Artificial neural network approach for land cover classification of fused Hyperspectral and LIDAR data

dc.contributor.author	Γιαμπουράς, Π.	el
dc.contributor.author	Χάρου, Ελένη	el
dc.contributor.author	Κεσίδης, Αναστάσιος Λ.	el
dc.date.accessioned	2015-06-05T15:59:28Z
dc.date.available	2015-06-05T15:59:28Z
dc.date.issued	2015-06-05
dc.identifier.uri	http://hdl.handle.net/11400/15144
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://link.springer.com	en
dc.subject	Hyperspectral images
dc.subject	Neural networks
dc.subject	Υπερφασματικές εικόνες
dc.subject	Νευρωνικά δίκτυα
dc.title	Artificial neural network approach for land cover classification of fused Hyperspectral and LIDAR data	en
heal.type	journalArticle
heal.classification	Topography
heal.classification	Geodesy
heal.classification	Τοπογραφία
heal.classification	Γεωδαισία
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85053969
heal.classificationURI	http://id.loc.gov/authorities/subjects/sh85053969
heal.classificationURI	N/A-Τοπογραφία
heal.classificationURI	N/A-Γεωδαισία
heal.identifier.secondary	DOI: 10.1007/978-3-642-41142-7_26
heal.language	en
heal.access	campus
heal.publicationDate	2013
heal.bibliographicCitation	Giampouras, P., Charou, E. and Kesidis, A. (2013) Artificial neural network approach for land cover classification of fused Hyperspectral and LIDAR data. "Advances in Information and Communication Technology", 412, p.255-261	en
heal.abstract	Hyperspectral remote sensing images are consisted of several hundreds of contiguous spectral bands that can provide very rich information and has the potential to differentiate land cover classes with similar spectral characteristics. LIDAR data gives detailed height information and thus can be used complementary with Hyperspectral data. In this work, a hyperspectral image is combined with LIDAR data and used for land cover classification. A Principal Component Analysis (PCA) is applied on the Hyperspectral image to perform feature extraction and dimension reduction. The first 4 PCA components along with the LIDAR image were used as inputs to a supervised feedforward neural network. The neural network was trained in a small part of the dataset (less than 0.4%) and a validation set, using the Bayesian regularization backpropagation algorithm. The experimental results demonstrate efficiency of the method for hyperspectral and LIDAR land cover classification.	en
heal.publisher	Springer	en
heal.journalName	Advances in Information and Communication Technology	en
heal.journalType	peer-reviewed
heal.fullTextAvailability	true