@inproceedings{4cc77b9e93694896a95678e227190adb,
title = "Identification of unexploded ordnance from clutter using neural networks",
abstract = "The largest costs associated with subsurface Unexploded Ordnance (UXO) remediation are associated with removing non-UXO debris. Discrimination between UXO and non-UXO is important for both cost and safety reasons. A neural network was developed to distinguish between UXO and non-UXO clutter using Time Domain Electromagnetic Method (TEM) data. There are two stages for the learning process of neural network: training and testing. A synthetic dataset was created using actual acquisition configurations, with varying amounts of random noise. This dataset included 934 UXO targets representing 7 different UXO types, and 789 clutter objects based on four templates with varying size and random asymmetry. The results show 97\% accuracy for correctly classifying clutter, and 97\% accuracy for correctly classifying UXO.",
author = "Anna Szidarovszky and Mary Poulton and Scott MacInnes",
note = "Publisher Copyright: Copyright {\textcopyright} (2008) by the Society of Exploration Geophysicists All rights reserved.; 78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008 ; Conference date: 09-11-2008 Through 14-11-2008",
year = "2018",
language = "English (US)",
isbn = "9781605607856",
series = "78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008",
publisher = "Society of Exploration Geophysicists",
pages = "2912--2916",
booktitle = "78th Society of Exploration Geophysicists International Exposition and Annual Meeting, SEG 2008",
address = "United States",
}