Abstract
This paper proposes a more effective supervised classification algorithm of remote sensing satellite image that uses the average fuzzy intracluster distance within the Bayesian algorithm. The suggested algorithm establishes the initial cluster centers by selecting training samples from each category. It executes the extended fuzzy c-means which calculates the average fuzzy intracluster distance for each cluster. The membership value is updated by the average intracluster distance and all the pixels are classified. The average intracluster distance is the average value of the distance from each data to its corresponding cluster center, and is proportional to the size and density of the cluster. The Bayesian classification algorithm is performed after obtaining the prior probability calculated by using the information of average intracluster distance of each category. While the data from the interior of the average intracluster distance is classified by fuzzy algorithm, the data from the exterior of intracluster is classified by Bayesian classification algorithm. The testing of the proposed algorithm by applying it to the multispectral remote sensing satellite image resulted in showing more accurate classification than that of the conventional maximum likelihood classification algorithm.
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© 2004 Springer-Verlag Berlin Heidelberg
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Jeon, YJ., Choi, JG., Kim, JI. (2004). A Study on Supervised Classification of Remote Sensing Satellite Image by Bayesian Algorithm Using Average Fuzzy Intracluster Distance. In: Klette, R., Žunić, J. (eds) Combinatorial Image Analysis. IWCIA 2004. Lecture Notes in Computer Science, vol 3322. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30503-3_44
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DOI: https://doi.org/10.1007/978-3-540-30503-3_44
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