基于无监督分类的多视极化SAR相干斑滤波

Multi-look Polarimetric SAR Speckle Filtering Based on Unsupervised Classification

相干斑噪声是引起SAR图像降质的主要原因之一。多视极化白化滤波器(MPWF)是一种专门应用于多视极化SAR图像降噪的有效技术。其中,滤波器参数估计的精确度直接决定了其滤波性能的好坏。对此,该文提出了一种新的基于无监督分类的自适应窗算法。该算法以分类图像作为对象;在滑动矩形窗内以中心像素作为参照物,自动搜索与其同类的像素并用于MPWF参数估计。实验结果表明,与其他几种典型的算法相比,该法不仅有效地抑制了相干斑,而且对图像的纹理信息具有很好的保持能力。

The presence of speckle is a major cause of degradation in SAR images. The Multilook Polarimetric Whitening Filter （MPWF） is an effective method on speckle reduction in multilook polarimetric Synthetic Aperture Radar （SAR） images. However, the capability of the filter is directly decided with the precision of the filter-parameter estimation. Hence, a novel adaptive windowing algorithm based on unsupervised classification is proposed here, where the classified image is chosen as the processed object and the central pixel in moving rectangular window is as the reference. Then through automatic search, the pixels around the central pixel which are in the same class are selected and used for parameter estimation. Compared to some other typical methods, this approach is demonstrated the effectiveness both on speckle reduction and preservation of texture information from the experimental results.