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 journalArticle

33 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 1



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

ASJC Scopus subject areas

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

Cite this

Kim, S. R., Lee, W-K., Kwak, D. A., Biging, G. S., Gong, P., Lee, J. H., & Cho, H. K. (2011). Forest cover classification by optimal segmentation of high resolution satellite imagery. Sensors, 11(2), 1943-1958.