Clutter Removal Method for GPR Based on Low-Rank and Sparse Decomposition With Total Variation Regularization

被引：8

作者：

Zhao, Yi ^{[1
,2
]}

Yang, Xiaopeng ^{[1
,2
]}

Qu, Xiaodong ^{[1
,2
]}

Lan, Tian ^{[3
]}

Gong, Junbo ^{[3
]}

机构：

[1] Beijing Inst Technol, Sch Informat & Elect, Minist Educ, Beijing 100081, Peoples R China

[2] Beijing Inst Technol, Key Lab Elect & Informat Technol Satellite Nav, Minist Educ, Beijing 100081, Peoples R China

[3] Beijing Inst Technol, Chongqing Innovat Ctr, Chongqing 401120, Peoples R China

来源：

IEEE GEOSCIENCE AND REMOTE SENSING LETTERS | 2023年 / 20卷

基金：

中国国家自然科学基金;

关键词：

Clutter; Sparse matrices; Soil; TV; Optimization; Numerical simulation; Matrix decomposition; Clutter removal; ground penetrating radar (GPR); low-rank and sparse decomposition (LRSD); total variation regularization (TVR); REDUCTION; RECOVERY;

D O I：

10.1109/LGRS.2023.3250717

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：

The performance of ground penetrating radar (GPR) target detection is seriously affected by the clutter. In this letter, an effective GPR clutter removal method is proposed based on low-rank and sparse decomposition with total variation regularization (LRSD-TVR). In the proposed method, a total variation (TV) regularization of sparse matrix is introduced to further remove the remaining clutter and to obtain a clearer target image. An iterative approach based on alternating direction method of multipliers (ADMM) is developed to solve the optimization problem of LRSD-TVR. In each iteration, the low-rank component, which corresponds to the clutter, is computed by singular value decomposition (SVD) thresholding. Besides, the sparse component corresponding to the target is obtained by solving the suboptimization problem reformulated in terms of TV component. The effectiveness of proposed method is verified by both numerical simulations and field experiments.

引用

页数：5

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