@inproceedings{d7e627e7e9e44a20b50de73b874f857f,
title = "Detection of explosive hazards using spectrum features from forward-looking ground penetrating radar imagery",
abstract = "Buried explosives have proven to be a challenging problem for which ground penetrating radar (GPR) has shown to be effective. This paper discusses an explosive hazard detection algorithm for forward looking GPR (FLGPR). The proposed algorithm uses the fast Fourier transform (FFT) to obtain spectral features of anomalies in the FLGPR imagery. Results show that the spectral characteristics of explosive hazards differ from that of background clutter and are useful for rejecting false alarms (FAs). A genetic algorithm (GA) is developed in order to select a subset of spectral features to produce a more generalized classifier. Furthermore, a GA-based K-Nearest Neighbor probability density estimator is employed in which targets and false alarms are used as training data to produce a two-class classifier. The experimental results of this paper use data collected by the US Army and show the effectiveness of spectrum based features in the detection of explosive hazards.",
keywords = "Forward-looking explosive hazards detection, Genetic algorithm, Ground-penetrating radar, K-nearest-neighbor, Spectral features",
author = "Justin Farrell and Havens, \{Timothy C.\} and Ho, \{K. C.\} and Keller, \{James M.\} and Ton, \{Tuan T.\} and Wong, \{David C.\} and Mehrdad Soumekh",
year = "2011",
doi = "10.1117/12.884685",
language = "English",
isbn = "9780819485915",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
booktitle = "Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI",
note = "Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVI ; Conference date: 25-04-2011 Through 29-04-2011",
}