Realistic Subsurface Anomaly Discrimination Using Electromagnetic Induction and an SVM Classifier. (11th May 2010)

• Record Type: Journal Article
• Authors: Pablo Fernández Pablo Fernández, Juan Juan; Shubitidze Shubitidze, Fridon Fridon; Shamatava Shamatava, Irma Irma; Barrowes Barrowes, Benjamin E. Benjamin E.; O'Neill O'Neill, Kevin Kevin
• Other Names: Ehlers Ehlers Frank Frank Academic Editor.
• Is Part Of: EURASIP journal on advances in signal processing. Volume 2010(2010)
• Publication Date: 2010-05-11
• Subjects: Signal processing -- Periodicals; Traitement du signal; Signal processing; Periodicals; 621.3822
• DOI: 10.1155/2010/305890
• Languages: English
• ISSNs: 1687-6172

Abstract

Abstract : The environmental research program of the United States military has set up blind tests for detection and discrimination of unexploded ordnance. One such test consists of measurements taken with the EM-63 sensor at Camp Sibert, AL. We review the performance on the test of a procedure that combines a field-potential (HAP) method to locate targets, the normalized surface magnetic source (NSMS) model to characterize them, and a support vector machine (SVM) to classify them. The HAP method infers location from the scattered magnetic field and its associated scalar potential, the latter reconstructed using equivalent sources. NSMS replaces the target with an enclosing spheroid of equivalent radial magnetization whose integral it uses as a discriminator. SVM generalizes from empirical evidence and can be adapted for multiclass discrimination using a voting system. Our method identifies all potentially dangerous targets correctly and has a false-alarm rate of about 5%.