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Mapping without the sun
Zhang, Jixian

reserved in the optical edge image; otherwise, the centred edge
pixel will be eliminated from the original edge image.
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Apply the proposed method to the two original remote sensing
images shown in Section 2. The segmentation algorithm based
on MRF is realized using simulated annealing method (Chee
Sun Won, 1987) on the assumption that the SAR image can be
described by Rayleigh distribution model.
P&i I x.) =
y t
T ex P
where, y. is the gray-level value of the pixel, x. is the marker
of segment result, and
{ 0 y. is not the target
1 y. is the target
which can be estimated with the mean value ju R (x.) and the
standard variance a R (x.) of the sampled area from each
category in the original image by the following two expressions.
) = v(x i )
2 4 — 2T 2
c R ( x i) = cr (x,)
In the simulated annealing algorithm, it is very important to
choose an appropriate annealing schedule. In the application of
image segmentation, either an exponential format or an
logarithmic format is often used. In our experiment, the
exponential format is adopted for the annealing schedule, i.e.
□ rz
Figure 3 First-order Neighbourhood System
and Its Associated Clique Types
Figure 4 is the segment result of applied MRF-based algorithm
to the original SAR image shown in Figure 1 (a). It can be seen
that a lot of regions whose weak backscattering coefficient cr
is low have been extracted out, and the runway area take on
good connectivity except for two holes existing within the left
runway which are caused by two airplanes in the original image.
Figure 4 Segment Result
Figure 6 ROI Area Figure 7 Modified ROI Area
T = T„a‘ (5)
where, T Q is the initial temperature which has been set to be 4
for the experiment here, a is the temperature descending index
which should be a constant within (0, 1), and k is the iteration
times. In the experiment, a is set to be 0.9 which is experiential
according to the reference (S. Geman, 1984). And the
neighbourhood system and its corresponding cliques being used
here is shown in Figure 3.
Figure 5 is the edge image of Figure 4 obtained by Roberts
detector, since the latter is an binary image. Figure 6 is the
result after removing the irrelevant segmented regions and
keeping only the runway region according to the geometric
characteristics of the typical man-made line-type target as
discussed in Section 3. Hough transform is utilized here to
extract the region whose edge is characterized by straight and
long lines and eliminate the others which fail this requirement.
One can see that the two holes still exist within the runway.
Filtering the image with morphological operator (opening and
then closing) can fill the holes and get Figure 7 with better
The fusion result of the ROI boundary image and the optical
Canny edge image (Figure 8) is shown in Figure 9. Compared
with Figure 8, the fake edges existing within the right runway