InSAR interference phase filtering based on joint EEMD–KECA algorithm

Jie YU; Limin LIU; Xiaojuan LI; Lin ZHU; Donghai XIE; Mi CHEN

doi:10.11834/jrs.20197272

Technology and Methodology | Views : 0 下载量: 239 CSCD: 2 更多指标

PDF
Export
Share
Collection
Album

InSAR interference phase filtering based on joint EEMD–KECA algorithm
Vol. 23, Issue 1, Pages: 78-88(2019)
Published： 2019-1 ，

Accepted： 4 March 2018
DOI： 10.11834/jrs.20197272

扫描看全文

余洁, 刘利敏, 李小娟, 朱琳, 谢东海, 陈蜜. 2019. 联合EEMD-KECA算法的InSAR干涉相位时频滤波. 遥感学报, 23(1): 78–88

Yu J, Liu L M, Li X J, Zhu L, Xie D H and Chen M. 2019. InSAR interference phase filtering based on joint EEMD–KECA algorithm. Journal of Remote Sensing, 23(1): 78–88
余洁, 刘利敏, 李小娟, 朱琳, 谢东海, 陈蜜. 2019. 联合EEMD-KECA算法的InSAR干涉相位时频滤波. 遥感学报, 23(1): 78–88 DOI： 10.11834/jrs.20197272.

Yu J, Liu L M, Li X J, Zhu L, Xie D H and Chen M. 2019. InSAR interference phase filtering based on joint EEMD–KECA algorithm. Journal of Remote Sensing, 23(1): 78–88 DOI： 10.11834/jrs.20197272.

摘要

根据干涉图信号和噪声时频分布差异的特点，提出一种改进的基于经验模态分解EEMD的InSAR干涉相位滤波方法。该方法首先利用可有效降低模态混叠的EEMD算法，对干涉图的实部及虚部分别进行2维经验模态分解，获得具有不同时间尺度的模态分量；然后根据信号和噪声分量的时间尺度分布特性的差异，采用适用于非线性信号分析的KECA算法对噪声识别、分离；最后利用去除噪声后的模态分量重构干涉图。为了证明本文方法的有效性，分别利用模拟数据及真实InSAR差分干涉相位进行滤波试验。对比本文EEMD-KECA滤波方法、Goldstein滤波、圆周期—中值滤波、EMD分解、EMD-PCA方法的滤波效果，采用相干斑指数、均方差指数、边缘保持指数进行定量评价。结果表明，与经典InSAR干涉图滤波方法相比，本文联合EEMD-KECA算法的滤波方法能有效滤除干涉图噪声，且在条纹边缘等细节信息的保持上也具有较大优势。

Abstract

Interferogram filtering is an important procedure in multi-temporal INSAR analysis and application in monitoring regional surface deformation. Researchers have introduced a series of filtering methods

such as Empirical Mode Decomposition (EMD) filter

based on time-frequency analysis to determine the characteristics of the interferometric phase for non-stationary signals. Given the differences in time-frequency distribution between signal and noise of interferogram

InSAR interference phase filtering method based on EMD is proposed and effectively applied in InSAR interferogram filtering with improved capability of phase noise filtering and detail preserving in edge area. However

in existing EMD filtering methods

some high-frequency modal components are eliminated as noise directly without objective assessment. The mode-mixing problem of EMD also influences the filtering effect. To address the problems of interferometry phase filtering in EMD

a newly improved InSAR interference phase filtering method based on EMD and noise-screening assessment is proposed. In the new algorithm

ensemble EMD (EEMD) is applied to obtain modal components of different time scales to the two-dimensional real and imaginary parts of the interferogram by fully utilizing the capability of EEMD in effectively reducing modal aliasing. Then

