SOR based fuzzy k-means clustering algorithm for classification of remotely sensed images

Authors:
Dong-jun Xin;Yen-Wei Chen
Affiliations:
College of Computer Science and Information Technology, Central South University of Forestry and Technology, Hunan, China;College of Computer Science and Information Technology, Central South University of Forestry and Technology, Hunan, China,College of Information Science and Engineering, Ritsumeikan University, Ja ...
Venue:
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Year:
2013

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Abstract

Fuzzy k-means clustering algorithms have successfully been applied to digital image segmentations and classifications as an improvement of the conventional k-means cluster algorithm. The limitation of the Fuzzy k-means algorithm is its large computation cost. In this paper, we propose a Successive Over-Relaxation (SOR) based fuzzy k-means algorithm in order to accelerate the convergence of the algorithm. The SOR is a variant of the Gauss---Seidel method for solving a linear system of equations, resulting in faster convergence. The proposed method has been applied to classification of remotely sensed images. Experimental results show that the proposed SOR based fuzzy k-means algorithm can improve convergence speed significantly and yields comparable similar classification results with conventional fuzzy k-means algorithm.