Full text: Mapping without the sun

vegetable fields 
housing estate 
Table 2. Accuracy evaluation by confusion matrix 
for image classified by improved fuzzy c-mean classifier 
The same accuracy evaluation was finished for the image 
classified by ERDAS, the overall accuracy is 79.2%.The test 
shows that the fuzzy c-mean classifier is a high precision 
method for image classification. 
In order to solve the remote sensing image problem of 
uncertainty and mixing pixel, improved fuzzy c-mean 
classifier of soft classification method which adopted 
Mahalanobis distance was introduced in this paper. 
Mahalanobis distance scale suits to the real 
conditional of pixels distributed can detect 
characteristic space for data muster of super ellipse structure. 
The using of it is advancement relative to the fuzzy c-mean 
classifier which use the Euclidean distance. 
After interpreted the theory of improved fuzzy c-mean 
classifier, improved fuzzy c-mean classifier was constructed 
to classify a image, at last Confusion matrix was introduced to 
assess the accuracy of classified image, then comparation was 
made between the soft classified image and the one classified 
by ERDAS, the test proved that the land cover classification 
by improved fuzzy c-mean classifier has very high accuracy. 
The method also have disadvantages, it relies on the original 
clustering center awfully because the aim function is 
degressive during iteration. So if the original center departs 
from the idea one, the fuzzy c-mean method will get into a 
minimum answer locally, but the whole optimum answer. 
Genetic Algorithm which is overall optimum method can 
be applied in classifying in the future to avoid that problem. 
Besides, it has large calculation amount and can spend much 
time, all those problem should be studied ulteriorly. 
3. Huang Ning, Zhu Minhui, Zhang hourong,2002. 
Considering neighborhood information in image fuzzy 
clustering..Journal of electronics,July,pp.56-59. 
4. Friedl M A,Brodeley C E. Decision Tree Classification of 
Land Cover from Remotely Sensed Data [J], Remote Sense 
Environ. 1997,(61):399-409. 
5. Gao Xinbo,Pei Jihong,Xie Weixin,2000.study of weighted 
index m in fuzzy c-mean algorithm. ACTA ELECTRONIC A 
SINICA,Vol.28,April pp.98-102. 
6. Hasbaga, Ma Jianwen,Li Qiqing, Liu Zhili,Han 
Xiuzhen,2004. fuzzy c mean improving and comparing the 
clustering of satellite image.Computer Engineering, vol.30 
Nol 1,June,pp.37-41. 
7. Foschi P G,Smith D K,1997. Detecting Sub-pixel Woody 
Vegetation in Digital Imagery Using Two Artificial 
intelligence Approaches. Photogrammetric Engineering & 
Remote Sensing,63(5):493-500. 
8. P. Gong, J. D. Marceau, and P. J. Howarth,1992. A 
comparison of spatial feature extraction algorithms for 
land-use classification with SPOTHRD data. Remote Sensing 
Environment,40:13 7-151. 
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) ”. 
1. Wang Juan, 2007.Study about the Recognition Method of 
Land Cover with Remote Sensing Image Based on Fractal 
Texture. Liaoning Technical University,FuXin. 
2. I. Nedeljkovic,2004.image classification based on fuzzy 
logic . The International Archives of the Photogrammetry, 
Remote Sensing and Spatial Information Sciences, Vol. 34, 

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