Abstract
Utilizing the Contourlet’s advantages of multiscale, localization, directionality and anisotropy, a new SAR image segmentation algorithm based on hidden Markov tree (HMT) in Contourlet domain and dempster-shafer (D-S) theory of evidence is proposed in this paper. The algorithm extends the hidden Markov tree framework to Contourlet domain and fuses the clustering and persistence of Contourlet transform using HMT model and D-S theory, and then, we deduce the maximum a posterior (MAP) segmentation equation for the new fusion model. The algorithm is used to segment the real SAR images. Experimental results and analysis show that the proposed algorithm effectively reduces the influence of multiplicative speckle noise, improves the segmentation accuracy and provides a better visual quality for SAR images over the algorithms based on HMT-MRF in the wavelet domain, HMT and MRF in the Contourlet domain, respectively.
This work is supported by the National Natural Science Fundation of China (No.60872137) ,the National Defence Fundation of China(No.9140A01060408DZ0104),and by the Ariation Science Fundation of China(20080181002).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bovolo, F., Bruzzone, L.: A Detail-Preserving Scale-Driven Approach to Change Detection in Multitemporal SAR Images. IEEE Transaction on Geoscience and Remote Sensing 43(12), 2963–2972 (2005)
Pierce, L.E., Ulaby, F.T.: SAR Speckle Reduction using Wavelet Denoising and Markov Random Field Modeling. IEEE Trans on GRS 40(10), 2196–2212 (2002)
Wu, Y., Wang, X., Liao, G.-S.: SAR Images Despeckling via Bayesian Fuzzy Shrinkage Based on Stationary Wavelet Transform. Applied and Numerical Harmonic Analysis 12, 407–418 (2006)
Wu, Y., Zhang, Q., Wang, X., Liao, G.-S.: SAR Images Despeckling Based on Hidden Markov Mixture Model in the Wavelet Domain. In: CIE International Conference on Radar, ShangHai, China, pp. 16–19 (2006)
Zhang, Q., Wu, Y.: Wavelet Markov Random Field Based on Context and Hidden Class Label for SAR Image Segmentation. Journal of Electronics & Information Technology 30(1), 211–215 (2008)
Do, M.N., Vetterli, M.: Contourlets:a new directional multiresolution image representation. In: Signals, Systems and Computers,Conference Record of the Thirty-Sixth Asilomar Conference, Rochester, vol. 1, pp. 3–6 (2002)
Candès, E.J., Donoho, D.L.: Curvelets. Technical report, Stanford Univ (1999)
Ni, W., Guo, B., Yan, Y.: Speckle Suppression for SAR Images Based on Adaptive Shrinkage in Contourlet Domain. In: Proceeding of the 6th World Congress on Intelligent Control and Automation, pp. 10017–10027 (2006)
Miao, Q., Wang, B.: A Novel Image Fusion Method Using Contourlet Transform. IEEE Trans. Signal Proc., 548–552 (2006)
Bouman, C.A., Shapiro, M.: A multiscale random field model for Bayesian image segmentation. IEEE Trans. on Images Processing 3(2), 162–177 (1994)
Venkatachalam, V., Choi, H.: Multiscale SAR image segmentation using wavelet-domain hidden Markov tree model. In: SPIE, vol. 3497, pp. 141–151 (1998)
Choi, H., Baraniuk, R.G.: Multiscale Image Segmentation Using Wavelet-Domain Hidden Markov Models. IEEE Transaction on Image Processing 10(9), 1309–1321 (2001)
Sha, Y.-H., Jiao, L.-C.: Unsupervised Image Segmentation Using Contourlet Domain Hidden Markov Tree Model. In: Kamel, M.S., Campilho, A.C. (eds.) ICIAR 2005. LNCS, vol. 3656, pp. 32–39. Springer, Heidelberg (2005)
Richards, J.A., Jia, X.-P.: A Dempster-Shafer Relaxation Approach to Context Classification. IEEE transactions on geoscience and remote sensing 45(5), 1422–1431 (2007)
Po, D.Y., Do Duncan, M.N.: Directional multiscale modeling of images using the Contourlet transform. IEEE Trans. on Image Proc. 15(6), 1610–1620 (2003)
Crouse, S.: Matthew: Wavelet-Based Statistical Signal Processing Using Hidden Markov Models. IEEE Trans. on Signal Proc. 46(4), 886–902 (1998)
Derin, H., Cole, W.: Segmentation of textured images using Gibbs random fields. CVGIP 35(1), 72–98 (1986)
Aarts, E.H.L.: Simulated Annealing and Boltzmann Machines: A Stochastic Approach to Combinatorial Optimization and Neural Computing. Wiley, New York (1989)
Besag, J.: On the statistical analysis of dirty pictures. J. R. Statist. Soc. B 48(3), 259–302 (1986)
Vetterli, M., Herley, C.: Wavelets and filter banks: Theory and desigh. IEEE Trans. Signal Proc. 40(9), 2207–2232 (1992)
Phoong, S.-M., Kim, C.W., Vaidyanathan, P.P., Ansari, R.: A new class of two-channel biorthogonal filter banks and wavelet bases. IEEE Trans. Signal Proc. 43(3), 649–665 (1995)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2009 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Wu, Y., Li, M., Zong, H., Wang, X. (2009). Fusion Segmentation Algorithm for SAR Images Based on HMT in Contourlet Domain and D-S Theory of Evidence. In: Gervasi, O., Taniar, D., Murgante, B., Laganà, A., Mun, Y., Gavrilova, M.L. (eds) Computational Science and Its Applications – ICCSA 2009. ICCSA 2009. Lecture Notes in Computer Science, vol 5593. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02457-3_76
Download citation
DOI: https://doi.org/10.1007/978-3-642-02457-3_76
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02456-6
Online ISBN: 978-3-642-02457-3
eBook Packages: Computer ScienceComputer Science (R0)