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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Published in:	Pattern recognition Vol. 42; no. 11; pp. 2527 - 2540
Main Authors:	Fan, Jianchao, Han, Min, Wang, Jun
Format:	Journal Article
Language:	English
Published:	Kidlington Elsevier Ltd 01.11.2009 Elsevier
Subjects:	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
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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.
AbstractList	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.
Author	Han, Min Wang, Jun Fan, Jianchao
Author_xml	– sequence: 1 givenname: Jianchao surname: Fan fullname: Fan, Jianchao email: fjchao@student.dlut.edu.cn organization: School of Electronic and Information Engineering, Dalian University of Technology, Dalian 116023, PR China – sequence: 2 givenname: Min surname: Han fullname: Han, Min email: minhan@dlut.edu.cn organization: School of Electronic and Information Engineering, Dalian University of Technology, Dalian 116023, PR China – sequence: 3 givenname: Jun surname: Wang fullname: Wang, Jun email: jwang@acae.cuhk.edu.hk organization: Department of Mechanical and Automation Engineering, Faculty of Engineering, The Chinese University of Hong Kong, Shatin, New Territories, Hong Kong
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Keywords	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
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Snippet	In this paper, a remote sensing image segmentation procedure that utilizes a single point iterative weighted fuzzy C-means clustering algorithm is proposed...
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SubjectTerms	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
Title	Single point iterative weighted fuzzy C-means clustering algorithm for remote sensing image segmentation
URI	https://dx.doi.org/10.1016/j.patcog.2009.04.013
Volume	42
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