Pakzad, Kian
5.2 Initial Segmentation
The assumption that a biotope mapping exists does not apply for every area. For some areas an automatic initial
segmentation is necessary instead. A view on the aerial images of moorland shows that there are many spectrally
inhomogeneous areas. Because some moorland classes consist of inhomogeneous areas a simple multispectral
classification is not suitable for segmentation. The segmentation process is depicted in Fig.2. It requires CIR-aerial
images. In the case of grayscale images a biotope mapping or a manually created segmentation is necessary.
The most important information for the segment
borders in moorland are the streets and the main DM du 3
ditches. This information can usually be Streets Sp ed 7
Ditches Sedments
extracted from the GIS database, e.g. from aad
ATKIS Basis DLM. Based on this information an with high N ER n
initial segmentation is performed. The CIR Aerial Compute 1 | .NiDhwlus with high Segmented
knowledge exploited for the segmentation is the image s I Exact / So rep
same used in section 5.3. The next step is to Suse" aen zo
refine the initial segmentation: Inside every NVDI-density
segment the normalized difference vegetation
index (NDVI) is computed and the regions with
high density of vegetation index and also with
very low density were extracted. For every extracted subsegment the form parameter compactness is computed. Only
subsegments with high compactness and a minimum area size are accepted as valid regions for the final segmented
image. Fig.5 shows the result of this procedure for two parts of the test area.
Figure 2. Initial segmentation of moorland
5.3 Interpretation
This section describes the monotemporal interpretation of moorland from aerial images. As described in section 2 the
system we used for this work needs the prior
knowledge in the explicit representation form of
semantic nets. Therefore, the prior knowledge about
the relevant area is formulated in a concept net. Fig.3
shows a simplified version of this net.
moorland
|—part-of 9»
Scene moorsegment
Layer
be w? oe bs Based on section 4 we distinguish four states of
frost agriculturally area of re-/ area of peat moorland classes: forest, agriculturally used area,
used area degeneration extraction . .
= x FT re, area of re-/degeneration and area of peat extraction.
Sor ge $e tof eof 2 ;
high medium ET p The states area of degeneration and area of
vegetation i i . . . > ge .
po a tracks Vegetation regeneration are combined, because their distinction
density
rf
1n aerial images is very difficult. As shown in Fig.3 we
NE distinguish two layers of abstraction in the concept
wy || net: a scene layer and an aerial image layer. In the
scene layer the different states are described with their
[petes | obligatory parts. E.g. the state area of peat extraction
|| is characterized by harvester tracks and low vegetation
density. The state area of re-/degeneration is also
characterized by harvester tracks in one part, but also
Figure 3. Concept net for the interpretation of moorland by mid vegetation density. The nodes in the second
layer, the aerial image layer, describe the depiction of
the scene layer nodes in CIR aerial images and their
properties. The nodes describe the structures and colors to be looked for, if a state is to be assigned to a segment. Thus
both color and textural information are used. That’s why the concept net is suitable for both color and grayscale images
(see below). At the bottom of Fig.3 segment analysis operators are shown. Every node at the bottom of the aerial image
layer has access to a special operator.
mid NDVI-
density
E low dismembered
|] homogeneity structure
Segment analysis operators
The segment analysis operators have to verify the meaning of the node for a particular segment. This means that they
look inside a given segment and estimate whether the hypothesis of the node is correct or not. In this way the operators
transform the explicitly formulated hypothesis into image processing operations. Only on this level the interpretation
has direct access on the raster images. E.g. the operator connected with the node dismembered structure analyses the
structure of the edges in a particular segment. Short and curved lines lead to better assessment than long and straight
lines.
1106 International Archives of Photogrammetry and Remote Sensing. Vol. XXXIII, Part B7. Amsterdam 2000.
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