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Digital Image Processing
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Multiresolution Fuzzy Clustering For SAR Image Segmentation
The proposed algorithm
The proposed algorithm can be described by Figure 1. A multiresolution framework is exploited to reduce the computational burden while preserving good segmentation results. A pyramid of the image is obtained by using the wavelet transform. Each layer of the pyramid is consisted by 4 sub-images forming 4-dimension texture feature for the clustering. The FCM
Figure 1: Overall scheme of the proposed algorithm
(a)
(b)
(c)
Figue 2. Experimental results.(A) Original JERS-1/SAR image. (B) 2-level wavelet pyramid. (c) Final clustering map.
Algorithm is first implemented on the coarsest image approximation and a test a carried out, for each pixel, on the final membership value against a prescribed threshold T. The pixel is uncertain pixels at the finer image approximation. This procedure is iterated downward until the bottom of the pyramid is reached where all remaining uncertain pixels are labeled. Finally, the clustering is followed with a majority filtering to obtain more homogeneous regions. Since each time the algorithm operates on a very limited number of pixel, computation time is reduced by a large amount.
Results
Experiments were carried out for the proposed algorithm using different JERS-1/SAR image. An example is given in Figure 2. The original image consits of 1024 x 1024 pixels is show figure 2(a). the pyramid of this image computed by using 2-level wavelt multiresolution fuzzy clustering and majority filtering is given in Figure 2(c). Note that the majority filtered is perform within the window size of 3 x 3 pixels.
Conclusion
Amultiresolution fuzzy clustering approach to SAR image segmentation within a wavelet-based framework has been presented. It has been shows that wave let representation provides large computation time saving and useful feature to improves te result of the fuzzy clustering algorithm.
Acknowledgement
The authors with to thank the National Research Council of Thailand (NRCT) of providing the satellite image data.
Reference
- J.A. Richards, Remote Sensing Digital Image Analysis, Berlin: springer-Verlag, 1993.
- M.N. Trivedi and J.C. Bezdek, "Low-Level Segmenttion of Aerial Images with Fuzzy Clustering, "IEEE Transon Systems, man and Cybernetics, vol. SMC-16, no.4, pp.589-598,1986.
- C.S. Burrus, R.A. Gopinath, and H. Guo, Introduction to wavelets and Wavelet Transform, New Jersey: Perentice -hall International, Inc., 1998.
- S. Mallat, A Wavelet Tour of Signal Processing. San Diago: Academic Press, 1998.
- S. Mallant, "A Theory of Multiresolution Signal Decomposition: The Wavelet Representation," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 11, pp. 674-693, 1989.
- I. Daubechies, "Orthonormal Bases of Compaactly supported Wavelets," Comm. Pure and Applied Mathematics, vol. 41, no. 7, pp. 909-996, 1988.
- J.C. Bezek and S.K. Pal, Fuzzy Models for Pattern Recognition, IEEE Press 1992.
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