Delineation of lineaments from satellite data based on efficient neural network and pattern recognition techniques

dc.contributor.author	Βασιλάς, Νικόλαος	el
dc.contributor.author	Περαντώνης, Σταύρος	el
dc.contributor.author	Χάρου, Ελένη	el
dc.contributor.author	Τσενόγλου, Θεοχάρης	el
dc.contributor.author	Στεφανούλη, Μάρθα	el
dc.date.accessioned	2015-05-14T21:22:51Z
dc.date.available	2015-05-14T21:22:51Z
dc.date.issued	2015-05-15
dc.identifier.uri	http://hdl.handle.net/11400/10436
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://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.75.6859	en
dc.subject	νευρωνικά δίκτυα
dc.subject	τεχνητή νοημοσύνη
dc.subject	δορυφορικά δεδομένα
dc.subject	τεχνικές αναγνώρισης
dc.subject	Neural networks
dc.subject	artificial intelligence
dc.subject	satellite data
dc.subject	recognition techniques
dc.title	Delineation of lineaments from satellite data based on efficient neural network and pattern recognition 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://zbw.eu/stw/descriptor/19808-6
heal.contributorName	Βαρουφάκης, Σταύρος	el
heal.identifier.secondary	DOI: 10.1.1.75.6859
heal.language	en
heal.access	campus
heal.recordProvider	Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε.	el
heal.publicationDate	2002-04
heal.bibliographicCitation	Vassilas, N., Perantonis, S., Charou, E., Tsenoglou, T., Stefouli, M. et al. (2002) Delineation of Lineaments from Satellite Data Based on Efficient Neural Network and Pattern Recognition Techniques. 2th Hellenic Conference on AI. pp.355-365. Thessaloniki, Greece.	en
heal.abstract	An automated lineament detection method based on a modified Hough transform is presented. The method first performs an efficient data clustering using Kohonen’s self-organizing maps then binarizes the classification result and finally applies the modified Hough transform in order to identify lineaments. The capabilities of the method are described using Landsat TM satellite data from the Vermion area in Greece. The results of the automated analysis show major geological faults in the selected area.	en
heal.fullTextAvailability	true
heal.conferenceName	2th Hellenic Conference on AI	en
heal.conferenceItemType	full paper