Fast and efficient land-cover classification of multispectral remote sensing data using artificial neural network techniques

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Χάρου, Ελένη	el
dc.contributor.author	Βαρουφάκης, Σταύρος	el
dc.date.accessioned	2015-05-14T19:11:30Z
dc.date.issued	2015-05-14
dc.identifier.uri	http://hdl.handle.net/11400/10425
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://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=628531	en
dc.subject	γεωγραφία
dc.subject	ταξινόμηση εικόνας
dc.subject	τεχνητή νοημοσύνη
dc.subject	τηλεανίχνευση
dc.subject	αυτο-οργανούμενοι χάρτες
dc.subject	διάνυσμα κβάντωσης
dc.subject	Geography
dc.subject	image classification
dc.subject	artificial intelligence
dc.subject	remote sensing
dc.subject	Self-organizing maps
dc.subject	vector quantisation
dc.title	Fast and efficient land-cover classification of multispectral remote sensing data using artificial neural network techniques	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/sh85053986
heal.keywordURI	http://id.loc.gov/authorities/subjects/sh99004370
heal.identifier.secondary	DOI: 10.1109/ICDSP.1997.628531
heal.dateAvailable	10000-01-01
heal.language	en
heal.access	forever
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	1997-06-02
heal.bibliographicCitation	Vassilas, N., Charou, E. and Varoufakis, S. (1997) Fast and efficient land-cover classification of multispectral remote sensing data using artificial neural network techniques. 13th International Conference on Digital Signal Processing (DSP97). pp.995-998. Santorini: IEEE.	en
heal.abstract	A time and memory efficient methodology for supervised and unsupervised land-cover classification of multispectral remote sensing (MRS) data based on artificial neural network (ANN) techniques is presented. The proposed methodology first performs a vector quantization (VQ) using the self-organizing maps (SOM) algorithm to compress the MRS data followed by either efficient clustering and automatic classification or, when training sets are available, by a forced reduction of the training set size induced by vector quantization resulting to a faster training of the supervised ANN algorithms.	en
heal.publisher	IEEE	en
heal.fullTextAvailability	false
heal.conferenceName	13th International Conference on Digital Signal Processing (DSP97)	en
heal.conferenceItemType	other