Intelligent techniques for efficient generation of ground cover maps

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Περαντώνης, Σταύρος	el
dc.contributor.author	Χάρου, Ελένη	el
dc.contributor.author	Βαρουφάκης, Σταύρος	el
dc.date.accessioned	2015-05-14T19:26:04Z
dc.date.available	2015-05-14T19:26:04Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10428
dc.rights	Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες	*
dc.rights.uri	http://creativecommons.org/licenses/by-nc-nd/3.0/us/	*
dc.source	http://users.iit.demokritos.gr/~exarou/PAPERS/groundcovercalgary.pdf	en
dc.subject	αυτο-οργανούμενοι χάρτες
dc.subject	νευρωνικά δίκτυα
dc.subject	Ευφυείς τεχνικές
dc.subject	Αποδοτική παραγωγή
dc.subject	τηλεανίχνευση
dc.subject	Self-organizing maps
dc.subject	Neural networks
dc.subject	Intelligent Techniques
dc.subject	Efficient Generation
dc.subject	Remote Sensing
dc.title	Intelligent techniques for efficient generation of ground cover maps	en
heal.type	conferenceItem
heal.generalDescription	σε έντυπη μορφή στο γραφείο μου	el
heal.classification	Τεχνολογία
heal.classification	Πληροφορική
heal.classification	Technology
heal.classification	Computer science
heal.classificationURI	N/A-Τεχνολογία
heal.classificationURI	N/A-Πληροφορική
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85133147
heal.classificationURI	http://skos.um.es/unescothes/C00750
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh99004370
heal.keywordURI	http://zbw.eu/stw/descriptor/19808-6
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	1998-05
heal.bibliographicCitation	Vassilas, N., Perantonis, S., Charou, E. and Varoufakis, S. (1998) Intelligent Techniques for Efficient Generation of Ground-Cover Maps. 20th Canadaian Symposium on Remote Sensing. pp.255-258. Calgary, Canada.	en
heal.abstract	In this work, a new methodology based on artificial neural networks (ANN) and indexing techniques is used with the aim to improve memory requirements for storing multisource or multispectral remote sensing (MRS) data and at the same time increase classification speed. This methodology features: a) data quantization using a self-organizing map, b) training set reduction to speed up ANN training, c) fast clustering of prototypes, and d) fast indexed classification. Results obtained for both supervised and unsupervised classification to ground-cover categories using, at no loss of generality, a Landsat TM image, show savings in time and memory without a significant compromise of classification performance.	en
heal.fullTextAvailability	true
heal.conferenceName	20th Canadaian Symposium on Remote Sensing	en
heal.conferenceItemType	full paper