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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
2. The integration scale h, needed for determining the
texture properties at the highest resolution.
3. The number m of the used pyramid levels.
4. The differentiation scale s 2 , needed for determining the
integrated squared gradient of the texture features.
5. The integration scale f 2) needed for determining the
integrated squared gradient of the texture features.
5.2 Choice of scale
One of the main and up to now unsolved problem is the
choice of suitable scales for texture edge extraction. This
is a general problem and to our knowledge has not been
solved satisfactorily. The reason is that textures may appear
at very different scales. Though these scales may be identi
fied by some automatic means it is not clear whether these
scales correspond to textures or macro structures which
the analyzing module wants to resolve. E. g. in the case
of the tile row 2 column 4 in Fig. 8 two interpretations are
possible: Either the tile is part of a textured region, where
the texture is quite regular and has long wave lengths, or
the tile may be interpreted as a composition of some few
homogeneous regions separated by lines.
That means, the user or the calling routine has to decide
which levels of the Laplacian box should be used to obtain
the required results. This strongly depends on the applica
tion. This may be a severe problem in case of textures of
very different scale.
In our work this step was done interactively.
This section demonstrates the properties of the new texture
edge extraction scheme.
In all cases we compare the result of the texture edge ex
traction with the grey level edge extraction from FEX.
The basic problem is the proper choice of the scale param
For the first investigations presented below, we used the
Sobel operators as differentiation kernel for the determina
tion of the texture parameters, thus fixed s\ = I/a/2-
6.1 Results from synthetic data
First, we present our results of the texture edge extraction
for synthetic data. The scales were selected such that the
feature extraction could rely on the intensity differences be
tween the tiles. Obviously, this was quite successful in this
case. The texture edges, however, are a bit cleaner.
6.2 Results from natural scenes
In this section, we show the potential of our algorithm for
texture edge extraction of natural scenes. The results are
shown in Fig. 9.
In order to show the difference between grey level and tex
ture edge extraction, and not to obtain intensity edges alone,
the integration scale t is chosen larger in the texture edge
Figure 8: Results of texture edge extraction: Upper row)
collage of Brodatz-textures; lower row left) using Intensity
alone (s = 5.0, t = 5.0); right) using the Laplacian box
(si — 0.7, t\ = 6.0, rn = 3, S2 = 5.0, t 2 — 5.0/
extraction scheme. This is reasonable, as we want to group
several furrows into one field. Obviously, this reasoning
leads to quite satisfying results. Due to our special imple
mentation, not all texture edges at the image borders are
6.3 Results from non-textured images
To achieve an improved method for image segmentation
we have to ensure, that our technique provides good re
sults not only for textured images, but also for non-textured
images. Therefore, we applied our approach to some non-
textured images too. The results are shown in Fig. 10.
The result is satisfactory. The spurious texture edges in the
background can be explained, as no thresholding is per
formed, in contrast to the procedure for grey level edge ex
This paper presented a filter based approach for texture
edge extraction using the scale characteristics of the local
autocovariance function.
The approach was implemented and tested on synthetic
and natural scenes and shows some promising results. One
of the main problems is the choice of suitable scales for tex
ture edge extraction. In our experiments this step was done
To perform a qualitative evaluation, we have to compare
our algorithm with other approaches, as shown (Shao and
Forstner, 1994). Also more detailed aspects need to be
analyzed, e. g. the effect of the higher levels of the Lapla
cian box on texture edge extraction and the ability to extract
edges at boundaries between textured and non-textured re