Remote Sensing Classification Using Fuzzy C-means Clustering with Spatial Constraints Based on Markov Random Field

Abstract This paper proposes a new clustering algorithm which integrates Fuzzy C-means clustering with Markov random field (FCM). The density function of the first principal component which sufficiently reflects the class differences and is applied in determining of initial labels for FCM algorithm. Thus, the sensitivity to the random initial values can be avoided. Meanwhile, this algorithm takes into account the spatial correlation information of pixels. The experiments on the synthetic and QuickBird images show that the proposed method can achieve better classification accuracy and visual qualities than the general FCM algorithm.

[1]  G. Ram Mohana Reddy,et al.  Efficient Fuzzy Clustering Based Approach to Brain Tumor Segmentation on MR Images , 2011 .

[3]  Lawrence O. Hall,et al.  Fast clustering with application to fuzzy rule generation , 1995, Proceedings of 1995 IEEE International Conference on Fuzzy Systems..

[4]  Don-Lin Yang,et al.  An efficient Fuzzy C-Means clustering algorithm , 2001, Proceedings 2001 IEEE International Conference on Data Mining.

[5]  Jian Yang,et al.  Research on initial clustering centers of fuzzy c-means algorithm and its application to intrusion detection , 2010, 2010 The 2nd Conference on Environmental Science and Information Application Technology.

[6]  Aly A. Farag,et al.  A modified fuzzy c-means algorithm for bias field estimation and segmentation of MRI data , 2002, IEEE Transactions on Medical Imaging.

[7]  Dzung L. Pham,et al.  Fuzzy clustering with spatial constraints , 2002, Proceedings. International Conference on Image Processing.

[8]  Lawrence O. Hall,et al.  Fast fuzzy clustering , 1998, Fuzzy Sets Syst..

[9]  Jie Yu,et al.  Remote sensing image classification based on improved fuzzy c-means , 2008 .

[10]  Liu Su-hua.  An Improved Fuzzy C-means Algorithm Based on Genetic Algorithm , 2005 .

[11]  Anupam Joshi,et al.  Fuzzy clustering for intrusion detection , 2003, The 12th IEEE International Conference on Fuzzy Systems, 2003. FUZZ '03..

[12]  Stan Z. Li,et al.  Markov Random Field Modeling in Image Analysis , 2001, Computer Science Workbench.

[13]  Yannis A. Tolias,et al.  Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions , 1998, IEEE Trans. Syst. Man Cybern. Part A.

[14]  James C. Bezdek,et al.  Pattern Recognition with Fuzzy Objective Function Algorithms , 1981, Advanced Applications in Pattern Recognition.

[15]  Jong-Myon Kim,et al.  A generalized spatial fuzzy c-means algorithm for medical image segmentation , 2009, 2009 IEEE International Conference on Fuzzy Systems.

[16]  J. C. Dunn,et al.  A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters , 1973 .

[17]  Daoqiang Zhang,et al.  Robust image segmentation using FCM with spatial constraints based on new kernel-induced distance measure , 2004, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

[18]  Enrique H. Ruspini,et al.  A New Approach to Clustering , 1969, Inf. Control..

[19]  E. LeDrew,et al.  Application of principal components analysis to change detection , 1987 .

[20]  Y. A. Tolias,et al.  On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system , 1998, IEEE Signal Processing Letters.

[21]  J. A. Hartigan,et al.  A k-means clustering algorithm , 1979 .

[22]  Florence Tupin,et al.  Remote Sensing Imagery , 2014 .

[23]  Kuang Tai.  Research on initialization of image segmentation with FCM algorithm , 2006 .

[24]  B. Xu,et al.  Land-use/land-cover classification with multispectral and hyperspectral EO-1 data , 2007 .