Interpretation of ground penetrating radar dataset using normalised cross-correlation technique

W. A. Wahab, W. Z. Zakaria, R. C. Omar, R. Roslan, J. Jaafar, A. M. Suldi

Research output: Contribution to journalArticle


Ground Penetrating Radar (GPR) is one of the latest non-destructive geophysical technology and most widely used in detecting underground utilities. GPR can detect both metal and non-metal, however, it is unable to identify the type of underground utility object. Many researchers come out with their techniques to interpret the GPR image. The current method requires experience in interpretation. Thus, in this study, a new method to detect underground utility utilizing the Normalised Cross-Correlation (NCC) template matching technique is proposed. This technique will reduce the dependency on experts to interpret the radargram, less time consuming and eventually save cost. Upon detection, the accuracy of the system is assessed. From the accuracy assessment performed, it is shown that the system provides accurate detection results for both, depth and pipe size. The Root Mean Square Error (RMSE) for the buried pipe depth obtained by using the proposed system is 0.110 m, whereas the highest percentage match obtained is 91.34%, the remaining 8.66% mismatched might be due to the soil condition, velocity or processing parameter that affected the radargram. Based on the assessment, the developed system seems capable to detect the subsurface utility if the radar image and template image used is acquired using the same antenna frequency, point interval, and similar GPR instrument.

Original languageEnglish
Pages (from-to)3505-3509
Number of pages5
JournalInternational Journal of Engineering and Advanced Technology
Issue number1
Publication statusPublished - Oct 2019

All Science Journal Classification (ASJC) codes

  • Environmental Engineering
  • Engineering(all)
  • Computer Science Applications

Fingerprint Dive into the research topics of 'Interpretation of ground penetrating radar dataset using normalised cross-correlation technique'. Together they form a unique fingerprint.

  • Cite this