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Author (up) Cao, X.; Chen, J.; Imura, H.; Higashi, O. url  doi
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  Title A SVM-based method to extract urban areas from DMSP-OLS and SPOT VGT data Type Journal Article
  Year 2009 Publication Remote Sensing of Environment Abbreviated Journal Remote Sensing of Environment  
  Volume 113 Issue 10 Pages 2205-2209  
  Keywords Remote Sensing  
  Abstract Mapping urban areas at regional and global scales has become an urgent task because of the increasing pressures from rapid urbanization and associated environmental problems. Satellite imaging of stable anthropogenic lights from DMSP-OLS provides an accurate, economical, and straightforward way to map the global distribution of urban areas. To address problems in the thresholding methods that use empirical strategies or manual trial-and-error procedures, we proposed a support vector machine (SVM)-based region-growing algorithm to semi-automatically extract urban areas from DMSP-OLS and SPOT NDVI data. Several simple criteria were used to select SVM training sets of urban and non-urban pixels, and an iterative classification and training procedure was adopted to identify the urban pixels through region growing. The new method was validated using the extents of 25 Chinese cities, as classified by Landsat ETM+ images, and then compared with two common thresholding methods. The results showed that the SVM-based algorithm could not only achieve comparable results to the local-optimized threshold method, but also avoid its tedious trial-and-error procedure, suggesting that the new method is an easy and simple alternative for extracting urban extent from DMSP-OLS and SPOT NDVI data.  
  Address  
  Corporate Author Thesis  
  Publisher Place of Publication Editor  
  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN 0034-4257 ISBN Medium  
  Area Expedition Conference  
  Notes Approved no  
  Call Number GFZ @ kyba @ Serial 2041  
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