A method for segmenting remote sensing images using the Watershed algorithm and Fuzzy C-Means clustering
Subject Areas : Electronics Engineering
Mohsen Hamed
1
,
Fatemeh Hajiani
2
1 - Sama Technical and Vocational School, Islamic Azad University, Bushehr Branch, Bushehr, Iran
2 - Islamic Azad University, Khormuj Branch, Iran, Faculty Member
Keywords:
Abstract :
In the division of remote sensing image pixels using Watershed segmentation, the boundaries of the image are not well defined. In this paper, an image clustering algorithm based on Watershed segmentation and Fuzzy C-Means clustering is presented. The method is that first the Watershed algorithm is used to segment the image obtained from the sum of the image derivative with the original image. Image derivation makes the borders of the image well defined and does not overlap between the borders. After segmentation, Fuzzy C-Means clustering is used to combine similar regions. Finally, in order to improve the clustering results, a new segmentation matrix is calculated for each area of the image, according to the characteristics of its neighboring areas. Due to the fact that remote sensing images contain a high level of noise, the proposed algorithm is more capable of dealing with noise compared to the conventional Watershed algorithm, and the edges of the image appear better. The test results of the proposed method on a sample of remote sensing image show the practicality and efficiency of the proposed algorithm.
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[9] S. Saha and S. Bandyopadhyay, “A Symmetry Based Multiobjective Clustering Technique for Automatic Evolution of Clusters,” Pattern Recognition, vol. 43, No. 3, March 2010, pp. 738-751.
[10] M. Hasanzadeh, and s. kasaei, “A Multispectral Image Segmentation Method using Size-Weighted Fuzzy Clustering and Membership Connectedness, ” IEEE Geosc. and Remote Sen. Letters, Vol. 7, Issue 3, pp. 520-524, 2010.
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[13] L. Vincent and P. Soille, “Watersheds in digital spaces: An efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Machine Intel.vol. 13, pp. 583–598, 1991.
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[15] Beucher. S, “The watershed transformation applied to image Segmentation,” Scanning Microsc, Vol 6, p.p. 299-314, 2005.
[16] J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms,” Plenum, New York, 1981.
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[20] Gath, I. and Geva, A. B. (1989). “Unsupervised optimal fuzzy clustering.” In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 7:773–781.
_||_[1] M. Williamson, “Cambridge Dictionary of Space Technology,” Cambridge University Press, First Edition, 2001.
[2] J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms,” Plenum, New York, 1981.
[3] Zeng. Xuexing, Ren. Jinchang, McKee. David, Lavender. Samantha, Marshall. Stephen, "Pixel clustering and hyperspectral image segmentation for ocean colour remote sensing", In Hyperspectral Imaging Conference, 2012.
[4] Du Gen-yuan, Miao Fang; Sheng-Li, Tian; Liu Ye, "A Modified Fuzzy C-means Algorithm in Remote Sensing Image Segmentation", International Conference on Environmental Science and Information Application Technology, 4-5 July 2009, Vol.3, Pages 447–450.
[5] Li Ma, R.C. Staunton, “A modified fuzzy c-means image segmentation algorithm for use with uneven illumination patterns,” In Pattern Recognition Conference, 2007, Vol.. 40, No.11, pp.3005-3011.
[6] Leonid P. Podenok, RaufKh. Sadykhov, “Multispectral Satellite Image Segmentation Using Fuzzy Clustering and Nonlinear Filtering Methods,” International Machine Vision and Image Processing Conference, 2008, pp. 43-48.
[7] J. Fan, M. Han, and J. Wang, “Single point iterative weighted fuzzy c-means clustering algorithm for remote sensing image segmentation,” Pattern Recognition, 42(11), 2009, pp. 2527-2540.
[8] Leonid P. Podenok, RaufKh. Sadykhov, “Multispectral Satellite Image Segmentation Using Fuzzy Clustering and Nonlinear Filtering Methods,” International Machine Vision and Image Processing Conference, 2008, pp. 43-48.
[9] S. Saha and S. Bandyopadhyay, “A Symmetry Based Multiobjective Clustering Technique for Automatic Evolution of Clusters,” Pattern Recognition, vol. 43, No. 3, March 2010, pp. 738-751.
[10] M. Hasanzadeh, and s. kasaei, “A Multispectral Image Segmentation Method using Size-Weighted Fuzzy Clustering and Membership Connectedness, ” IEEE Geosc. and Remote Sen. Letters, Vol. 7, Issue 3, pp. 520-524, 2010.
[11] W. S. Aldrich, M. E. Kappus, and R. G. Resmini, “HYDICE post-flight data processing,” Proc. SPIE, vol. 2758, pp. 354–363, 1996.
[12] Pal. N. R, and Pal, S. K, “A Review on Image Segmentation Techniques,”Pattern Recognition, 26(9), p.p. 1274-1294, 1993.
[13] L. Vincent and P. Soille, “Watersheds in digital spaces: An efficient algorithm based on immersion simulations,” IEEE Trans. Pattern Anal. Machine Intel.vol. 13, pp. 583–598, 1991.
[14] Tarabalka. Y, Chanussot. J, and Benediktsson. J. A, “Segmentation and classification of hyperspectral images using watershed transformation”, Pattern Recognition, vol. 43, no. 7, pp. 2367-2379, July 2010.
[15] Beucher. S, “The watershed transformation applied to image Segmentation,” Scanning Microsc, Vol 6, p.p. 299-314, 2005.
[16] J. C. Bezdek, “Pattern recognition with fuzzy objective function algorithms,” Plenum, New York, 1981.
[17] J. C. Bezdek. “Cluster validity with fuzzy sets” Cybernetics and Systems, 1973, 3(3): pp. 58–73.
[18] J. C. Bezdek. “Mathematical models for systematic and taxonomy”. Proc. of Eight International Conference on NumericalTaxonomy, San Francisco, 1975: 143–166.
[19] R. Babuska, P. J. v. d. V. and Kaymak, U. (2002). “Improved covariance estimation for gustafson-kessel clustering,” In IEEE International Conference on Fuzzy Systems, pages 1081–1085.
[20] Gath, I. and Geva, A. B. (1989). “Unsupervised optimal fuzzy clustering.” In IEEE Transactions on Pattern Analysis and Machine Intelligence, pages 7:773–781.