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

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

116 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)2527-2540
Journal / PublicationPattern Recognition
Volume42
Issue number11
Publication statusPublished - Nov 2009
Externally publishedYes

Abstract

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 affected by the data distributing and the stochastic initializing the centrals of clustering. After the probability statistics of original data, the weights of data attribute are designed to adjust original samples to the uniform distribution, and added in the process of cyclic iteration, which could be suitable for the character of fuzzy C-means algorithm so as to improve the precision. Furthermore, appropriate initial clustering centers adjacent to the actual final clustering centers can be found by the proposed single point adjustment method, which could promote the convergence speed of the overall iterative process and drastically reduce the calculation time. Otherwise, the modified algorithm is updated from multidimensional data analysis to color images clustering. Moreover, with the comparison experiments of the UCI data sets, public Berkeley segmentation dataset and the actual remote sensing data, the real validity of proposed algorithm is proved. © 2009 Elsevier Ltd. All rights reserved.

Research Area(s)

  • Attribute weights, Center initialization, Clustering, Fuzzy C-means, Image segmentation