104 
Figure 16. The result of classification using contrast 
texture feature image assistant spectrum feature 
Figure 17. The result of classification using second 
moment texture feature image assistant spectrum feature 
Figure 18. The result of classification using entropy 
texture feature image assistant spectrum feature 
Precision 
(Percent) 
Kappa 
The maximum likelihood 
classification based on spectrum 
feature 
84.0789 
0.782 
8 
The maximum likelihood 
classification of entropy image 
assistant 
92.7664 
0.906 
4 
The maximum likelihood 
classification of second moment 
image assistant 
92.5438 
0.893 
7 
The maximum likelihood 
classification of contrast image 
assistant 
93.2179 
0.919 
5 
The maximum likelihood 
classification of correlation 
image assistant 
91.9348 
0.893 
5 
Table 2. The statistic data of classification precision 
The classification result appraisal and compares: 
From above-mentioned experiment result, we can draw the 
following conclusions: 
1) when the texture features were employed in the process of 
image classification , the general classification accuracy 
reached 92.6%, without the texture feature auxiliary, the 
general classification accuracy was only 84.07%. 
2) Using above four kind of textures feature to participate in the 
classification, we found the inhabited area, the forest, the 
badlands and the paddies had improvements in classified effect. 
The contrast image assistance classification was used to 
improve on classification effects of the water system. Flowever, 
compared with original image, it still had the phenomenon of 
divides by mistake. Classification effects of other several kinds 
of texture feature images for water system were not good, 
which caused many divides by mistake. 
3) Looked through the effect of above several features images 
assistance classification, the clarity and the dividing degree had 
certain enhancement to some extent, we was allowed to see 
from the statistical result of classification that the precision and 
the kappa coefficient all has the certain improvement; But 
phenomenon of wrong dividing and leaks dividing still exist. 
5. CONCLUSIONS 
This article mainly studies the algorithm of texture feature 
image extraction based on the gray level co-occurrence matrix 
and the precision appraisal. The program of extraction texture 
feature image was realized by VC++. We extract four texture 
images by this program, that is, second moment image, contrast 
image, correlation image and entropy image. Next, we take 
these four texture feature as a band respectively to combine 
with three bands of spectrum feature, which will form a image 
with 4 bands and was finally classified by means of the 
algorithm of the maximum likelihood classification. Indicated 
through the experiment, supervisor classification method of 
texture feature assistance spectrum feature classification 
improves the precision classification to some extent, but 
phenomenon of wrong dividing and leaks dividing still exist. 
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