Fuzzy Clustering Methods in Multispectral Satellite Image Segmentation

Rauf Kh. Sadykhov, Andrey V. Dorogush, Leonid P. Podenok

2007

Abstract

Segmentation method for subject processing the multispectral satellite images based on fuzzy clustering and preliminary non-linear filtering is represented. Three fuzzy clustering algorithms, namely Fuzzy C-means, Gustafson-Kessel, and Gath-Geva have been utilized. The experimental results obtained using these algorithms with and without preliminary nonlinear filtering to segment multispectral Landsat images have approved that segmentation based on fuzzy clustering provides good-looking discrimination of different land cover types. Implementations of Fuzzy C-means, Gustafson-Kessel, and Gath-Geva algorithms have got linear computational complexity depending on initial cluster amount and image size for single iteration step. They assume internal parallel implementation. The preliminary processing of source channels with nonlinear filter provides more clear cluster discrimination and has as a consequence more clear segment outlining.

References

  1. Hoppner, F., Klawonn F., Kruse, R., Runkler, T.: Fuzzy Cluster Analysis. Wiley, Chichester (1999).
  2. Gustafson, D. E., Kessel, W. C.: Fuzzy clustering with fuzzy covariance matrix. In Proceedings of the IEEE CDC, INSTICC Press, San Diego (1979) 761-766
  3. Babuska, R., van der Veen, P. J., Kaymak, U.: Improved covariance estimation for GustafsonKessel clustering. IEEE International Conference on Fuzzy Systems (2002) 1081-1085”
  4. Gath, I., Geva, A. B.: Unsupervised optimal fuzzy clustering. IEEE Transactions on Pattern Analysis and Machine Intelligence (1989) 7:773-781
  5. Dunn, J. C.: A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters. Journal of Cybernetics (1973) 3: 32-57
  6. Bezdek, J. C.: Pattern Recognition with Fuzzy Objective Function Algoritms. Plenum Press, New York (1981)
  7. Smith, S. M., Brady, J. M.: SUSAN - a new approach to low level image processing. International Journal of Computer Vision May (1997) 23(1):45-78
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Paper Citation


in Harvard Style

Kh. Sadykhov R., V. Dorogush A. and P. Podenok L. (2007). Fuzzy Clustering Methods in Multispectral Satellite Image Segmentation . In Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007) ISBN 978-972-8865-86-3, pages 91-98. DOI: 10.5220/0001635200910098


in Bibtex Style

@conference{anniip07,
author={Rauf Kh. Sadykhov and Andrey V. Dorogush and Leonid P. Podenok},
title={Fuzzy Clustering Methods in Multispectral Satellite Image Segmentation},
booktitle={Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)},
year={2007},
pages={91-98},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001635200910098},
isbn={978-972-8865-86-3},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Workshop on Artificial Neural Networks and Intelligent Information Processing - Volume 1: ANNIIP, (ICINCO 2007)
TI - Fuzzy Clustering Methods in Multispectral Satellite Image Segmentation
SN - 978-972-8865-86-3
AU - Kh. Sadykhov R.
AU - V. Dorogush A.
AU - P. Podenok L.
PY - 2007
SP - 91
EP - 98
DO - 10.5220/0001635200910098