Detection of Explosive Hazards Using Spectrum Features From Forward-Looking Ground Penetrating Radar Imagery

被引:4
作者
Farrell, Justin [1 ]
Havens, Timothy C. [1 ]
Ho, K. C. [1 ]
Keller, James M. [1 ]
Ton, Tuan T. [2 ]
Wong, David C. [2 ]
Soumekh, Mehrdad [3 ]
机构
[1] Univ Missouri, Columbia, MO 65211 USA
[2] US Army Night Vis & Elect Sensors Directorate, Ft Belvoir, VA 22060 USA
[3] NYU, Elect Engn, Buffalo, NY 14260 USA
来源
DETECTION AND SENSING OF MINES, EXPLOSIVE OBJECTS, AND OBSCURED TARGETS XVI | 2011年 / 8017卷
关键词
Forward-looking explosive hazards detection; ground-penetrating radar; K-Nearest-Neighbor; genetic algorithm; spectral features;
D O I
10.1117/12.884685
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
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.
引用
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页数:11
相关论文
共 3 条
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