基于小波域隐Markov模型的SAR图像滤波方法

SAR Images Filtering Using Wavelet-based Hidden Markov Model

在小波域隐Markov模型(HMM)的基础上提出一种新的合成孔径雷达(SAR)图像的滤波方法。首先根据小波变换的内在特征,建立小波域的隐Markov树(HMT)模型,通过EM算法可以获得该HMT模型的参数估计。然后根据SAR图像的统计性质,将SAR图像的乘法斑点杂噪声在局部范围内近似为加性白高斯噪声,通过最小均方差(MMSE)估计可以获得信号的小波变换值。通过对真实SAR图像的应用,结果说明该方法可以在保存图像细节特征的情况下有效地抑制图像的噪声。

A new synthetic aperture radar (SAR) image filter method is proposed based on hidden Markov model (HMM). First the hidden Markov tree(HMT) model in waveletdomain is established. The parameters can be estimated by EM algorithm.Then SAR image's statistical property is studied, the multiple speckle noise of SAR image can be approximate as white Gauss addition noise under the local area. The wavelet coefficient of signal can be estimated by minimum meansquare estimate(MMSE). Applying this method to simulated image and real SAR image, the result shows that this method can effectively reduce speckle noise, while keeping image details.