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斑点噪声是由合成孔径雷达(SAR)的相干成像原理造成的固有缺陷,为了更好地进行SAR图像地形分类、目标检测等后续处理,提出一种用于极化SAR图像的滤波方法。该方法利用新的优化函数和约束条件,在保持图像功率等于L ee滤波功率的前提下,通过最小化信号子空间向量与原始向量的欧式距离,达到降斑的目的。实验结果表明:利用旧金山地区的真实极化SAR数据,使用该方法滤波的图像结果与原有的子空间滤波器相比,更接近原始图像的均值,并且通过滤波提高了不同类别的目标在特征空间的区分度,从而有利于不同类型地物的分类。
Speckle noise is an inherent defect caused by the coherent imaging principle of Synthetic Aperture Radar (SAR). In order to better perform terrain classification and target detection of SAR images, a filtering method for polarimetric SAR images is proposed. This method uses the new optimization functions and constraints to achieve speckle reduction by minimizing the European distance between the signal subspace vector and the original vector while keeping the image power equal to the L ee filtering power. The experimental results show that using the real-world polarized SAR data in San Francisco area, the results of the image filtering using this method are closer to the mean value of the original image than the original subspace filter, and the filtering performance of the different types of targets in the feature The degree of differentiation of space, which is conducive to the classification of different types of features.