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Remote Sensing Technology and Application  2014, Vol. 29 Issue (2): 344-351    DOI: 10.11873j.issn.1004-0323.2014.2.0344
    
High-resolution Remote Sensing Classification Aided by the Auxiliary Data in High\|density Urban Area
Yu Qipeng,Zhang Xiaoxiang,Mei Dandan,Xu Pan
( Institute of Geographical Information Science and Engineering,Hohai University,Nanjing 210098,China)
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Abstract  

High-resolution remote sensing images and GIS ancillary datasets such as parcels are combined to perform land use/cover change mapping in the urban\|built areas.NAIP datasets,a novel high\|resolution aerial remote sensing images in The National Agriculture Imagery Program,are used in these works.After trial and error image segmentation pursuing for good processing results,an objcet\|oriented image classification framework based on decision tree rules,combined with the cadastral datasets as a secondary data,was built to improve high\|resolution remote sensing image classification on the high\|density urban areas.The classification accuracy  of object\|oriented remote sensing image classification combined with geographic auxiliary data are better than only using the remote sensing images.Experiments studies showed that roads,building and others are excellently extracted.Comparing with the conventional object\|oriented classification,the overall classification accuracy of this novel methodology increased from 84.08% to 89.79%.Such a result reveals that auxiliary data can effectively improve the accuracy of high\|resolution remote sensing image classification.

Key words:  High resolution remote sensing      Image segmentation      Image classification      Object-oriented      Ancillary data     
Received:  01 April 2013      Published:  14 May 2014
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Yu Qipeng
Zhang Xiaoxiang
Mei Dandan
Xu Pan

Cite this article: 

Yu Qipeng,Zhang Xiaoxiang,Mei Dandan,Xu Pan. High-resolution Remote Sensing Classification Aided by the Auxiliary Data in High\|density Urban Area. Remote Sensing Technology and Application, 2014, 29(2): 344-351.

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http://www.rsta.ac.cn/EN/10.11873j.issn.1004-0323.2014.2.0344     OR     http://www.rsta.ac.cn/EN/Y2014/V29/I2/344

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