4. EXPERIMENT AND RESULT ANALYSIS 
Figure 10. Texture feature image of contrast 
Figure 11. Texture feature image of correlation 
4.1 Spectrum-feature-based classification 
The spectrum feature is the basic feature of the remote sensing 
image. The traditional image classification algorithms are 
based on the spectrum feature. The spectrum-feature-based 
maximum likelihood classification algorithm in the supervised 
classification is used in the paper. Classification result and 
precision statistics are as follows: 
Figure 14. The result of using the maximum likelihood 
classification method base on spectrum feature 
Class 
Prod.Acc. 
(Percent) 
User.Acc. 
(Percent) 
inhabited area 
86.31 
84.10 
Paddy field 
78.31 
85.20 
Terraced field 
83.45 
81.31 
Forest 
84.41 
82.21 
Bare land 
87.81 
85.81 
lakes 
88.33 
80.27 
Table 1. Precision statistics of the maximum likelihood 
classification 
precision=84.0789%, Kappa= 0.7828 
4.2 Texture feature assistance classification 
Figure 12. Texture feature image of entropy 
Figure 13. Texture feature image of second moment 
In figure 6~9 images are based on the 5x5 sliding window. In 
figure 10-13 images are based on the 7x7 sliding window.Seen 
from the charts, brightness of contrast image’s is bigger as well 
as the inhabited area appears quite obviously in this figure. But 
other terrain features are not very prominent depending on the 
visual observation in contrast image. The entropy image 
gradually becomes bigger with the window and texture features 
turns more abundant. 
For supervisor classification algorithm of texture features 
assistance classification, we have still used the maximum 
likelihood classification of supervised classification to compare 
conveniently. 
The image with 4 bands is formed by 3 bands spectrum feature 
image by adding a texture feature image. The texture feature 
images are obtained by 5x5 sliding window computing. 
We obtain four supervisor classification images using texture 
feature assistance spectrum feature classification. These are 
Figure 15. The result of classification using correlation 
texture feature image assistant spectrum feature 
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