Abstract
Image segmentation is a fundamental step and foundation for automatic SAR image interpretation. By combining the Gabor filter bank (GFB) and active contours, this paper proposes a new SAR image segmentation method. Firstly, GFB is used to efficiently suppress speckles in SAR image and modify the gray histogram into Gaussian mixture model (GMM). Then GMM-based pixel classification is employed to pre-segment the filtered image. Finally, the active contours, initialized with the pre-segmented regions, are applied to unfiltered image for the final segmentation with several iterations. Experiments are conducted to vivificate the efficiency and effectiveness of the proposed method.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Jiao, L., Wang, S., Hou, B.: A Review of SAR Images Understanding and Interpretation. Acta Electronica Sinica 33(12), 2423–2434 (2005)
El Zaart, A., Ziou, D., Wang, S., Jiang, Q.: Segmentation of SAR Images. Pattern Recognit. 35(2), 713–724 (2002)
Ross, T., Worrell, S., Velten, V., Mossing, J., Bryant, M.: Standard SAR ATR Evaluation Experiments Using the MSTAR Public Release Data Set. In: SPIE Conf. on Algorithms for Synthetic Aperture Radar Imagery, vol. 3370, pp. 566–573 (1998)
Chumsamrong, W., Thitimajshima, P., Rangsanseri, Y.: Synthetic Aperture Radar (SAR) Image Segmentation Using a New Modified Fuzzy C-means Algorithm. In: Proceedings of IEEE Symp. Geosci. Remote Sens., Honolulu, pp. 624–626 (2000)
Petrou, M., Matrucceli, A.: On the Stability of Thresholding SAR Images. Pattern Recognit. 31(11), 1791–1796 (1998)
Hua, X., Pierce, L.E., Ulaby, F.T.: SAR Speckle Reduction Using Wavelet Denoising and Markov Random Field Modeling. IEEE Trans. Geosci. Remote Sens. 40(10), 2196–2212 (2002)
Yan, X., Jiao, L., Xu, S.: SAR Image Segmentation Based on Gabor Filters of Adaptive Window in Overcomplete Brushlet Domain. In: Proceedings of Asia-Pacific Conference on Synthetic Aperture Radar, pp. 660–663 (2009)
Weisenseel, R.A., Karl, W.C., Castanon, D.A., Brower, R.C.: MRF-based Algorithms for Segmentation of SAR Images. In: Proceedings of the 1998 International Conference on Image Processing, vol. 3, pp. 770–774 (1998)
Horritt, M.S.: A Statistical Active Contour Model for SAR Image Segmentation. Image and Vision Computing 17, 213–224 (1999)
Kristan, M., Leonardis, A., Skocaj, D.: Multivariate Online Kernel Density Estimation with Gaussian Kernels. Pattern Recognit. 44(10), 2630–2642 (2011)
Ari, C., Aksoy, S., Arkan, O.: Maximum Likelihood Estimation of Gaussian Mixture Models Using Stochastic Search. Pattern Recognit. 45, 2804–2816 (2012)
Kass, M., Witkin, A., Terzopoulos, D.: Snakes: Active Contour Models. Int. J. Compution Vision 1(4), 321–331 (1987)
Peng, R., Wang, X., Lu, Y.: SAR Imagery Segmentation Based on Integrated Active Contour. In: Proceedings of the Int. Conf. on Advanced Computer Control, pp. 43–47 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Ni, W., Gao, X., Yan, W. (2013). SAR Image Segmentation Based on Gabor Filter Bank and Active Contours. In: Yang, J., Fang, F., Sun, C. (eds) Intelligent Science and Intelligent Data Engineering. IScIDE 2012. Lecture Notes in Computer Science, vol 7751. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-36669-7_65
Download citation
DOI: https://doi.org/10.1007/978-3-642-36669-7_65
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-36668-0
Online ISBN: 978-3-642-36669-7
eBook Packages: Computer ScienceComputer Science (R0)