﻿ 全极化雷达遥感影像的迭代优化非局部均值去噪法
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Polarimetric radar image despeckling by iteratively refined nonlocal means
MA Xiaoshuang, WU Penghai
Department of Resources and Environmental Engineering, Anhui University, Hefei 230601, China
Abstract: 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.
Key words: polarimetric synthetic aperture radar    speckle filtering    nonlocal means    likelihood-ratio test

1 非局部均值及PolSAR相干斑统计特性 1.1 非局部均值去噪思想

NLM去噪算法滤波过程的通式可表示为

(1)

 图 1 NLM算法的思路 Fig. 1 The diagram of the idea of nonlocal means

1.2 PolSAR相干斑统计特性

(2)

2 迭代优化的PolSAR NLM去噪算法

2.1 迭代优化的PolSAR影像块相似性度量

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

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(13)

2.2 权重及滤波参数的设置

(14)

(15)

3 试验结果和分析

3.1 模拟试验

ENL指数的定义为

(16)

EPD-ROA的定义如下

(17)

(18)

 图 2 模拟影像去噪结果 Fig. 2 Filtering results on the simulated image

 评价指数 增强Lee滤波 IDAN PretestNLM 本文算法 ENL 24.4 42.7 82.3 80.7 EPD-ROA 0.78 0.70 0.88 0.90 ARBH 0.199 0.187 0.154 0.150 ARBα 0.275 0.280 0.223 0.237 ARBA 0.240 0.246 0.183 0.171

3.2 真实试验

 图 3 C波段AirSAR影像去噪结果 Fig. 3 Filtering results on the C band AirSAR image

 图 4 L波段AirSAR影像去噪结果 Fig. 4 Filtering results on the L band AirSAR image

 滤波算法 C波段影像 L波段影像 ENL EPD-ROA ENL EPD-ROA 增强Lee滤波 42.3 0.71 39.4 0.77 IDAN 60.9 0.70 50.4 0.75 Pretest NLM 103.5 0.78 88.8 0.84 本文算法 97.8 0.84 76.5 0.89

4 结论

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http://dx.doi.org/10.11947/j.AGCS.2019.20180034

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#### 文章信息

MA Xiaoshuang, WU Penghai

Polarimetric radar image despeckling by iteratively refined nonlocal means

Acta Geodaetica et Cartographica Sinica, 2019, 48(8): 1038-1045
http://dx.doi.org/10.11947/j.AGCS.2019.20180034