Please use this identifier to cite or link to this item: http://hdl.handle.net/11400/10425
Title: Fast and efficient land-cover classification of multispectral remote sensing data using artificial neural network techniques
Authors: Βασιλάς, Νικόλαος
Χάρου, Ελένη
Βαρουφάκης, Σταύρος
Item type: Conference publication
Conference Item Type: Other
Keywords: γεωγραφία;ταξινόμηση εικόνας;τεχνητή νοημοσύνη;τηλεανίχνευση;αυτο-οργανούμενοι χάρτες;διάνυσμα κβάντωσης;Geography;image classification;artificial intelligence;remote sensing;Self-organizing maps;vector quantisation
Subjects: Τεχνολογία
Πληροφορική
Technology
Computer science
Issue Date: 14-May-2015
2-Jun-1997
Publisher: IEEE
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.
Description: σε έντυπη μορφή στο γραφείο μου
Language: English
Citation: 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.
Conference: 13th International Conference on Digital Signal Processing (DSP97)
Access scheme: Embargo
License: Αναφορά Δημιουργού-Μη Εμπορική Χρήση-Όχι Παράγωγα Έργα 3.0 Ηνωμένες Πολιτείες
URI: http://hdl.handle.net/11400/10425
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