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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Bibliographic Details
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
Online Access:Get full text
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Summary: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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2009.04.013