Volume 35 Issue 5
May  2009
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Liu Nana, Li Jingwen, Li Ninget al. Unsupervised classification approach based on graph-segment for multispectral remote sensing images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(5): 544-546. (in Chinese)
Citation: Liu Nana, Li Jingwen, Li Ninget al. Unsupervised classification approach based on graph-segment for multispectral remote sensing images[J]. Journal of Beijing University of Aeronautics and Astronautics, 2009, 35(5): 544-546. (in Chinese)

Unsupervised classification approach based on graph-segment for multispectral remote sensing images

  • Received Date: 10 Aug 2008
  • Publish Date: 31 May 2009
  • To solving the noisy points and high cost problems of pixel-based multispectral image classification, a hybrid unsupervised approach with graph-based segment and fuzzy c-means clustering was presented. First, based on the relationships among neighboring pixels, image was segmented into groups of sub-regions using the graph-based algorithm. Then according to the global feature vector of sub-region, the fuzzy c-means classifier was used to obtain the classification map. Experiments turn out that the proposed approach, which considers both relationships of neighboring pixels and global feature of sub-region, can achieve better accuracy and efficiency by comparing the result with pixel-based fuzzy c-means classification.

     

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