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Detection of mines and minelike targets using principal component and neural-network methods

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dc.contributor.authorWitherspoon, Ned H., author
dc.contributor.authorDubey, Abinash C., author
dc.contributor.authorTian, Bin, author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorMiao, Xi, author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:48:22Z
dc.date.available2007-01-03T04:48:22Z
dc.date.issued1998
dc.description.abstractThis paper introduces a new system for real-time detection and classification of arbitrarily scattered surface-laid mines from multispectral imagery data of a minefield. The system consists of six channels which use various neural-network structures for feature extraction, detection, and classification of targets in six different optical bands ranging from near UV to near IR. A single-layer autoassociative network trained using the recursive least square (RLS) learning rule was employed in each channel to perform feature extraction. Based upon the extracted features, two different neural-network architectures were used and their performance was compared against the standard maximum likelihood (ML) classification scheme. The outputs of the detector/classifier network in all the channels were fused together in a final decision-making system. Two different final decision making schemes using the majority voting and weighted combination based on consensual theory were considered. Simulations were performed on real data for six bands and on several images in order to account for the variations in size, shape, and contrast of the targets and also the signal-to-clutter ratio. The overall results showed the promise of the proposed system for detection and classification of mines and minelike tagets.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationMiao, Xi, et al., Detection of Mines and Minelike Targets Using Principal Component and Neural-Network Methods, IEEE Transactions on Neural Networks 9, no. 3 (May 1998): 454-463.
dc.identifier.urihttp://hdl.handle.net/10217/922
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1998 IEEE.
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectpattern recognition
dc.subjectlandmine detection
dc.subjectprincipal component analysis
dc.subjectneural networks
dc.titleDetection of mines and minelike targets using principal component and neural-network methods
dc.typeText

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