DETECTING CYLINDRICAL TARGETS CHARACTERISTICS HIDDEN IN GPR IMAGES USING TWO INTELLIGENT METHODS: ARTIFICIAL NEURAL NETWORK AND TEMPLATE MATCHING

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AHMADI REZA; FATHIANPOUR NADER; NOROUZI GHOLAM HOSSAIN

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Journal: JOURNAL OF ENGINEERING GEOLOGY Year:2016 | Volume:9 | Issue:4 Page(s): 3069-3092

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Title

DETECTING CYLINDRICAL TARGETS CHARACTERISTICS HIDDEN IN GPR IMAGES USING TWO INTELLIGENT METHODS: ARTIFICIAL NEURAL NETWORK AND TEMPLATE MATCHING

Author(s)

AHMADI REZA | FATHIANPOUR NADER | NOROUZI GHOLAM HOSSAIN | Issue Writer Certificate

Keywords

GROUNDQ2

PENETRATING RADAR (GPR)Q2

GEOMETRICAL CHARACTERISTICS OF CYLINDRICAL TARGETSQ2

ARTIFICIAL NEURAL NETWORKQ2

TEMPLATE MATCHINGQ2

NORMALIZED CROSS CORRELATIONQ2

Abstract

GROUND- Penetrating Radar (GPR) is a non-destructive and high-resolution geophysical method that uses high frequency reflected EM waves to detect buried objects and manmade structures. In current study this method has been used to identify geometrical characteristics of buried cylindrical targets such as tunnel structures. This aim has been obtained through determination of relationships between physical and GEOMETRICAL CHARACTERISTICS OF CYLINDRICAL TARGETS with the parameters of GPR hyperbolic response using two intelligent pattern recognition methods known as ARTIFICIAL NEURAL NETWORK and TEMPLATE MATCHING. To this goal GPR responses of synthetic cylindrical objects corresponding to common geotechnical targets (such as tunnels, canals, qanats and pipes) have been simulated using forward modeling by 2D Finite Difference and have been used as templates in the neural network and TEMPLATE MATCHING algorithms. The structure of applied neural network was designed based on extracting discriminant and unique features (eigen values and the norm of eigen values in the horizontal and vertical directions) from the GPR images and predicting all geometrical parameters of the objects simultaneously. The TEMPLATE MATCHING operation also carried out by two different similarity approaches named spatial domain convolution and NORMALIZED CROSS CORRELATION in 2D wave number domain. Afterward it was delineated that the wave number domain approach is generally faster (more than 23 times) than the other approach.The results of the research show that both two employed intelligent methods having in situ, real-time, accurate and automatic application capabilities can be applied for real geotechnical applications, however in general the neural network method has led to less error and as a result higher estimation power for the geometrical parameters of the cylindrical targets than TEMPLATE MATCHING method.

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APA: Copy

AHMADI, REZA, FATHIANPOUR, NADER, & NOROUZI, GHOLAM HOSSAIN. (2016). DETECTING CYLINDRICAL TARGETS CHARACTERISTICS HIDDEN IN GPR IMAGES USING TWO INTELLIGENT METHODS: ARTIFICIAL NEURAL NETWORK AND TEMPLATE MATCHING. JOURNAL OF ENGINEERING GEOLOGY, 9(4), 3069-3092. SID. https://sid.ir/paper/186266/en

Vancouver: Copy

AHMADI REZA, FATHIANPOUR NADER, NOROUZI GHOLAM HOSSAIN. DETECTING CYLINDRICAL TARGETS CHARACTERISTICS HIDDEN IN GPR IMAGES USING TWO INTELLIGENT METHODS: ARTIFICIAL NEURAL NETWORK AND TEMPLATE MATCHING. JOURNAL OF ENGINEERING GEOLOGY[Internet]. 2016;9(4):3069-3092. Available from: https://sid.ir/paper/186266/en

IEEE: Copy

REZA AHMADI, NADER FATHIANPOUR, and GHOLAM HOSSAIN NOROUZI, “DETECTING CYLINDRICAL TARGETS CHARACTERISTICS HIDDEN IN GPR IMAGES USING TWO INTELLIGENT METHODS: ARTIFICIAL NEURAL NETWORK AND TEMPLATE MATCHING,” JOURNAL OF ENGINEERING GEOLOGY, vol. 9, no. 4, pp. 3069–3092, 2016, [Online]. Available: https://sid.ir/paper/186266/en

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