Achim Alin, Kuruoğlu Ercan E, Zerubia Josiane
Signal Processing Group, Department of Electrical and Electronic Engineering, University of Bristol, BS8 1UB Bristol, UK.
IEEE Trans Image Process. 2006 Sep;15(9):2686-93. doi: 10.1109/tip.2006.877362.
Synthetic aperture radar (SAR) images are inherently affected by a signal dependent noise known as speckle, which is due to the radar wave coherence. In this paper, we propose a novel adaptive despeckling filter and derive a maximum a posteriori (MAP) estimator for the radar cross section (RCS). We first employ a logarithmic transformation to change the multiplicative speckle into additive noise. We model the RCS using the recently introduced heavy-tailed Rayleigh density function, which was derived based on the assumption that the real and imaginary parts of the received complex signal are best described using the alpha-stable family of distribution. We estimate model parameters from noisy observations by means of second-kind statistics theory, which relies on the Mellin transform. Finally, we compare the proposed algorithm with several classical speckle filters applied on actual SAR images. Experimental results show that the homomorphic MAP filter based on the heavy-tailed Rayleigh prior for the RCS is among the best for speckle removal.
合成孔径雷达（SAR）图像天生受到一种称为斑点噪声的信号相关噪声的影响，这种噪声是由雷达波的相干性引起的。在本文中，我们提出了一种新颖的自适应去斑滤波器，并推导了雷达散射截面（RCS）的最大后验（MAP）估计器。我们首先采用对数变换将乘性斑点噪声转换为加性噪声。我们使用最近引入的重尾瑞利密度函数对RCS进行建模，该函数是基于这样的假设推导出来的：接收复信号的实部和虚部最好用α稳定分布族来描述。我们通过依赖梅林变换的第二类统计理论从噪声观测中估计模型参数。最后，我们将所提出的算法与应用于实际SAR图像的几种经典斑点滤波器进行比较。实验结果表明，基于RCS重尾瑞利先验的同态MAP滤波器在去斑方面表现出色。
