Abstract
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 language | English |
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| Pages | II-1063 - II-1068 |
| Publication status | Published - 1999 Dec 1 |
| Externally published | Yes |
| Event | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 - Seoul, South Korea Duration: 1999 Aug 22 → 1999 Aug 25 |
Other
| Other | Proceedings of the 1999 IEEE International Fuzzy Systems Conference, FUZZ-IEEE'99 |
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| City | Seoul, South Korea |
| Period | 99/8/22 → 99/8/25 |
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence
- Applied Mathematics