technique.
Our programmes were designed in C language, and
ran on MC-6700. It took 12 minutes to segment the
512-by-512 pixel image into 21 classifications using
K-means algorithm. Based on that, it took 6 minutes to
merge 21 classifications into 7 categories using the spec-
tral and textural knowledge base.
The results above support the following conclusions:
1. It is feasible to segment an image first using an
unsupervised classification algorithm, then to discriminate
the category of every segmentation region using a know-
ledge base. It is of fast computation speed and high
classification accuracy.
2. The method presented in this paper not only can
segment an image into regions of same properties, but al-
so can discriminate the categories of the regions
automatically.
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(c) The image classified into 21
classifications by k — means.
(f) Merging the 7 classifitions of Fig.
classifications by k — means. 2(d) into 4 categories such as
drttlement place, forest, water area
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Fig.2 A Part of experimental results.