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InSAR interference phase filtering based on joint EEMD–KECA algorithm

  • role： First author第一作者
  • Affiliation：

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

  • Email：yuj2011@whu.edu.cn
  • Introduction：余洁，1964年生，女，教授，研究方向为遥感影像处理与应用及空间数据分析。E-mail：yuj2011@whu.edu.cn
Jie YU134,  
  • Affiliation：

    College of Computer, Hubei University of Education, Wuhan 430205, China

Limin LIU2,  
  • Affiliation：

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

Xiaojuan LI134,  
  • Affiliation：

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

Lin ZHU134,  
  • Affiliation：

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

Donghai XIE134,  
  • Affiliation：

    State Key Laboratory Incubation Base of Urban Environmental Processes and Digital Simulation, Capital Normal University, Beijing 100048, China

    Beijing Key Laboratory of Resources Environment and Geographic Information System, Capital Normal University, Beijing 100048, China

    College of Resource Environment and Tourism, Capital Normal University, Beijing 100048, China

Mi CHEN134

résumé

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.

mots-clés

EEMD algorithm;modal aliasing;KECA;noise separation;INSAR interferogram;filtering

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