Forest cover classification by optimal segmentation of high resolution satellite imagery

So Ra Kim, Woo Kyun Lee, Doo Ahn Kwak, Greg S. Biging, Peng Gong, Jun Hak Lee, Hyun Kook Cho

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


This study investigated whether high-resolution satellite imagery is suitable for preparing a detailed digital forest cover map that discriminates forest cover at the tree species level. First, we tried to find an optimal process for segmenting the high-resolution images using a region-growing method with the scale, color and shape factors in Definiens® Professional 5.0. The image was classified by a traditional, pixel-based, maximum likelihood classification approach using the spectral information of the pixels. The pixels in each segment were reclassified using a segment-based classification (SBC) with a majority rule. Segmentation with strongly weighted color was less sensitive to the scale parameter and led to optimal forest cover segmentation and classification. The pixel-based classification (PBC) suffered from the "salt-and-pepper effect" and performed poorly in the classification of forest cover types, whereas the SBC helped to attenuate the effect and notably improved the classification accuracy. As a whole, SBC proved to be more suitable for classifying and delineating forest cover using high-resolution satellite images.

Original languageEnglish
Pages (from-to)1943-1958
Number of pages16
Issue number2
Publication statusPublished - 2011 Feb


  • Digital forest cover map
  • High resolution
  • Pixel-based classification
  • Satellite image
  • Segment-based classification

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Atomic and Molecular Physics, and Optics
  • Instrumentation
  • Electrical and Electronic Engineering


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