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Terrain classification in SAR images using principal components analysis and neural networks

dc.contributor.authorZoughi, R., author
dc.contributor.authorGhaloum, S., author
dc.contributor.authorAzimi-Sadjadi, Mahmood R., author
dc.contributor.authorIEEE, publisher
dc.date.accessioned2007-01-03T04:43:06Z
dc.date.available2007-01-03T04:43:06Z
dc.date.issued1993
dc.description.abstractThe development of a neural network-based classifier for classifying three distinct scenes (urban, park and water) from several polarized SAR images of San Francisco Bay area is discussed. The principal component (PC) scheme or Karhunen-Loeve (KL) transform is used to extract the salient features of the input data, and to reduce the dimensionality of the feature space prior to the application to the neural networks. Employing PC scheme along with polarized images used in this study, led to substantial improvements in the classification rates when compared with previous studies. When a combined polarization architecture is used the classification rate for water, urban and park areas improved to 100%, 98.7%, and 96.1%, respectively.
dc.format.mediumborn digital
dc.format.mediumarticles
dc.identifier.bibliographicCitationAzimi-Sadjadi, M. R., S. Ghaloum, and R. Zoughi, Terrain Classification in SAR Images Using Principal Components Analysis and Neural Networks, IEEE Transactions on Geoscience and Remote Sensing 31, no. 2 (March 1993): 511-515.
dc.identifier.urihttp://hdl.handle.net/10217/857
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartofFaculty Publications
dc.rights©1993 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.subjectimage processing
dc.subjectgeophysics computing
dc.subjectneural nets
dc.subjectremote sensing by radar
dc.titleTerrain classification in SAR images using principal components analysis and neural networks
dc.typeText

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