Open AccessClassification of different materials for underground object using artificial neural network
Author(s) -
Hasimah Ali,
A.Z. Ahmad Firdaus,
Mohd Shuhanaz Zanar Azalan,
Siti Nurul Aqmariah Mohd Kanafiah,
Shaeke Salman,
Megat Harun Al Rashid Megat Ahmad,
Tengku Sarah Tengku Amran,
Mohamad Syafiq Mohd Amin
Publication year - 2019
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/705/1/012013
Subject(s) - ground penetrating radar , artificial neural network , artificial intelligence , signature (topology) , classifier (uml) , pattern recognition (psychology) , computer science , multilayer perceptron , object (grammar) , radar , geology , computer vision , mathematics , telecommunications , geometry
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.