MXM. Here M is the size of the image blocks which are
divided in the following steps. It is because that texture feature
is a property of area. Another supposition is that any texture
area in image should include at least one foundational element
of the texture(Zhou, 2001).
At the first step, the initial segmentation image will be gotten
by the following process. First, the original image is divided
into many sub-block with size of M>
every sub-block is calculated by the equations above. Second, a
feasible clustering algorithm is chosen to classify them to
certain amount classes. And different classes are marked by
different gray level. Edges between different areas in initial
segmentation image are ladderlike. In order to get smooth and
rational edge, it is necessary to perform the following edge
fining algorithm. The algorithm, initial value and parameters of
clustering should be confirmed in this step.
At the second step, a feasible edge fining algorithm is proposed
to deal with the initial segmentation image. First, the boundary
sub-blocks are distinguished by their classes and locations.
Second, each boundary sub-block images are subdivided into
four lower sub-blocks with size of (M/2) X (M/2). Texture
feature of this level sub-block images are calculated. Finally,
the distances of this level sub-block image to its adjacent
classes are calculated. It will be marked as corresponding class
according to distances. Actually, boundary sub-blocks should
be altered to their neighbouring classes, which assured the
integrality of the area of the segmentation.
The algorithm is given as follows.
Step 1: To divide the image I into sub-block images with size
of M*M and calculate their texture features respectively.
Step 2: To classify the sub-block images into certain number of
classes using K-mean clustering algorithm. And then, pixels in
image are marked to corresponding classes according to their
texture feature. After that, the initial segmentation image S(0)
would be gotten.
Step 3: To distinguish the boundary blocks and divide them to
lower sub-blocks with size of (M/2) X (M/2). Texture feature of
this level sub-block images are calculated. These lower
sub-blocks are marked to corresponding class according to their
distances to the neighbouring classes. After that, the first fining
segmentation S(l) will be finished.
Step 4: If S(t)=S(t-l) or M/2
foundational element, then output the final segmentation image.
Otherwise go to Step 3.
4. EXPERIMENT RESULT
4.1 Experiment
First, the camera image is used for segmentation experiment.
The original image and the segmentation image are given in the
follows.
4.2 Resu
Figure 1. Original camera image
imm
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t ■ • ■ -
wf • * .
.. L .1
Figure 2. Segmentation image
Second, high spatial resolution RS image acquired by
unmanned aerial vehicle is given as the original image. The
figure 3 is the original image and the figure 4 is the
segmentation result.
Figure 3. Original high resolution RS image
Figure 4. Final segmentation
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REF
LIU L.F
Sensing
18(6):441
Ming D
algorithm
engineeri
HUANG
Spatial R
Transforr
Universit
Su J.Y.
Residenti
Geomatic
29(9):791
Zhou F.,
Peking U
