Remote sensing classification using fuzzy C-means clustering with spatial constraints based on random field

European Journal of Remote Sensing

Volumn 46, Issue 1, 2013, Pages 305-316

  Yang, HongLei ^a   Peng, JunHuan ^a   Xia, BaiRu ^a   Zhang, DingXuan ^a  

^a CHINA UNIVERSITY OF GEOSCIENCES   (China)

Author keywords

Fuzzy C means clustering; Kernel density function; Markov random field; Remote sensing classification

Indexed keywords

FUZZY SYSTEMS; MARKOV PROCESSES; REMOTE SENSING;

CLASSIFICATION ACCURACY; FIRST PRINCIPAL COMPONENTS; FUZZY C MEANS CLUSTERING; KERNEL DENSITY FUNCTION; MARKOV RANDOM FIELDS; NEW CLUSTERING ALGORITHMS; REMOTE SENSING CLASSIFICATION; SPATIAL CORRELATIONS;

CLUSTERING ALGORITHMS;

EID: 84876897576     PISSN: None     EISSN: 22797254     Source Type: Journal    
DOI: 10.5721/EuJRS20134617     Document Type: Article

References (24)

1. Ahmed M.N., Yamany S.M., Mohamed N., Farag A.A, Moriarty T. (2000) A modified fuzzy C-means algorithm for bias field estimation and segmentation of MRI data. IEEE Transactions Medical Imaging, 21 (3): 193-199. doi: http://dx.doi.org/10.1109/42.996338.
2. Bezdejk J.C. (1981) Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum Press, New York. doi: http://dx.doi.org/10.1007/978-1-4757-0450-1.
3. Chen S.C., Zhang D.Q. (2004) Robust image segmentation using FCM with spatial constraints based on new Kernel-Induced distance measure. IEEE Transactions of Systems, Man, and Cybernetics-part B: Cybernetics, 34 (4): 1907-1916. doi: http://dx.doi.org/10.1109/TSMCB.2004.831165.
4. Cheng T.W., Goldgof D.B., Hall L.O. (1996) Fast fuzzy clustering. Fuzzy Sets and Systems, 93: 49-56. doi: http://dx.doi.org/10.1109/FUZZY.1995.409998.
5. Dunn J.C. (1973) A fuzzy relative of the ISODATA process and its use in detecting compact Well-Separated clusters. Journal of Cybernetics, 3: 32-57. doi: http://dx.doi.org/10.108 0/01969727308546046.
6. Fung T., Ledrew E. (1987) Application of principal component analysis to change detection. Photogrammetric Engineering and Remote Sensing, 53: 1649-1658.
7. Hartigan J.A., Wong M.A. (1979) A K-Means clustering Algorithm. Applied Statistics, 8 (1): 100-108. doi: http://dx.doi.org/10.2307/2346830.
8. Hiren S., Jeffrey U., Anupam J. (2003) Fuzzy clustering for intrusion detection. Fuzzy Systems, 2003. FUZZ '03. The 12th IEEE International Conference, 2: 1274-1278. doi: http://dx.doi.org/10.1109/FUZZ.2003.1206614.
9. Hou H.F., Liu S.H. (2005) An Improved Fuzzy C-means Algorithm Based on Genetic Algorithm. Computer Engineering, 31 (17): 152-154.
10. Hung M.C., Yang D.L. (2001) An efficient fuzzy C-Means clustering algorithm. Proceedings IEEE International Conference, 225-232.
11. Kannan A S.R. (2005) Segmentation of MRI using new unsupervised fuzzy C-means algorithm. International Journal on Graphics, Vision and Image Processing, 5 (2): 1023-1027.
12. Kuang T., Zhu Q.X., Sun Y. (2006) Research on initialization of image segmentation with FCM algorithm. Computer Applications, 26 (4): 784-786.
13. Li S.Z. (1995) Markov Random Field Modeling in Image Analysis. Springer Verlag, Berlin.
14. th International conference on Fuzzy Systems, 409-414.
15. Megha P. Arakeri, Ram Mohana R. (2011) Efficient fuzzy clustering based approach to brain tumor segmentation on MR images. Computational Intelligence and Information Technology, 250 (3): 790-795. doi: http://dx.doi.org/10.1007/978-3-642-25734-6_141.
16. Pham D.L., Prince J.L. (1999) An adaptive fuzzy C-means algorithm for image segmentation in the presence of intensity inhomogeneities. Pattern Recognition Letters, 120 (1): 57-68. doi: http://dx.doi.org/10.1016/S0167-8655(98)00121-4.
17. Pham D.L. (2002) Fuzzy clustering with spatial constraints. Proceedings of the IEEE International Conference on Image Processing, New York, 2: 65-68. doi: http://dx.doi. org/10.1109/ICIP.2002.1039888.
18. Ruspini E.H. (1969) A new approach to clustering, Information and. Control, 15:22-32.
19. Shamsi H., Seuedarabi H., Erfani S. (2011) MRI image segmentation based on new fuzzy c-means algorithm. International Journal on Computer Science and Engineering, 3 (8): 3115-3120.
20. Tolias Y. A., Panas S.M. (1998a) On applying spatial constraints in fuzzy image clustering using a fuzzy rule-based system. IEEE Signal Processing Letters, 5 (10): 245-247. doi: http://dx.doi.org/10.1109/97.720555.
21. Tolias Y.A., Panas S.M. (1998b) Image segmentation by a fuzzy clustering algorithm using adaptive spatially constrained membership functions. IEEE Transactions on Systems Man and Cybernetics Part B-Cybernetics, Cybernet PartA, 28 (3): 359-369. doi: http://dx.doi.org/10.1109/3468.668967.
22. Xu B., Gong P. (2007) Land use/cover classification with multispectral and hyperspectral EO 1 data. Photogrammetric Engineering & Remote Sensing, 73 (8): 955-965.
23. Yang J., Ning Y.F. (2010) Research on initial clustering centers of fuzzy c-means algorithm and its application to intrusion detection. Environmental Science and Information Application Technology (ESIAT), 2010 International Conference, 161-163. doi: http://dx.doi.org/10.1109/ESIAT.2010.5568387.
24. Yu J., Guo P.H., Chen P.X., Zhang Z.S., Ruan W.B. (2008) Remote sensing image classification based on improved fuzzy c-means. Geospatial Information Science, 11 (2): 90-94. doi: http://dx.doi.org/10.1007/s11806-008-0017-8.