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Although the segments obtained will tend to stay compact (con
trary to regions gained e.g. by spectral clustering where no spatial
information is used) they do not have to be connected. The root of
a subtree at a high level covers a large area within the image and
can thus connect regions, which are neither close to each other
nor connected, but have similiar spectral properties. Another im
portant feature is that the number of segments is exclusivly based
on the given data. Neither an exact number has to be set, nor a
maximum number, because the number of segments is a direct
result of the algorithm.
As the proposed method was designed to segment homogeneous
areas, one of its limits is shown in Figure 4. It is not able to con
sider a heterogeneous image area like regions with strong tex
ture as one segment. Such a region (e.g. cities or forests with
great fluctuations in height and/or changes in backscatter proper
ties due to different vegetation) will be segmented in many small
regions. Future work will include the analysis of all regions in
a more rigorous way than the above mentioned simple merging
algorithm to overcome this disadvantage.
Figure 4: top: original; middle: segmentation; bottom: segment
borders
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