Magenta 
residential area 
vegetable plot 
130.7625 
7 
3 
115.3787 
Blue 
residential area 
glebe 
5.0828 
2 
0 
0 
Cyan 
paddy field 
residential area 
61.8504 
4 
2 
55.5003 
Green 
glebe 
vegetable plot 
1041.4520 
9 
3 
698.4803 
I 
29437.7717 
312 
21 
1265.8236 
Table 2 the results of the change detection in land use 
From the above experiment results, we can know that the 
monitoring results of land use changing could not only rely on 
the analysis results of computer, and should integrate with 
manual optical interpretation. It is the better effective method 
for now to extract the land use changing information utilizing 
the computer automatic processing and the manual optical 
interpretation. 
5. CONCLUSION 
This paper utilizes the Maximum Likelihood Classification into 
the SPOT5 multi-spectral image, and obtains a better classified 
effect. The calculated total classification precision is 
86.20%,and the Kappa coefficient is 0.8275. According to the 
classification results, this paper researches a remote sensing 
changing detection method based on the land use vector graph. 
This method uses the vector frame attribute of the land 
utilization chart, meanwhile carries on a very good use of its 
category attribute. Additionally this method also avoids the 
effects caused by the different sensors of satellites, the 
incidence angles in imaging, and so on. And this method fully 
excavates the various attributes of the land utilization vector 
chart, then participates the category attributes of land utilization 
chart into the change detection, and obtains the comparatively 
high accuracy monitor results. 
ACKNOWLEDGEMENTS 
Our research project was supported by the “Excellent Youth 
Research Fund Program of Liaoning Technical University 
(N0.O6AOI)”, and the “Open Research Fund Program of the 
Geomatics and Applications Laboratory of Liaoning Technical 
University (No. 2006008)”. 
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