A Study on the Classification for Satellite Images using Hybrid Method


The KIPS Transactions:PartB , Vol. 11, No. 2, pp. 159-168, Apr. 2004
10.3745/KIPSTB.2004.11.2.159,   PDF Download:

Abstract

This paper presents hybrid classification method to improve the performance of satellite images classification by combining Bayesian maximum likelihood classifier, ISODATA clustering and fuzzy C-Means algorithm. In this paper, the training data of each class were generated by separating the spectral signature using ISODATA clustering. We can classify according to pixel´s membership grade followed by cluster center of fuzzy C-Means algorithm as the mean value of training data for each class. Bayesian maximum likelihood classifier is performed with prior probability by result of fuzzy C-Means classification. The results shows that proposed method could improve performance of classification method and also perform classification with no concern about spectral signature of the training data. The proposed method is applied to a Landsat TM satellite image for the verifying test.


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Cite this article
[IEEE Style]
J. Y. Jun and K. J. Il, "A Study on the Classification for Satellite Images using Hybrid Method," The KIPS Transactions:PartB , vol. 11, no. 2, pp. 159-168, 2004. DOI: 10.3745/KIPSTB.2004.11.2.159.

[ACM Style]
Jeon Yeong Jun and Kim Jin Il. 2004. A Study on the Classification for Satellite Images using Hybrid Method. The KIPS Transactions:PartB , 11, 2, (2004), 159-168. DOI: 10.3745/KIPSTB.2004.11.2.159.