Classification of different materials for underground object using artificial neural network

Abstract

Ground Penetrating Radar (GPR) is widely used for non-destructive investigation of the shallow subsurface exploration especially in locating the buried infrastructure such as pipes, cables and road inspections. However, the interpreting hyperbolic signature of buried object in GPR images remains a challenging task. Therefore, this paper presented the classification of different materials based on GPR images using artificial neural network (ANN). In this research, GPR images so called the B-scan radargram represented by hyperbolic signature are firstly acquired and pre-processed. Then, the hyperbolic signature features are extracted using statistical techniques. The extracted features is then fed up as input to the multilayer perceptron (MLP) neural network classifier. A series of experiments have been conducted based on extracted hyperbolic features of different materials and shapes. Based on the results, the proposed method in classifying different materials based on GPR images using neural network showed promising results.