Abstract
In recent years, SAR image processing plays a major role in coastal region monitoring through object identification for the betterment of the livelihood of sea shore people. In order to carry out the above said task, SAR images need to be analysed for extracting features which could be accomplished only after the removal of speckle noise. In this work, a new approach using Neuro-Fuzzy method is proposed for the removal of speckle noise. It is developed on fuzzy logic rule-based system. It is designed based on 3-input 1-output first order Sugeno type fuzzy inference system (FIS). The experimental analysis shows an effective performance of the proposed approach. The obtained results of the proposed approach are compared with results of the traditional approaches and it is proved that the NeuroFuzzy approach is giving better results compared with traditional methods.
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
References
Yu, Y., Acton, S.T.: Speckle reducing anisotropic diffusion. IEEE Trans. Image Process. 11(11), 1260–1270 (2002)
Mastin, G.A.: Adaptive filters for digital image noise smoothing: an evaluation. Comput. Vis. Graph. Image Process. 31(1), 103–121 (1985)
Frost, V.S., Stiles, J.A., Shanmugan, K.S., Holtzman, J.C.: A model for radar images and its application to adaptive digital filtering of multiplicative noise. IEEE Trans. Pattern Anal. Mach. Intell. 2, 157–166 (1982)
Lee, J.-S.: Digital image enhancement and noise filtering by use of local statistics. IEEE Trans. Pattern Anal. Mach. Intell. 2, 165–168 (1980)
Lopes, A., Nezry, E., Touzi, R., Laur, H.: Maximum a posteriori speckle filtering and first order texture models in SAR images. In: 10th Annual International Geoscience and Remote Sensing Symposium on Remote Sensing Science for the Nineties, IGARSS 1990, pp. 2409–2412. IEEE (1990)
Oliver, C., Quegan, S.: Understanding Synthetic Aperture Radar Images. Artech House, Norwood (1998)
Lopes, A., Touzi, R., Nezry, E.: Adaptive speckle filters and scene heterogeneity. IEEE Trans. Geosci. Remote Sens. 28(6), 992–1000 (1990)
Pandey, R., Ghanekar, U.: Fuzzy Filtering Algorithms for Image Processing: Performance Evaluation of Various Approaches
Tyan, C.-Y., Wang, P.P.: Image processing-enhancement, filtering and edge detection using the fuzzy logic approach. In: Second IEEE International Conference on Fuzzy Systems, pp. 600–605. IEEE (1993)
Lee, J.S.: Speckle suppression and analysis for synthetic aperture radar images. Opt. Eng. 25(5), 255636–255636 (1986)
Argenti, F., Alparone, L.: Speckle removal from SAR images in the undecimated wavelet domain. IEEE Trans. Geosci. Remote Sens. 40(11), 2363–2374 (2002)
Li, Y., Gong, H., Feng, D., Zhang, Y.: An adaptive method of speckle reduction and feature enhancement for SAR images based on curvelet transform and particle swarm optimization. IEEE Trans. Geosci. Remote Sens. 49(8), 3105–3116 (2011)
Buckley, J.J., Hayashi, Y.: Fuzzy neural networks: a survey. Fuzzy Sets Syst. 66(1), 1–13 (1994)
Basturk, A., Yksel, M.E.: A generalized neuro-fuzzy filter for removing different types of noise in digital images by a single operator. In: 2006 IEEE 14th Conference on Signal Processing and Communications Applications, pp. 1–4. IEEE, April 2006
Jang, J.-S.R., Sun, C.-T., Mizutani, E.: Neuro-Fuzzy and Soft Computing. Prentice-Hall, Upper Saddle River (1997)
Bhuiyan, M., Ahmad, M., Swamy, M.: Spatially adaptive wavelet based method using the cauchy prior for denoising the SAR images. IEEE Trans. Circuits Syst. Video Technol. 17(4), 500–507 (2007)
Argenti, F., Bianchi, T., Alparone, A.: Segmentation-based MAP despeckling of SAR images in the undecimated wavelet domain. IEEE Trans. Geosci. Remote Sens. 46(9), 2728–2742 (2008)
Deledalle, C., Denis, L., Tupin, F.: Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans. Image Process. 18(12), 2661–2672 (2009)
Lee, J.-S.: Speckle suppression and analysis for synthetic aperture radar images. Optical Eng. 25(5), 255636–255636 (1986)
Amirmazlaghani, M., Amindavar, H.: Two novel Bayesian multiscale approaches for speckle suppression in SAR images. IEEE Trans. Geosci Remote Sens. 47(7), 2980–2993 (2010)
Gnanadurai, D., Sadasivam, V.: An efficient adaptive thresholding technique for wavelet based image denoising. Int. J. Sig. Process. 2(2), 114–119 (2005)
Amirmazlaghani, M., Amindavar, H.: A novel sparse method for despeckling SAR images. IEEE Trans. Geosci. Remote Sens. 50(12), 5024–5032 (2012)
Julier, S.J., Uhlmann, J.K.: A general method for approximating nonlinear transformations of probability distributions. Technical report, RRG, Department of Engineering Science, University of Oxford, Oxford, UK (1996)
Julier, S.J., Uhlmann, J.K.: A new extension of the Kalman filter to nonlinear systems. In: Presented at the AeroSense: 11th International Symposium on Aerospace/Defense Sensing, Simulation and Controls, Orlando, FL (1997)
Gleich, D., Datcu, M.: Gauss Markov model for wavelet-based SAR image despeckling. IEEE Sig. Process. Lett. 13(6), 365–368 (2006)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singanamalla, V., Vaithyanathan, S. (2017). Neuro-Fuzzy Approach for Speckle Noise Reduction in SAR Images. In: Santosh, K., Hangarge, M., Bevilacqua, V., Negi, A. (eds) Recent Trends in Image Processing and Pattern Recognition. RTIP2R 2016. Communications in Computer and Information Science, vol 709. Springer, Singapore. https://doi.org/10.1007/978-981-10-4859-3_23
Download citation
DOI: https://doi.org/10.1007/978-981-10-4859-3_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-10-4858-6
Online ISBN: 978-981-10-4859-3
eBook Packages: Computer ScienceComputer Science (R0)