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Remote Sensing Technology and Application  2012, Vol. 27 Issue (5): 706-711    DOI: 10.11873/j.issn.1004-0323.2012.5.706
    
Extraction of High Spatial Resolution Remote Sensing Image Classification based on PCA and Multi-scale Texture Feature
Liu Youshan1,2,Lv Chengwen1,2,Zhu Fengxia1,2,Gao Chao1,2
(1.College of Territorial Resources and Tourism,Anhui Normal University,Wuhu 241003,China;
2.Anhui Engineering Technology Research Center of Resources Environment and GIS,Wuhu 241003,China)
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Abstract  

The types of urban ground objects and their spatial distribution are complex.And the ground objects are multi-scale,different types of urban ground objects have different texture scale.The paper uses Principal Component Analysis(PCA) to deal with high\|resolution remote sensing images in order to reduce the quantity of data,suppress the noise,and highlight important information.On this basis,this paper extracts the texture features from the first principal component of PCA on basis of Gray Level Co-occurrence Matrix,and chooses the best combination of multi-scale textures to decision tree classification.The results show that the method of the decision tree classification based on PCA and multi-scale texture can extract the types of ground objects effectively.The precision of classification is 82.4%and Kappa coefficient is 0.78.

Key words:  Principal Component Analysis(PCA)      Multi-scale texture      High spatial resolution     
Received:  21 October 2011      Published:  17 October 2012
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Cite this article: 

Liu Youshan,Lv Chengwen,Zhu Fengxia,Gao Chao. Extraction of High Spatial Resolution Remote Sensing Image Classification based on PCA and Multi-scale Texture Feature. Remote Sensing Technology and Application, 2012, 27(5): 706-711.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2012.5.706     OR     http://www.rsta.ac.cn/EN/Y2012/V27/I5/706

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