Land Surface Classification With Airborne Multi-Spectral Scanner Image Using A Neuro-Fuzzy Model 


Vol. 9,  No. 5, pp. 939-944, Oct.  2002
10.3745/KIPSTD.2002.9.5.939


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  Abstract

In this paper, we propose and apply new classification method to the remotely sensed image acquired from airborne multi-spectral scanner. This is a neuro-fuzzy image classifier derived from the generic model of a 3-layer fuzzy perceptron. We implement a classification software system with the proposed method for land cover image classification. Comparisons with the proposed and maximum-likelihood classifiers are also presented. The results show that the neuro-fuzzy classification method classifies more accurately than the maximum likelihood method. In comparing the maximum-likelihood classification map with the neuro-fuzzy classification map, it is apparent that there is more different as amount as 7.96% in the overall accuracy. Most of the differences are in the "Building" and "Pine tree", for which the neuro-fuzzy classifier was considerably more accurate. However, the "Bare soil" is classified more correctly with the maximum-likelihood classifier rather than the neuro-fuzzy classifier.

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  Cite this article

[IEEE Style]

J. G. Han, K. H. Ryu, Y. K. Yeon, K. H. Chi, "Land Surface Classification With Airborne Multi-Spectral Scanner Image Using A Neuro-Fuzzy Model," The KIPS Transactions:PartD, vol. 9, no. 5, pp. 939-944, 2002. DOI: 10.3745/KIPSTD.2002.9.5.939.

[ACM Style]

Jong Gyu Han, Keun Ho Ryu, Yeon Kwang Yeon, and Kwang Hoon Chi. 2002. Land Surface Classification With Airborne Multi-Spectral Scanner Image Using A Neuro-Fuzzy Model. The KIPS Transactions:PartD, 9, 5, (2002), 939-944. DOI: 10.3745/KIPSTD.2002.9.5.939.

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