Extracting sparse crack features from correlated background in ground penetrating radar concrete imaging using robust principal component analysis technique

Yu Zhang; Tian Xia

doi:10.1117/12.2218657

8 April 2016 Extracting sparse crack features from correlated background in ground penetrating radar concrete imaging using robust principal component analysis technique

Yu Zhang, Tian Xia

Author Affiliations +

Proceedings Volume 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016; 980402 (2016) https://doi.org/10.1117/12.2218657
Event: SPIE Smart Structures and Materials + Nondestructive Evaluation and Health Monitoring, 2016, Las Vegas, Nevada, United States

Abstract

Crack detection is an important application for Ground penetrating radar (GPR) to examine the concrete road or building structure conditions. The layer of rebars or utility pipes that typically exist inside the concrete structure can generate stronger scattering than small concrete cracks to affect detection effectiveness. In GPR image, the signature patterns of regularly distributed rebars or pipes can be deemed as correlated background signals, while for the small size cracks, their image features are typically irregularly and sparsely distributed. To effectively detect the cracks in concrete structure, the robust principal component analysis algorithm is developed to characterize the rank and sparsity of GPR image. For performance evaluations, simulations are conducted with various configurations.

Conference Presentation

Citation Download Citation

Yu Zhang and Tian Xia "Extracting sparse crack features from correlated background in ground penetrating radar concrete imaging using robust principal component analysis technique", Proc. SPIE 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, 980402 (8 April 2016); https://doi.org/10.1117/12.2218657

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