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A context-sensitive clustering technique based on graph-cut initialization and expectation-maximization algorithm
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DOI：10.1109/LGRS.2007.905119.png)
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Abstract
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This letter presents a multistage clustering technique for unsupervised classification that is based on the following: 1) a graph-cut procedure to produce initial segments that are made up of pixels with similar spatial and spectral properties; 2) a fuzzy c-means algorithm to group these segments into a fixed number of classes; 3) a proper implementation of the expectation-maximization (EM) algorithm to estimate the statistical parameters of classes on the basis of the initial seeds that are achieved at convergence by the fuzzy c-means algorithm; and 4) the Bayes rule for minimum error to perform the final classification on the basis of the distributions that are estimated with the EM algorithm. Experimental results confirm the effectiveness of the proposed technique.
Keywords:
clustering
expectation-maximization (EM) algorithm
remote sensing
segmentation
unsupervised classification
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