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dc.contributor.authorXiaoyang, FU
dc.contributor.authorDale, Patricia
dc.contributor.authorShuqing, ZHANG
dc.contributor.editorHuang Xichou
dc.date.accessioned2017-05-03T11:11:39Z
dc.date.available2017-05-03T11:11:39Z
dc.date.issued2008
dc.date.modified2009-05-05T07:02:10Z
dc.identifier.issn10020063
dc.identifier.doi10.1007/s11769-008-0162-x
dc.identifier.urihttp://hdl.handle.net/10072/22542
dc.description.abstractCoastal wetlands are characterized by complex patterns both in their geomorphic and ecological features. Besides field observations, it is necessary to analyze the land cover of wetlands through the color infrared (CIR) aerial photography or remote sensing image. In this paper, we designed an evolving neural network classifier using variable string genetic algorithm (VGA) for the land cover classification of CIR aerial image. With the VGA, the classifier that we designed is able to evolve automatically the appropriate number of hidden nodes for modeling the neural network topology optimally and to find a near-optimal set of connection weights globally. Then, with backpropagation algorithm (BP), it can find the best connection weights. The VGA-BP classifier, which is derived from hybrid algorithms mentioned above, is demonstrated on CIR images classification effectively. Compared with standard classifiers, suchas Bayes maximum-likelihood classifier, VGA classifier and BP-MLP (multi-layer perception) classifier, it has shown that the VGA-BP classifier can have better performance on highly resolution land cover classification.
dc.description.peerreviewedYes
dc.description.publicationstatusYes
dc.format.extent281116 bytes
dc.format.mimetypeapplication/pdf
dc.languageEnglish
dc.language.isoen_AU
dc.publisherKexue Chubanshe
dc.publisher.placeChina
dc.publisher.urihttp://www.springer.com/geography/journal/11769
dc.relation.ispartofstudentpublicationN
dc.relation.ispartofpagefrom162
dc.relation.ispartofpageto170
dc.relation.ispartofissue2
dc.relation.ispartofjournalChinese Geographical Science
dc.relation.ispartofvolume18
dc.rights.retentionY
dc.subject.fieldofresearchPhysical Geography and Environmental Geoscience
dc.subject.fieldofresearchHuman Geography
dc.subject.fieldofresearchcode0406
dc.subject.fieldofresearchcode1604
dc.titleEvolving Neural Network Using Variable StringGenetic Algorithms (VGA) for Color Infrared Aerial Image Classification.
dc.typeJournal article
dc.type.descriptionC1 - Articles
dc.type.codeC - Journal Articles
gro.rights.copyright© 2008 Springer-Verlag. This is the author-manuscript version of this paper. Reproduced in accordance with the copyright policy of the publisher. The original publication is available at www.springerlink.com
gro.date.issued2008
gro.hasfulltextFull Text
gro.griffith.authorDale, Patricia E.


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