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

Abstract

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)

CLC Number:

TN958

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.

Figures/Tables 8

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References 31

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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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Nonlocal means filter for polarimetric SAR images based on fusion distance

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