Ground Penetrating Radar (GPR) is a non-destructive test used as imaging tool for exploration of shallow subsurface such locating the buried infrastructures. Due to the existence of various subsurface material and environmental noise, such as bricks and tree branch, it is a challenging task to interpret the GPR data into a meaningful information. Thus, this project proposes the hyperbolic recognition of buried pipes in GPR images in the presence of scattering objects. In this framework, the GPR images were firstly subjected to image pre-processing. Then, the GPR images were decomposed using Discrete Wavelet Transform (DWT) to analyze the texture analysis of hyperbola signature. Then, the approximation subband of DWT were extracted and used as features to recognize the hyperbolic of buried pipes and scattering objects presence in GPR images. A series of experiment has been conducted on GPR data collection at Agency Nuclear Malaysia. Based on the results obtained, the average recognition rate of extracted approximation subband of DWT features using k-NN classifier is 99.75%, thus shows a promising results in recognizing the buried pipes in the presence of scattering objects.
