, the separate training and further 
fusion by a SVM outperformed all other parametric and 
nonparametric classification techniques including a 
conventional SVM. 
We assume the 
concept to be 
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more adequate 
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transformation, 
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rule images. T! 
original imager 
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increased multii 
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multitemporal 
worthwhile. He 
increased by si 
applied classify 
approaches for 
ongoing researc 
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