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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
Figure 6: Scheme for generating the Laplacian box. Upper
row: generated Gaussian pyramid, lower row: generated
Laplacian pyramid.
The resampling factor ki for the Gaussian pyramid levels i
generated before is k{ = 2*. Because we are interested
in a uniform size of all generated levels of the Laplacian
pyramid, our resampling factor differs to the factor used in
(Burt and Adelson, 1983). Due to the uniform size of the
generated images in the different levels, we call this stack
Laplacian box.
4.4 Normalization for generation a multichannel im
In section 4.3 we discussed the use of the scale space in
order to obtain a rich image description. Our next step is
to specify this predicate. In the segmentation process we
use all generated resolution levels in parallel. Combining
all levels U (0 < i < N) of the Laplacian box of all texture
parameters, we get a multichannel image, thus we actually
stack the three Laplacian boxes.
In order to be able to fuse the different channels of the
Laplacian box, we need to normalize these channels.
For the normalization, we use the expected noise behavior
of the filter kernels of the Laplace box, which we determine
by analyzing the impulse response, based on the linearity
of the generation process:
If a filter h{r.c) is applied to an image g(r,c) with white
noise n(r,c) ~ N(0, sulting image g'{r,c) = h(r, c) * g(r, c) is given by:
°n' = O’r
Therefore the influence factor of the filter operation is the to
tal of the squares of the filter coefficients. This corresponds
to the proposal of (Ballard and Rao, 1994) who take the
total energy of the filters. For our specific case, the analy
sis of the impulse response of the levels U of the Laplacian
box, generated using the binomial mask B A (Jàhne, 1989),
we get the normalization factor /¿:
/»■=-?-=£ h 2 { (r,c)
The normalization factors only depend on the used filter
mask. Fig. 7 shows all channels of the feature space for
the texture edge extraction.
Figure 7: Feature space for texture edge extraction. From
top to down the aerial image with strength, anisotropy and
direction of the texture respectively, and from left to right
the levels of the Laplacian box for each feature.
The final task is the extraction of texture edges.
5.1 Edge detection
We use the feature extraction program FEX to extract the
texture edges. This program analyzes the local autocovari
ance function of a multichannel image g using the negative
Hessian Tg, in our specific case T(SCAF). Using FEX
for edge detection, results in texture edges. These edges
separate neighboring textured areas depend on the user-
selectable parameters of FEX (resolution scale, scale for
lines and a significance level for internal statistical tests).
Altogether, we need to specify five parameters:
1. The differentiation scale si, needed for determining the
texture properties at the highest resolution.