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Detection and classification of buried dielectric anomalies by means of the bispectrum method and neural networks

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dc.contributor.authorBalan, Ajay, N., author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:43:36Z
dc.date.available2007-01-03T04:43:36Z
dc.date.issued1995
dc.description.abstractThe development of neural network-based system for detection and classification of buried landmines is the main focus of this paper. Shape-dependent features are extracted by means of the bispectrum method. These features are then applied to the neural network. A multilayer back-propagation-type neural network is trained and tested on the feature sets extracted from equally spaced radial slices of image windows. Simulation results obtained for two types of targets indicated good detection and classification rates.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationBalan, Ajay N. and Mahmood R. Azimi-Sadjadi, Detection and Classification of Buried Dielectric Anomalies by Means of the Bispectrum Method and Neural Networks, IEEE Transactions on Instrumentation and Measurement 44, no. 6 (December 1995): 998-1002.
dc.identifier.urihttp://hdl.handle.net/10217/859
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1995 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.subjectmultilayer perceptrons
dc.subjectmilitary systems
dc.subjectfeature extraction
dc.subjectfeedforward neural nets
dc.titleDetection and classification of buried dielectric anomalies by means of the bispectrum method and neural networks
dc.typeText

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