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An Adaptive Weighted Fuzzy C-means Clustering Algorithm for Remote Sensing Image Classification |
Wenping Liu, Tianyi Cui, Chih-Cheng Hung, Shihong Chen |
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Abstract The Fuzzy C-means (FCM) clustering algorithm is a well-known tool
for pattern and image classification, while FCM is showing unstable
behaviors with different fuzzy indexes. The New Weighted Fuzzy
C-means (NW-FCM) algorithm, based on the weighted mean concept, was
proposed to improve the performance of FCM, however, NW-FCM needs
man-machine interaction and can not classify by itself. In this
paper, an Adaptive Weighted Fuzzy Clustering Algorithm (AWFCM),
which integrates the weighted mean concept with the mechanism of
splitting and lumping from the Iterative Self-organizing Data
Analysis Techniques Algorithm (ISODATA), is proposed for remote
sensing image classification. At the same time, a statistical method
is introduced into the algorithm. The AWFCM does not need a priori
knowledge about the number of clusters and their centers, and can
automatically estimate an initial number of clusters and their
centers, also the optimum final number of clusters. Experimental
results demonstrate that the AWFCM has better performance than those
of the K-means, ISODATA and FCM algorithms.
DOI: 10.12733/jics20101678
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