in the example, the test imag was divided into 3 kinds of
features: cropland, vegetable fields and housing estate by
visual interpretation. 3 kinds of samples were selected from
classified area,30 samples were selected for each kind. The
size of each sample is 80 X 80 pixels. During the classifying
process, whether the template size is too big or too samll, the
result image will be affected negatively, so the size is always
between 8X8 and 20 X 20. in this paper, 10X10 was selected
as the template.
The characteristics selected for classifying should
differentiate different feature primely. In order to select good
characters we have compared the test result, so in this paper
contrast, entropy and variance of histogram characteristics
were selected for classifying.
The average of the 3 characteristics of cropland, vegetable
fields and housing estate were calculated as the clustering
center, the average is listed in the table belows.
samples/ characteristics
contrast
entropy
Variance
Vegetable fields
1.7290
3.7256
4.0796
Housing estate
5.0049
6.3066
9.8611
cropland
0.3317
0.4124
0.5715
Table 1. average of characteristics of samples
2) Initializing: m is always between 1.5 and 5, if m<2, the
classification inclined to hard clustering, if m>3, the
classification is too smooth, so in this paper, 2.5 is
initialized to m.
3) Initializing membership array £/ <0) . ¿/0 is calculated
by formula (3), then membership array £/ (0) is
recalculated by and formula (5).
4) Image classification by the fuzzy classifier. Based on
those idea, program was finished to realized
classification based on Matlab.
5) Result image after classification was achieved by the rule
of maximum membership using fuzzy classification. In
order to remove the noise exit on the classified image,
median filter was adopted to the result image.The
original image and the classified image after filtering is
shows as figure 2 and figure 3.
Figure 2. original image
Figure 3. image classified by improved fuzzy c-mean
classifier.
Figure 4. image classified by ERDAS
The test was processed based on unsupervised classificaiton,
the classified image is only the dividing of three kinds of
features. The three kinds of features was ascertained through
priori knowledge after fuzzy clustering.In figure 3, he green is
vegetable fields, the blue is housing estate, the red is cropland.
We can see from image, different kinds of features were
recognized correctly, and the boundary of them is very clear.
4 ACCURACY EVALUATION FOR CLASSIFIED
IAMGE
Many standards were proposed to assess the accuracy for
classified image, among them, confusion matrix is accepted
widly, so confusion matrix was introduced in precision
evaluation in the paper. 3 indexs were calculated, The overall
accuracy represents the probability of consistence between
classified image and real field of classified feature types. The
producer’s accuracy represent the conditional probability of
consistence between a random selected point from real field
and correspondence one from classified image. The user’s
accuracy represents the conditional probability of consistence
between a random sample point selected from classified
image and correspondence point in real field. We selected
1,000 points randomly on the classified image, and compared
with the priori knowledge, then the statistical table was
achieved, it shows as tabel 2.
