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Pattern Classification of Multi-Spectral Satellite Images based on Fusion of Fuzzy Algorithms  

Jeon, Young-Joon (동의대학교 컴퓨터공학과)
Kim, Jin-Il (동의대학교 컴퓨터공학과)
Publication Information
Journal of KIISE:Software and Applications / v.32, no.7, 2005 , pp. 674-682 More about this Journal
Abstract
This paper proposes classification of multi-spectral satellite image based on fusion of fuzzy G-K (Gustafson-Kessel) algorithm and PCM algorithm. The suggested algorithm establishes the initial cluster centers by selecting training data from each category, and then executes the fuzzy G-K algorithm. PCM algorithm perform using classification result of the fuzzy G-K algorithm. The classification categories are allocated to the corresponding category when the results of classification by fuzzy G-K algorithm and PCM algorithm belong to the same category. If the classification result of two algorithms belongs to the different category, the pixels are allocated by Bayesian maximum likelihood algorithm. Bayesian maximum likelihood algorithm uses the data from the interior of the average intracluster distance. The information of the pixels within the average intracluster distance has a positive normal distribution. It improves classification result by giving a positive effect in Bayesian maximum likelihood algorithm. The proposed method is applied to IKONOS and Landsat TM remote sensing satellite image for the test. As a result, the overall accuracy showed a better outcome than individual Fuzzy G-K algorithm and PCM algorithm or the conventional maximum likelihood classification algorithm.
Keywords
fuzzy Gustafson-Kessel algorithm; PCM algorithm; Bayesian maximum likelihood algorithm; remote sensing satellite image;
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