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Mapping without the sun
Zhang, Jixian

Section 3. And relevant image processing technologies are
introduced in Section 4. Experiments are carried out on the
given images and conclusions drawn from the results are
presented in Section 5.
Figure 1 (a) and (b) are a SAR image and an optical image
which have been registered, respectively. The imaging region is
an airport, so the typical line-type target to be detected is the
runway here. One can obviously see that the optical image fit
the human vision well. The image is smooth as a whole. The
runway is comparatively bright in the optical image because of
its material. However, some route signs (seen in the top right
comer of the image) on the runway and some trails (seen in the
middle of the right runway) which are caused by abrasion or
erode or some other reasons will present evident edges within
the runway area and baffle the location and detection of the
runway edge pixels. While, in the SAR image, those signs and
trails “disappear”, because objects made of the same material
has almost the same backscattering coefficient G for
microwave. However, the speckle noise is evident in the whole
image. It can be seen in the area between the two runways. (It
also exists within the runway, but it is too dark to be seen there.)
This phenomenon is a big challenge to SAR image processing.
Basing on the above analysis, a detection method for line-type
targets using the SAR and the optical image fusion is proposed
in this paper. The processing flow of this method in shown in
Figure 2, and the denotation * indicates that the image is a
binary image. The detailed procedures are as follows.
(1) apply Canny detector (Canny [1986]) to the optical image in
order to extract as much edge information as possible;
meanwhile, adopt the segmentation algorithm (Robert A., 1998)
based on Markov Random Field (MRF) to obtain the regions
with weak backscattering coefficient in the SAR image; Canny
edge detector is one type of edge detector though the linear
smoothing filter which has strong resistance of Gaussian noise
exiting in most optical images. MRF-based segmentation
algorithm introduces the image intensity as well as the structure
information into the processing, thus it can obtain appealing
results when applied to SAR image with the speckle noise
which is supposed to be multiplicative existing.
based on MRF
Roberts edge
Figure 2 Processing Flowchart
(2) further extract ROI out of the segmentation result according
to the geometric characteristic of runway areas;
Typical man-made targets, such as highways and airport
runways, have distinct geometric characteristic in the image
which will favour the detection of this type of targets. They are
always very straight lines and their comers are usually obtuse
angles or right angles. Since Hough transform (Hough [1962])
is commonly used in straight line detection, it is applied to the
edge image of the segmentation result which is obtained by
Roberts detector. The edge image is binary in xy-plane (row
and column). Here a brief introduction of Hough transform is
given. More detailed information can be found in some relevant
references or books. The essence of the Hough transform is
that p = x cos 0 + y sin 0 is used as a normal representation of a
line. Its computational attractiveness arises from sub-dividing
the pO parameter space into so-called accumulator cells. At the
end of this transform, the image is transferred to pO -plane, and
the value Q in the position (p. , 6 j ) means that Q points in the
xy-plane lie on the line p. = x cos + y sin 6.. Therefore, the
long and straight lines represent themselves by the strong points
with big value in p6 -plane. Detect the peak points in pQ -
plane can find out which edge is characterized by long and
straight lines. Then, segmented regions with these detected
edges will retained and the others will be discarded. And the
ROI image is obtained.
(3) filter the ROI image (which is binary) with morphological
operator (opening and closing) in order to fill the holes that are
caused by the airplanes whose backscattering coefficient is
fairly strong within the runway area;
Opening operation can smooth bigger objects and discard
smaller objects. While closing operation is able to fill tiny holes
internal to the object and connect adjacent objects.
(4) apply the simpler Roberts detector to obtain the edge of the
modified ROI image;
Roberts detector is one of the oldest edge detectors in digital
image processing, and it is also the simplest. Since a binary
image is to be processed here, this detector is the most
economical choice in terms of processing speed and simplicity.
(5) eliminate irrelevant pixels in the optical edge image with the
ROI edge of SAR image as the reference in order to get the
final detection result. The fusion rule here is that search the
neighbourhood in the ROI boundary image whose centre is the
edge pixel in the optical edge image; if there is the boundary
pixel in the neighbourhood, the centred edge pixel will be