Neuro-fuzzy classifier for land cover classification

Sang Gu Lee*, Jong Gyu Han, Kwang Hoon Chi, Jae Young Suh, Hee Hyol Lee, Michio Miyazaki, Kageo Akizuki

*Corresponding author for this work

Research output: Contribution to conferencePaperpeer-review

13 Citations (Scopus)


In this paper, we present a neuro-fuzzy classifier derived from the generic model of a 3-layer fuzzy perceptron and implement a classification software system for land cover classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. We use the image of Daeduk Science Complex Town which is obtained by AMS (Airborne Multispectral Scanner). The results show that the mixed composition areas such as 'bare soil', 'dried grass' and 'coniferous tree' are classified more accurately in the proposed method. This system can be used to classify the mixed composition area like the natural environment of the Korean peninsula. This classifier is superior in suppression of the classification errors for mixtures of land cover signatures.

Original languageEnglish
PagesII-1063 - II-1068
Publication statusPublished - 1999 Dec 1
Externally publishedYes
EventProceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 - Seoul, South Korea
Duration: 1999 Aug 221999 Aug 25


OtherProceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99
CitySeoul, South Korea

ASJC Scopus subject areas

  • Software
  • Theoretical Computer Science
  • Artificial Intelligence
  • Applied Mathematics


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