A Small-Baseline InSAR Inversion Algorithm Combining a Smoothing Constraint and L1-Norm Minimization

被引:12
|
作者
Wang, Jili [1 ,2 ]
Deng, Yunkai [2 ]
Wang, Robert [2 ]
Ma, Peifeng [3 ,4 ]
Lin, Hui [3 ]
机构
[1] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[2] Chinese Acad Sci, Inst Elect, Space Microwave Remote Sensing Syst Dept, Beijing 100190, Peoples R China
[3] Chinese Univ Hong Kong, Inst Space & Earth Informat Sci, Hong Kong, Peoples R China
[4] Minist Land & Resources, Key Lab Urban Land Resources Monitoring & Simulat, Shenzhen 518034, Peoples R China
关键词
Deformation monitoring; small baseline (SB); synthetic aperture radar interferometry (InSAR);
D O I
10.1109/LGRS.2019.2893422
中图分类号
P3 [地球物理学]; P59 [地球化学];
学科分类号
0708 ; 070902 ;
摘要
Atmospheric artifacts and phase unwrapping errors have unfavorable effects on differential synthetic aperture radar interferometry (DInSAR) deformation monitoring. In this letter, we present an alternative small-baseline DInSAR inversion algorithm, velocity-constraint L-1-norm minimization. The proposed algorithm improves the robustness of time series deformation estimation by combining a smoothing constraint and L-1-norm minimization. The smoothing constraint can minimize temporal atmospheric artifacts, and the L-1-norm minimization outperforms L-2-norm minimization in the presence of phase unwrapping errors. The iteratively reweighted least square algorithm is employed to adjust the weights of DInSAR observations and smoothing constraints in L-1-norm minimization. The proposed algorithm is validated using simulated data and TerraSAR-X data. The experimental results show that the proposed algorithm is suitable for the inversion of approximately linear deformation processes affected by both atmospheric artifacts and unwrapping errors.
引用
收藏
页码:1061 / 1065
页数:5
相关论文
共 50 条
  • [1] InSAR Deformation Time Series Using an L1-Norm Small-Baseline Approach
    Lauknes, Tom R.
    Zebker, Howard A.
    Larsen, Yngvar
    IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2011, 49 (01): : 536 - 546
  • [2] SMOOTHING APPROXIMATIONS FOR LEAST SQUARES MINIMIZATION WITH L1-NORM REGULARIZATION FUNCTIONAL
    Nkansah, Henrietta
    Benyah, Francis
    Amankwah, Henry
    INTERNATIONAL JOURNAL OF ANALYSIS AND APPLICATIONS, 2021, 19 (02): : 264 - 279
  • [3] L1-NORM MINIMIZATION FOR OCTONION SIGNALS
    Wang, Rui
    Xiang, Guijun
    Zhang, Fagan
    PROCEEDINGS OF 2016 INTERNATIONAL CONFERENCE ON AUDIO, LANGUAGE AND IMAGE PROCESSING (ICALIP), 2016, : 552 - 556
  • [4] A Laplacian approach to l1-norm minimization
    Bonifaci, Vincenzo
    COMPUTATIONAL OPTIMIZATION AND APPLICATIONS, 2021, 79 (02) : 441 - 469
  • [5] AN ALGORITHM FOR CLUSTERING USING L1-NORM BASED ON HYPERBOLIC SMOOTHING TECHNIQUE
    Bagirov, Adil M.
    Mohebi, Ehsan
    COMPUTATIONAL INTELLIGENCE, 2016, 32 (03) : 439 - 457
  • [6] Linearized alternating directions method for l1-norm inequality constrained l1-norm minimization
    Cao, Shuhan
    Xiao, Yunhai
    Zhu, Hong
    APPLIED NUMERICAL MATHEMATICS, 2014, 85 : 142 - 153
  • [7] Signal reconstruction by conjugate gradient algorithm based on smoothing l1-norm
    Wu, Caiying
    Zhan, Jiaming
    Lu, Yue
    Chen, Jein-Shan
    CALCOLO, 2019, 56 (04)
  • [8] Noise attenuation based on L1-norm constraint inversion in seismic while drilling
    Liu Q.
    Meitan Xuebao/Journal of the China Coal Society, 2021, 46 (08): : 2699 - 2705
  • [9] Seismic Acoustic Impedance Inversion Using Reweighted L1-Norm Sparse Constraint
    He, Liangsheng
    Wu, Hao
    Wen, Xiaotao
    You, Jiachun
    IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2022, 19
  • [10] A NEW ALGORITHM FOR NONLINEAR L1-NORM MINIMIZATION WITH NONLINEAR EQUALITY CONSTRAINTS
    SOLIMAN, SA
    CHRISTENSEN, GS
    ROUHI, AH
    COMPUTATIONAL STATISTICS & DATA ANALYSIS, 1991, 11 (01) : 97 - 109