基于融合距离的极化SAR图像非局部均值滤波

doi:10.12305/j.issn.1001-506X.2024.05.04

摘要/Abstract

摘要：

在极化合成孔径雷达(synthetic aperture radar, SAR)图像降噪领域, 常见的非局部均值滤波仅依靠像素间的统计距离进行相似性度量, 忽略了像素点的空间信息。本文结合极化SAR数据统计特性和图像空间特征作为像素间的相似性度量, 提出了一种利用融合距离来计算相邻窗口权重的方法——基于融合距离的非局部均值滤波器。融合距离的引入使得滤波器能够更全面的评估像素间的相似性, 从而得到更合适的像素权重。此外, 本方法还引进变异系数对邻域窗口的权重进行评估, 通过该参数可以控制滤波的程度。在多幅极化SAR图像上的实验结果表明, 所提出的滤波器能够在有效抑制斑点噪声的同时保留较为完整的图像边缘信息和极化散射特性。

关键词: 极化合成孔径雷达, 非局部均值滤波, 相似性度量, 变异系数

Abstract:

In the field of polarimetric synthetic aperture radar (SAR) image denoising, the common nonlocal means (NLM) filter only relies on the statistical distance between pixels to measure the similarity and ignores the spatial information of them. This study combines the statistical characteristics of polarimetric SAR data and image spatial features as similarity measures between pixels, and proposes a method for calculating adjacent window weights using fusion distance, which names NLM filter based on fusion distance (FD-NLM). The introduction of fusion distance enables the filter to comprehensively evaluate the similarity between pixels, thereby obtaining more appropriate pixel weights. In addition, this method also employs the coefficient of variation (CV) to evaluate the weight of neighborhood windows, and using this parameter to control the filtering degree. The experimental results on multiple polarimetric SAR images show that the proposed filter can effectively suppress speckle noise while retaining relatively complete image edge information and polarization scattering characteristics.

Key words: polarimetric synthetic aperture radar (SAR), nonlocal means (NLM) filter, similarity measure, coefficient of variation (CV)

中图分类号:

TN958

曾顶, 殷君君, 杨健. 基于融合距离的极化SAR图像非局部均值滤波[J]. 系统工程与电子技术, 2024, 46(5): 1493-1502.

Ding ZENG, Junjun YIN, Jian YANG. Nonlocal means filter for polarimetric SAR images based on fusion distance[J]. Systems Engineering and Electronics, 2024, 46(5): 1493-1502.

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滤波类型	实验1				实验2
滤波类型	ENL	EPI	标准差	SSIM	ENL	EPI	标准差	SSIM
原始数据	0.828 5	1.000 0	0.303 9	1.000 0	0.986 9	1.000 0	0.202 0	1.000 0
Pretest滤波	4.586 7	0.631 8	0.215 2	0.530 6	5.239 0	0.678 4	0.090 6	0.688 4
改进Lee滤波	3.797 2	0.506 0	0.181 8	0.516 9	4.512 1	0.463 3	0.080 1	0.693 9
SNLL距离滤波	5.071 1	0.540 2	0.136 3	0.559 8	5.699 3	0.523 4	0.084 8	0.685 0
所提滤波	5.808 5	0.551 4	0.123 9	0.656 9	8.027 6	0.629 5	0.073 5	0.719 3

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