Cauchy pdf modelling and its application to SAR image despeckling

来源 :Journal of Systems Engineering and Electronics | 被引量 : 0次 | 上传用户:kid0226
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Synthetic aperture radar (SAR) imagery is a kind of coherent system that produces a random pattern,named speckle,which degrades the merit of SAR images and affects their further application seriously.Therefore,how to restore SAR image from the speckle has become a necessary step in post-processing of image.A new despeckling method is putforth on the basis of wavelet.First,a new approach on the basis of “second kind statistics” is used to estimate the dispersion parameter of the Cauchy distribution.Then,this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image.Based on the above ideas,a new homomorphic wavelet-based maximum a posterior (MAP) despeckling method is proposed.Finally,the simulated speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention. Synthetic aperture radar (SAR) imagery is a kind of coherent system that produces a random pattern, named speckle, which degrades the merit of SAR images and affects their further application seriously. Herefore, how to restore SAR image from the speckle has become a necessary step in post-processing of image. A new despeckling method is putforth on the basis of wavelet. First, a new approach on the basis of “second kind statistics ” is used to estimate the dispersion parameter of the Cauchy distribution. Chen, this Cauchy prior is applied to model the distribution of the wavelet coefficients for the log-transformed reflectance of SAR image. Based on the above ideas, a new homomorphic wavelet-based maximum a posterior (MAP) despeckling method is proposed. speckled image and the real SAR image are used to verify our proposed method and the results show that it outperforms the other methods in terms of the speckle reduction and the feature retention.
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