Intensity Normalisation of GPR C-Scans

Author:

Luo Tess X. H.1,Lai Wallace W. L.2ORCID,Lei Zhanzhan1

Affiliation:

1. College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518000, China

2. Department of Land Surveying and Geoinformatics, The Hong Kong Polytechnic University, Kowloon, Hong Kong

Abstract

The three-dimensional (3D) ground-penetrating radar (GPR) has been widely applied in subsurface surveys and imaging, and the quality of the resulting C-scan images is determined by the spatial resolution and visualisation contrast. Previous studies have standardised the suitable spatial resolution of GPR C-scans; however, their measurement normalisation remains arbitrary. Human bias is inevitable in C-scan interpretation because different visualisation algorithms lead to different interpretation results. Therefore, an objective scheme for mapping GPR signals after standard processing to the visualisation contrast should be established. Focusing on two typical scenarios, a reinforced concrete structure and an urban underground, this study illustrated that the essential parameters were greyscale thresholding and transformation mapping. By quantifying the normalisation performance with the integration of image segmentation and structural similarity index measure, a greyscale threshold was developed in which the normalised standard deviation of the unit intensity of any surveyed object was two. A transformation function named “bipolar” was also shown to balance the maintenance of real reflections at the target objects. By providing academia/industry with an object-based approach, this study contributes to solving the final unresolved issue of 3D GPR imaging (i.e., image contrast) to better eliminate the interfering noise and better mitigate human bias for any one-off/touch-based imaging and temporal change detection.

Funder

Research Grant Council of HKSARG

The National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Clutter Removal in Ground Penetrating Radar by Learned RPCA;2023 46th International Conference on Telecommunications and Signal Processing (TSP);2023-07-12

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