一种基于核回归的SAR图像自适应相干斑抑制方法

为了在抑制相干斑噪声的同时更好地保持SAR图像中的点目标和边缘目标，在经典核回归方法的基础上，本文提出了基于核回归的SAR图像自适应相干斑抑制方法。通过分析SAR图像的幅度分布特性，在构建模型时，以图像的幅度值为判别条件，使核函数在幅值较小的背景区域具有较大的光滑作用以抑制噪声，而在幅值较大的目标区域光滑作用较小以保护目标特征；同时考虑对边缘的保护作用，基于散布矩阵修正了自适应核回归方法，建立了基于核回归的SAR图像自适应相干斑抑制方法。试验结果表明，该算法通过将幅度值和散布矩阵引入核函数，更好地抑制了噪声，同时也保持了图像中的点目标和边缘。

An Adaptive Speckle Reduction Method Based Kernel Regression for SAR Image

In order to reduce speckle noise in SAR image processing while preserving scatter targets and edge as more as possible, an adaptive speckle reduction method based on a kernel regression is presented. By analyzing the magnitude distribution characteristic of SAR image, while building model the image magnitude is chosen as the classification condition. The kernel function heavily smoothes to reduce the speckle for background region with small magnitude, and protect targets for targets region with large magnitude. Then considering preserving the edges, the steering kernel is modified based on scatter matrix, finally the speckle reduction method based on kernel regression for SAR image is proposed. The experiment results show that the proposed method can reduce speckle noise while preserving targets and edges by introducing magnitude information and scatter matrix into kernel function.