Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/150099
Title: Deep learning for ground penetrating radar image processing
Authors: Koh, Leonard Deng Liang
Keywords: Engineering::Electrical and electronic engineering::Antennas, wave guides, microwaves, radar, radio
Engineering::Computer science and engineering::Computing methodologies::Simulation and modeling
Engineering::Computer science and engineering::Computing methodologies::Image processing and computer vision
Issue Date: 2021
Publisher: Nanyang Technological University
Source: Koh, L. D. L. (2021). Deep learning for ground penetrating radar image processing. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/150099
Project: B3123-201
Abstract: Ground Penetrating Radar (GPR) is a useful technique that uses radar pulses to image the subsurface. It is a non-intrusive method of surveying the sub-surface to detect underground utilities such as pipes, cables, etc. The GPR images usually come in three variations, either as an A-scan, B-scan, or C-scan images. Firstly, this paper will discuss how GPR can be used for detecting tree roots underground and discuss how factors like permittivity will affect the overall B-scan image. Secondly, this paper will also talk about an open-source forward-based solver software called gprMax that simulates electromagnetic (EM) wave propagation. It solves Maxwell’s equations in three dimensions (3D) using the Finite-Difference Time-Domain (FDTD) method. It was designed for modelling GPR applications, but it can be also used to model many other electromagnetic wave propagation applications. Thirdly, this paper will also discuss how Deep Learning Techniques can be used to create a surrogate Deep Neural Network (DNN) model for forward modelling of GPR images to solve a problem that National Parks Board (NParks) are currently facing.
URI: https://hdl.handle.net/10356/150099
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:EEE Student Reports (FYP/IA/PA/PI)

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