Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation

In this paper, a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-means clustering algorithm is proposed based upon the prior information. This method can solve the fuzzy C-means algorithm's problem that the clustering quality is greatly affect...

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Veröffentlicht in:	Pattern recognition Jg. 42; H. 11; S. 2527 - 2540
Hauptverfasser:	Fan, Jianchao, Han, Min, Wang, Jun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ltd 01.11.2009 Elsevier
Schlagworte:	Applied sciences Attribute weights Center initialization Clustering Exact sciences and technology Fuzzy C-means Image processing Image segmentation Information, signal and communications theory Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Telecommunications and information theory Image segmentation Center initialization Fuzzy C-means Clustering Attribute weights Prior information Data analysis Automatic classification Image processing Updating Iterative method Signal classification Color image Remote sensing Computation time Uniform distribution Multidimensional analysis Fuzzy algorithm Convergence rate Non contact measurement
ISSN:	0031-3203, 1873-5142
Online-Zugang:	Volltext
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