Kernel Entropy Component Analysis (KECA) algorithm is used for nonlinear analysis of signal noise identification and separation. Finally

interferogram is reconstructed with modal components after noise elimination. After the decomposition and reconstruction of mixed signals

the noise in the interferogram is effectively filtered out. The performance of the proposed filtering method is tested on simulated data and real SAR images by comparing the filtering effects of Goldstein filter

periodic-median filter

EMD

and EMD–PCA filter with the proposed EEMD–KECA filter. Speckle

variance

and edge-keeping indexes are used for quantitative evaluation. Unlike the classical InSAR interferogram filtering

the proposed method combined with EEMD–KECA filtering algorithm can effectively filter out the interference noise figure and has an advantage in maintaining the fringe edge details. In the proposed method

EEMD is used to decompose interferogram signal instead of EMD

and KECA is applied to evaluate the phase noise quantitatively. The proposed method can achieve good filtering results while preserving the advantages of strong edge detail maintenance and can avoid the incomplete and partly filtering of image information caused by modal aliasing. Experimental results show that the proposed method not only can obtain excellent filtering performance in the slowly changing region of fringe but also has excellent filtering effect and high phase fidelity in the dense fringe area and phase edge details. The proposed filtering method is suitable for interference fringes of different densities.

关键词

EEMD模态混叠KECA噪声分离INSAR干涉图滤波

Keywords

EEMD algorithmmodal aliasingKECAnoise separationINSAR interferogramfiltering

references

Abdallah W B and Abdelfattah R. 2015. Two-dimensional wavelet algorithm for interferometric synthetic aperture radar phase filtering enhancement. Journal of Applied Remote Sensing, 9(1): 096061

段克清, 向家彬, 汪枫. 2005. InSAR相位条纹图的加权圆周期中值滤波算法. 空军雷达学院学报, 19(1): 4–6

Duan K Q, Xiang J B and Jiang F. 2005. An algorithm of weighted periodic pivoting median filtering for InSAR phase fringe. Journal of Air Force Radar Academy, 19(1): 4–6 (

Eichel P H and Ghiglia D C. 1993. Spotlight SAR interferometry for terrain elevation mapping and interferometric change detection. Sand: Sandia National Labs Technology: 2529–2546

Flandrin P, Rilling G and Goncalves P. 2004. Empirical mode decomposition as a filter bank. IEEE Signal Processing Letters, 11(2): 112–114

Goldstein R M and Werner C L. 1998. Radar interferogram filtering for geophysical applications. Geophysical Research Letters, 25(21): 4035–4038

郭际明, 黄长军, 喻小东, 聂智平. 2014. 利用BEMD-自适应滤波去除SAR干涉图噪声. 武汉大学学报•信息科学版, 39(4): 422–427

Guo J M, Huang C J, Yu X D and Nie Z P. 2014. Interferogram denoising method based on BEMD and adaotive filter. Geomatics and Information Science of Wuhan University, 39(4): 422–427 (

黄长军, 郭际明, 喻小东, 袁长征. 2013. 干涉图EMD-自适应滤波去噪法. 测绘学报, 42(5): 707–714

Huang C J, Guo J M, Yu X D and Yuan C Z. 2013. The study of interferogram denoising method based on EMD and adaptive filter. Acta Geodaetica et Cartographica Sinica, 42(5): 707–714 (

黄丽瑾, 施俊, 钟瑾. 2012. 基于核熵成分分析的数据降维. 计算机工程, 38(2): 175–177

Huang L J, Shi J and Zhong J. 2012. Data dimension reduction based on kernel entropy component analysis. Computer Engineering, 38(2): 175–177 (

Jenssen R. 2010. Kernel entropy component analysis. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(5): 847–860

Lanari R, Fornaro G, Riccio D, Migliaccio M, Papathanassiou K P, Moreira J R, Schwabisch M, Dutra L, Puglisi G, Franceschetti G and Coltelli M. 1996. Generation of digital elevation models by using SIR-C/X-SAR multifrequency two-pass interferometry: the Etna case study. IEEE Transaction on Geoscience and Remote Sensing, 34(5): 1097–1114

Lee J S, Papathanassiou K P, Ainsworth T L, Grunes M R and Reigber A. 1998. A new technique for noise filtering of SAR interferometric phase images. IEEE Transactions on Geoscience and Remote Sensing, 36(6): 1456–1465

