基于同质像素预选择的极化SAR图像非局部均值滤波

doi:10.11999/JEIT150314

Abstract
Figure/Table
References (22)
Related Citation (15)

Download: PDF (30920 KB)
Export: BibTeX | EndNote (RIS)

Abstract

A Non Local Means (NLM) filtering based on Homogeneous Pixels Preselection (NLM-HPP) is proposed to solve the problem of preserving structural feature and polarimetric scattering properties in speckle reduction of Polarimetric SAR (PolSAR) images. Firstly, this method combines statistical property and polarimetric scattering mechanism to select homogeneous pixels in the filtering process. Secondly, the loss function of structure is introduced to improve the accuracy of similarity measure between pixels in NLM method. Finally, it averages the covariance matrices of homogeneous pixels with the weights according to the refined similarity measure, inducing efficient reduction of the speckle in PolSAR images. The implementation results on real PolSAR images, compared with the Refined Lee filter, Scattering-Model-Based speckle filter and two kinds of Non Local Means filter, demonstrate that the proposed method can reduce speckle effectively, and further retain structural information and polarimetric information in PolSAR images.

Key words： Polarimetric SAR (PolSAR) Non Local Means (NLM) Structure preserving Polarimetric information Homogeneous pixels preselection

Received: 17 March 2015 Published: 01 November 2015

PACS:

TP751

Fund:

The National Natural Science Foundation of China (61371154, 41076120, 61271381, 61102154); The Jointly funded Project for Aerospace Science Foundation and Key Laboratory of Photoelectric Control Technology (201301P4007); The Fundameutal Research Fund for the Central Universities of China (2012HGCX0001)

Corresponding Authors: Yang Xue-zhi E-mail: hfut.cv@gmail.com

	Service

	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Yang Xue-zhi
	Chen Jing
	Zhou Fang
	Lang Wen-hui
	Zheng Xin
	Li Guo-qiang

Cite this article:

Yang Xue-zhi,Chen Jing,Zhou Fang等. Polarimetric SAR Image Despeckling Using Non Local Means Filter Based on Homogeneous Pixels Preselection[J]. JEIT, 2015, 37(12): 2991-2999.

URL:

http://jeit.ie.ac.cn/EN/10.11999/JEIT150314 OR http://jeit.ie.ac.cn/EN/Y2015/V37/I12/2991

[1]	Alonso-González A, López-Martínez C, and Salembier P. Filtering and segmentation of polarimetric SAR data based on binary partition trees[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012, 50(2): 593-605.
[2]	周晓光, 匡纲要, 万建伟. 多极化SAR图像斑点抑制综述[J]. 中国图象图形学报, 2008, 13(3): 377-385.
	Zhou Xiao-guang, Kuang Gang-yao, and Wan Jian-wei. A review of polarimetric SAR speckle reduction[J]. Journal of Image and Graphics, 2008, 13(3): 377-385.
[3]	Lee J S, Grunes M R, Schuler D L, et al.. Scattering- model-based speckle filtering of polarimetric SAR data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2006, 44(1): 176-187.
[4]	Lee J S, Grunes M R, and De Grandi G. Polarimetric SAR speckle filtering and its implication for classification[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(5): 2363-2373.
[5]	Deledalle C A, Denis L, and Tupin F. Iterative weighted maximum likelihood denoising with probabilistic patch-based weights[J]. IEEE Transactions on Image Processing, 2009, 18(12): 2661-2672.
[6]	Liu G and Zhong H. Nonlocal means filter for polarimetric SAR data despeckling based on discriminative similarity measure[J]. IEEE Geoscience and Remote Sensing Letters, 2014, 11(2): 514-518.
[7]	Chen J, Chen Y, An W, et al.. Nonlocal filtering for polarimetric SAR data: A pretest approach[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(5): 1744-1754.
[8]	Lee J S, Ainsworth T L, Wang Y, et al.. Polarimetric SAR speckle filtering and the extended sigma filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2015, 53(3): 1150-1160.
[9]	易子麟, 尹东, 胡安洲, 等. 基于非局部均值滤波的SAR图像去噪[J]. 电子与信息学报,2012, 34(4): 950-955.
	Yi Z L, Yin D, Hu A Z, et al.. SAR image despeckling based on non-local means filter[J]. Journal of Electronics and Information Technology, 2012, 34(4): 950-955.
[10]	Buades A, Coll B, and Morel J M. A non-local algorithm for image denoising[C]. IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, USA, 2005, 2: 60-65.
[11]	钟莹, 杨学志, 唐益明, 等. 采用结构自适应块匹配的非局部均值去噪算法[J]. 电子与信息学报, 2013, 35(12): 2908-2915.
	Zhong Y, Yang X Z, Tang Y M, et al.. Non-local means denoising derived from structure-adapted block matching[J]. Journal of Electronics & Information Technology, 2013, 35(12): 2908-2915.
[12]	Torres L, Sant'Anna S J S, da Costa Freitas C, et al.. Speckle reduction in polarimetric SAR imagery with stochastic distances and nonlocal means[J]. Pattern Recognition, 2014, 47(1): 141-157.
[13]	Zhong H, Zhang J, and Liu G. Robust polarimetric SAR despeckling based on nonlocal means and distributed Lee filter[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014, 52(7): 4198-4210.
[14]	Deledalle C A, Tupin F, and Denis L. Polarimetric SAR estimation based on non-local means[C]. IEEE International on Geoscience and Remote Sensing Symposium, Hawaii, USA, 2010: 2515-2518.
[15]	D'Hondt O, Guillaso S, and Hellwich O. Iterative bilateral filtering of polarimetric SAR data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2013, 6(3): 1628-1639.
[16]	Deledalle C A, Denis L, Poggi G, et al.. Exploiting patch similarity for SAR image processing: the nonlocal paradigm [J]. IEEE Signal Processing Magazine, 2014, 31(4): 69-78.
[17]	Lee J S, Wen J H, Ainsworth T L, et al.. Improved sigma filter for speckle filtering of SAR imagery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(1): 691-707.
[18]	Dellepiane S G and Angiati E. Quality assessment of despeckled SAR images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(2): 691-70.
[19]	Hebar M, Gleich D, and Cucej Z. Autobinomial model for SAR image despeckling and information extraction[J]. IEEE Transactions on Geoscience and Remote Sensing, 2009, 47(8): 2818-2835.
[20]	Ding Z, Zeng T, Dong F, et al.. An improved PolSAR image speckle reduction algorithm based on structural judgment and hybrid four-component polarimetric decomposition[J]. IEEE Transactions on Geoscience and Remote Sensing, 2013, 51(8): 4438-4449.
[21]	Feng H, Hou B, and Gong M. SAR image despeckling based on local homogeneous-region segmentation by using pixel-relativity measurement[J]. IEEE Transactions on Geoscience and Remote Sensing, 2011, 49(7): 2724-2737.
[22]	Lee J S, Grunes M R, Pottier E, et al.. Unsupervised terrain classification preserving polarimetric scattering characteristics[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004, 42(4): 722-731.