Road subsurface distress recognition method using multiattribute feature fusion with ground penetrating radar

ABSTRACT

Ground penetrating radar (GPR) has been a widely used nondestructive testing method for pavement structure conditions. Random scattering occurs when GPR waves propagate, leading to complex reflected signals with a large amount of clutter from medium anomalies. The original GPR amplitude profile cannot fully display the physical parameters and geometric dimensions of the subsurface medium anomalies, which could lead to misjudgment and omission of the subsurface distress. To overcome these challenges, we obtain the instantaneous frequency, instantaneous phase, and instantaneous amplitude characteristics of the signal by the Hilbert time–frequency transform of GPR data. Then, a fusion framework was proposed to combine the original profiles with the abovementioned three features based on a two-dimensional wavelet transform. In addition, the information entropy (IE) and Laplacian operator gradient (LG) values are finally utilized to evaluate the effectiveness of the proposed fusion method. The results showed that instantaneous attributes of GPR can reinforce the information that might not be revealed in standard amplitude-based data visualization. The proposed method strengthens the contrast of the reflection and absorption features of the signal at the anomalies, which can be used in the automatic interpretation of GPR data to improve its discrimination ability in the future.

KEYWORDS:

• Subsurface distress
• GPR data fusion
• Hilbert transform
• multiattribute
• wavelet transform

Acknowledgments

This study was partly supported by the Innovation Program of Shanghai Municipal Education Commission (2021-01-07-00-07-E00092), and partly supported by the National Natural Science Foundation for Young Scholars of China (NSFC52108411), and partly supported by the Shanghai Science and Technology Commission (grant number 20DZ2251900, 20DZ1202104, 21DZ1200601). The authors are responsible for all the views and opinions expressed in this paper.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been republished with minor changes. These changes do not impact the academic content of the article.