Neural network architectures for the classification of temporal image sequences

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dc.contributor.author German, GWH en
dc.contributor.author Gahegan, Mark en
dc.date.accessioned 2011-08-10T22:19:39Z en
dc.date.issued 1996 en
dc.identifier.citation Computers and Geosciences 22(9):969-979 1996 en
dc.identifier.issn 0098-3004 en
dc.identifier.uri http://hdl.handle.net/2292/7286 en
dc.description.abstract This paper illustrates problems and solutions for the design and configuration of neural network architectures used to classify remotely sensed multi-temporal imagery. A methodology is presented that avoids many of the traditional problems associated with neural network design, thus enabling the technique to be applied directly, without placing the burden of configuration on the user. The structure inherent within the data is used to derive both the network architecture and starting weights. Experiments with agricultural landuse classification show that the resulting networks classify at least as well as more traditional statistical approaches. en
dc.language EN en
dc.relation.ispartofseries Computers and Geosciences en
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher. Details obtained from http://www.sherpa.ac.uk/romeo/issn/0098-3004// en
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm en
dc.subject neural networks en
dc.subject multi-temporal en
dc.subject remote sensing en
dc.subject LANDSAT TM en
dc.subject MAPPINGS en
dc.title Neural network architectures for the classification of temporal image sequences en
dc.type Journal Article en
dc.identifier.doi 10.1016/S0098-3004(96)00035-0 en
pubs.issue 9 en
pubs.begin-page 969 en
pubs.volume 22 en
dc.rights.holder Copyright: 1996 Elsevier Science Ltd en
pubs.end-page 979 en
dc.rights.accessrights http://purl.org/eprint/accessRights/RestrictedAccess en
pubs.subtype Article en
pubs.elements-id 134258 en
pubs.org-id Science en
pubs.org-id School of Computer Science en
pubs.record-created-at-source-date 2011-08-11 en


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