Li F F, Hu D H and Ding C B. 2013. InSAR Phase Noise Reduction Based on Empirical Mode Decomposition. IEEE Geoscience and Remote Sensing Letters, 10(5): 1180–1184

李佳, 李志伟, 丁晓利, 朱珺和汪长城. 2011. 强噪声SAR干涉图的等值线中值——Goldstein二级滤波. 遥感学报, 15(4): 750–765

Li J, Li Z W, Ding X L, Zhu J and Wang C C. 2011. Filtering strong noisy synthetic aperture radar (SAR) interferogram with integrated Contoured Median. Journal of Remote Sensing, 15(4): 750–765 (

廖明生, 林珲, 张祖勋, 杨文和张力. 2003. INSAR干涉条纹图的复数空间自适应滤波. 遥感学报, 7(2): 98–105

Liao M S, Lin H, Sun Z X, Yang W and Zhang L. 2003. Adaptive algorithm for filtering interferometric phase noise. Journal of Remote Sensing, 7(2): 98–105 (

Song R, Guo H D, Liu G, Perski Z and Fan J H. 2014a. Improved Goldstein SAR interferogram filter based on empirical mode decomposition. IEEE Geoscience and Remote Sensing Letters, 11(2): 399–403

Song R, Guo H D, Liu G, Perski Z, Yue H Y, Han C M and Fan J H. 2014b. SAR interferometric phase filtering technique based on bivariate empirical mode decomposition. Remote Sensing Letters, 5(8): 743–752

Song R, Guo H D, Liu G, Perski Z, Yue H Y, Han C M and Fan J H. 2015. Improved Goldstein SAR interferogram filter based on adaptive-neighborhood technique. IEEE Geoscience and Remote Sensing Letters, 12(1): 140–144

孙豆, 邢世其, 李永帧和代大海. 2017. SAR稀疏成像模型参数自适应选择. 遥感学报, 21(4): 579–587

Sun D, Xing S Q, Li Y Z and Dai D H. 2017. Adaptive parameter selection of SAR sparse imaging model. Journal of Remote Sensing, 21(4): 579–587 (

王文波, 赵攀, 张晓东. 2012. 利用经验模态分解和主成分分析的SAR图像相干斑抑制. 测绘学报, 41(6): 838–843

Wang W B, Zhao P and Zhang X D. 2012. Research on SAR image speckle reduction using EMD and principle component analysis. Acta Geodaetica et Cartographical Sinca, 41(6): 838–843 (

Wang Y, Huang H F, Dong Z and Wu M Q. 2015. A dual-domain interferometric phase filter. Remote Sensing Letters, 6(12): 962–971

Wu Z H and Huang N E. 2009. Ensemble empirical mode decomposition: a noise-assisted data analysis method. Advances in Adaptive Data Analysis, 1(1): 1–41

杨晨, 黄丽亚, 文念, 杨俊宇. 2015. 基于二维集合经验模式分解的稳态视觉诱发电位目标检测研究. 生物医学工程学杂志, 32(3): 508–513

Yang C, Huang L Y, Wen N and Yang J Y. 2015. Study on steady state visual evoked potential target detection based on two-dimensional ensemble empirical mode decomposition. Journal of Biomedical Engineering, 32(3): 508–513 (

于晶涛, 陈鹰. 2004. 一种新的INSAR干涉条纹图滤波方法. 测绘学报, 33(2): 121–126

Yu J T and Chen Y. 2004. A new filter on INSAR interferogram fringe. Acta Geodaetica et Cartographica Sinica, 33(2): 121–126 (

俞晓莹．2012．改进的SBAS地表形变监测及地下水应用研究．长沙: 中南大学

Yu X Y. 2012. Improved SBAS Technology for Land Deformation Detection and Groundwater Applicaton.Changsha: Central South University

Alert me when the article has been cited

提交

Noise estimation of hyperspectral image in the spatial and spectral dimensions

Nonlocal means filtering for polarimetric SAR images based on heterogeneity

Speckle filtering for polarimetric SAR data based on self-cross bilateral filter

Identification and estimation of InSAR interferogram noise based on wavelet transform

Optimal integration-based adaptive direction filter for InSAR interferogram