全极化雷达遥感影像的迭代优化非局部均值去噪法

相干斑的存在严重降低了全极化合成孔径雷达(polarimetric synthetic aperture radar,PolSAR)的影像质量,对相干斑进行抑制是使用PolSAR数据必不可少的预处理程序。本文提出了一种迭代优化的PolSAR非局部均值去噪方法。该方法在每次迭代去噪过程中,通过同时考虑原始影像全极化噪声统计特性和前一次迭代所得影像的全极化信息来完善像素间极化相似性的度量,从而实现对影像更精准的估计。试验部分利用模拟的PolSAR数据和真实的PolSAR影像进行了算法效果的验证。结果表明:去噪算法在显著抑制影像噪声水平的同时,也能较好地保持影像的边缘和极化特性等细节信息。

Polarimetric radar image despeckling by iteratively refined nonlocal means

The presence of speckle degrades the quality of the polarimetric synthetic aperture radar (PolSAR) image, hence despeckling is an essential procedure before using SAR images to obtain land-cover information in most cases. In this paper, a PolSAR filtering method based on iteratively refined nonlocal means is presented. In each iteration step of the proposed method, by considering both the statistical trait of the original image and the information of the image obtained in last iteration, the polarimetric similarity between pixels is refined, so as to improve the estimation results. Experiments on a simulated PolSAR image and two real PolSAR images revealed the positive despeckling performances of our proposed method:the speckle is reduced to a large degree, and the image details, such as the edges and the polarimetric traits, are effectively preserved.