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Detection and classification of buried dielectric anomalies using neural networks–further results

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dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorStricker, Scott A., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:43:36Z
dc.date.available2007-01-03T04:43:36Z
dc.date.issued1994
dc.description.abstractThe development of a neural network-based detection and classification system for use with buried dielectric anomalies is the main focus of this paper. Several methods of data representation are developed to study their effects on the trainability and generalization capabilities of the neural networks. The method of Karhonen-Loeve (KL) transform is used to extract energy dependent features and to reduce the dimensionality of the weight space of the original data set. To extract the shape-dependent features of the data, another data preprocessing method known as Zernike moments is also studied for its use in the detector/classifier system. The effects of different neural network paradigms, architectural variations, and selection of proper training data on detection and classification rates are studied. Simulation results for nylon and wood targets indicate superior performance when compared to conventional schemes.
dc.description.sponsorshipThis work was supported by the U.S. Army Belvoir RDandE Center under contract No. DAAL03-86-D-0001.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationAzimi-Sadjadi, Mahmood R. and Scott A. Stricker, Detection and Classification of Buried Dielectric Anomalies Using Neural Networks--Further Results, IEEE Transactions on Instrumentation and Measurement 43, no. 1 (February 1994): 34-39.
dc.identifier.urihttp://hdl.handle.net/10217/858
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1994 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.subjectdata reduction
dc.subjectdata structures
dc.subjectdielectric measurement
dc.subjectdielectric properties of solids
dc.subjectdigital simulation
dc.subjectfeature extraction
dc.subjectgeophysical techniques
dc.subjectimage processing equipment
dc.subjectlearning (artificial intelligence)
dc.subjectneural nets
dc.subjectpattern recognition
dc.subjectpolymers
dc.subjectsoil
dc.subjecttransforms
dc.subjectwood
dc.titleDetection and classification of buried dielectric anomalies using neural networks–further results
dc.typeText

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