A Partially Supervised Approach for Detection and Classification of Buried Radioactive Metal Targets Using Electromagnetic Induction Data

被引：6

作者：

Turlapaty, Anish C. ^{[1
]}

Du, Qian ^{[2
]}

Younan, Nicolas H. ^{[2
]}

机构：

[1] Univ Maryland Eastern Shore, Princess Anne, MD 21853 USA

[2] Mississippi State Univ, Dept Elect & Comp Engn, Mississippi State, MS 39762 USA

来源：

IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING | 2013年 / 51卷 / 01期

关键词：

Buried radioactive target detection; electromagnetic induction data; support vector machines; SUPPORT VECTOR MACHINES; DEPLETED URANIUM; RECOGNITION; SVM;

D O I：

10.1109/TGRS.2012.2200044

中图分类号：

P3 [地球物理学]; P59 [地球化学];

学科分类号：

0708 ; 070902 ;

摘要：

The analysis of the data obtained from electromagnetic induction (EMI) sensors is one of the most viable tools for the detection of metallic objects buried under soil. The existing detection methods usually consist of sophisticated EM modeling of the source/target geometry to build suitable discriminators. The major technical challenge in this field is the reduction of false alarms with an increase of the detection probability. In this paper, we propose a partially supervised approach to detect buried radioactive targets, i.e., depleted uranium, without sophisticated EM modeling. Using the EMI data obtained by a GEM-3 sensor for a field survey, our proposed algorithm can successfully detect and discriminate the targets from nontarget metals, compared to other unsupervised and supervised approaches.

引用

页码：108 / 121

页数：14

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