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Remote Sens. 2014, 6(10), 9729-9748; doi:10.3390/rs6109729

GPR Signal Characterization for Automated Landmine and UXO Detection Based on Machine Learning Techniques

1
Defense University Center, Spanish Naval Academy, Plaza de España 2, 36920 Marín, Spain
2
Applied Geotechnologies Research Group, University of Vigo, Rúa Maxwell s/n, Campus Lagoas-Marcosende, 36310 Vigo, Spain
*
Author to whom correspondence should be addressed.
Received: 25 June 2014 / Revised: 24 September 2014 / Accepted: 29 September 2014 / Published: 13 October 2014
(This article belongs to the Special Issue Close-Range Remote Sensing by Ground Penetrating Radar)
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Abstract

Landmine clearance is an ongoing problem that currently affects millions of people around the world. This study evaluates the effectiveness of ground penetrating radar (GPR) in demining and unexploded ordnance detection using 2.3-GHz and 1-GHz high-frequency antennas. An automated detection tool based on machine learning techniques is also presented with the aim of automatically detecting underground explosive artifacts. A GPR survey was conducted on a designed scenario that included the most commonly buried items in historic battle fields, such as mines, projectiles and mortar grenades. The buried targets were identified using both frequencies, although the higher vertical resolution provided by the 2.3-GHz antenna allowed for better recognition of the reflection patterns. The targets were also detected automatically using machine learning techniques. Neural networks and logistic regression algorithms were shown to be able to discriminate between potential targets and clutter. The neural network had the most success, with accuracies ranging from 89% to 92% for the 1-GHz and 2.3-GHz antennas, respectively. View Full-Text
Keywords: demining; ground penetrating radar; neural network; logistic regression; pattern recognition demining; ground penetrating radar; neural network; logistic regression; pattern recognition
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Núñez-Nieto, X.; Solla, M.; Gómez-Pérez, P.; Lorenzo, H. GPR Signal Characterization for Automated Landmine and UXO Detection Based on Machine Learning Techniques. Remote Sens. 2014, 6, 9729-9748.

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