Share Email Print
cover

Proceedings Paper

Extracting sparse crack features from correlated background in ground penetrating radar concrete imaging using robust principal component analysis technique
Author(s): Yu Zhang; Tian Xia
Format Member Price Non-Member Price
PDF $14.40 $18.00
cover GOOD NEWS! Your organization subscribes to the SPIE Digital Library. You may be able to download this paper for free. Check Access

Paper 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.

Paper Details

Date Published: 8 April 2016
PDF: 9 pages
Proc. SPIE 9804, Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016, 980402 (8 April 2016); doi: 10.1117/12.2218657
Show Author Affiliations
Yu Zhang, Univ. of Vermont (United States)
Tian Xia, Univ. of Vermont (United States)


Published in SPIE Proceedings Vol. 9804:
Nondestructive Characterization and Monitoring of Advanced Materials, Aerospace, and Civil Infrastructure 2016
Tzuyang Yu; Andrew L. Gyekenyesi; Peter J. Shull; H. Felix Wu, Editor(s)

© SPIE. Terms of Use
Back to Top