293
4.2 Result analysis
From the processing result above, it could be seen that the
segmentation algorithm proposed in this paper is feasible.
However, the results also have some problems. There are many
small scraps in some region of segmentation, which should be a
whole cluster region. It may be caused by the following points
that are still in improvement.
The first problem is to choose suitable value for displacement d.
Especially for different kind of images, How to give a suitable
value for displacement d is still a difficult thing.
The second problem is to calculate the initial value for
clustering. The initial value of clustering is vital to the
segmentation. However, it is difficult to find a common method
to give initial value for different images.
The third problem is to optimize the weights between texture
features. For example, the mean of energy is 0.000137 while
the mean of contrast is 1766.045. Therefore, it is necessary to
produce suitable weights for texture features automatically.
However, due to the difference of the images to be processed, it
is hard to find an effective method.
red by
ge. The
is the
5 CONCLUSIONS
By use of texture features, this algorithm proposed in this paper
can overcome the shortcoming of the traditional segmentation
methods. From the segmentation results of different original
images, it can be seen that texture features are useful in high
spatial resolution RS image processing.
REFERENCES
LIU L.F., 2003. Texture Analysis Methods Used In Remote
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Ming D.P., 2004. Remote sensing image segmentation
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HUANG X., 2006. Methods for Classification of the High
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Transform. Geomatics and Information Science of Wunan
University, 31(12): 1055-1058
Su J.Y., 2004. Semi-automatic Extraction Technique of
Residential Area in High Resolution Remote Sensing Image.
Geomatics and Information Science of Wunan University,
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Zhou F., 2001. Studies on texture analysis, Ph.D dissertation of
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