Intensity Normalisation of GPR C-Scans

• Published in: Remote sensing (Basel, Switzerland) Vol. 15; no. 5; p. 1309
• Main Authors: Luo, Tess X. H., Lai, Wallace W. L., Lei, Zhanzhan
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-03-2023
• Subjects: Algorithms; C-scan; Concrete structures; Gray scale; Ground penetrating radar; Human bias; Image contrast; Image processing; Image quality; Image segmentation; intensity normalisation; Interfaces; Mapping; Noise; Noise control; Reinforced concrete; Remote sensing; Semantics; Signal processing; Spatial discrimination; Spatial resolution; Transformations (mathematics); Underground structures; Visualization
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs15051309

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.