DocumentCode :
867518
Title :
SAR speckle reduction using wavelet denoising and Markov random field modeling
Author :
Xie, Hua ; Pierce, Leland E. ; Ulaby, Fawwaz T.
Author_Institution :
Dept. of Electr. Eng. & Comput. Sci., Michigan Univ., Ann Arbor, MI, USA
Volume :
40
Issue :
10
fYear :
2002
fDate :
10/1/2002 12:00:00 AM
Firstpage :
2196
Lastpage :
2212
Abstract :
The granular appearance of speckle noise in synthetic aperture radar (SAR) imagery makes it very difficult to visually and automatically interpret SAR data. Therefore, speckle reduction is a prerequisite for many SAR image processing tasks. In this paper, we develop a speckle reduction algorithm by fusing the wavelet Bayesian denoising technique with Markov-random-field-based image regularization. Wavelet coefficients are modeled independently and identically by a two-state Gaussian mixture model, while their spatial dependence is characterized by a Markov random field imposed on the hidden state of Gaussian mixtures. The Expectation-Maximization algorithm is used to estimate hyperparameters and specify the mixture model, and the iterated-conditional-modes method is implemented to optimize the state configuration. The noise-free wavelet coefficients are finally estimated by a shrinkage function based on local weighted averaging of the Bayesian estimator. Experimental results show that the proposed method outperforms standard wavelet denoising techniques in terms of the signal-to-noise ratio and the equivalent-number-of-looks measures in most cases. It also achieves better performance than the refined Lee filter.
Keywords :
Bayes methods; Markov processes; geophysical signal processing; geophysical techniques; radar imaging; remote sensing by radar; speckle; synthetic aperture radar; terrain mapping; wavelet transforms; Bayes method; Bayesian method; Expectation-Maximization algorithm; Markov random field model; algorithm; geophysical measurement technique; granular appearance; hyperparameters; image processing; image regularization; land surface; mixture model; radar imaging; radar remote sensing; speckle noise; speckle reduction; synthetic aperture radar; terrain mapping; two-state Gaussian mixture model; wavelet denoising; wavelet transform; Bayesian methods; Expectation-maximization algorithms; Image processing; Markov random fields; Noise reduction; Optimization methods; Speckle; State estimation; Synthetic aperture radar; Wavelet coefficients;
fLanguage :
English
Journal_Title :
Geoscience and Remote Sensing, IEEE Transactions on
Publisher :
ieee
ISSN :
0196-2892
Type :
jour
DOI :
10.1109/TGRS.2002.802473
Filename :
1105905
Link To Document :
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