FUZZY CLUSTERING METHODS IN MULTISPECTRAL SATELLITE IMAGE SEGMENTATION

Authors

  • Rauf Kh. Sadykhov
  • Valentin V. Ganchenko
  • Leonid P. Podenok

DOI:

https://doi.org/10.47839/ijc.8.1.660

Keywords:

Fuzzy Systems, Clustering, Image Segmentation, Multi-spectral Images, Satellite Images.

Abstract

Segmentation method for subject processing the multi-spectral 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 multi-spectral Landsat images have approved that segmentation based on fuzzy clustering provides good-looking discrimination of different land cover types. Implementations of Fuzzy Cmeans, 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

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Babuska, R., van der Veen, P. J., Kaymak, U.:, Improved covariance estimation for Gustafson-Kessel clustering. IEEE International Conference on Fuzzy Systems (2002) 1081–1085”.

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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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Published

2014-08-01

How to Cite

Sadykhov, R. K., Ganchenko, V. V., & Podenok, L. P. (2014). FUZZY CLUSTERING METHODS IN MULTISPECTRAL SATELLITE IMAGE SEGMENTATION. International Journal of Computing, 8(1), 87-94. https://doi.org/10.47839/ijc.8.1.660

Issue

2009, Volume 8, Issue 1

Section

Articles

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