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

 
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1997 (EN)
Fast and efficient land-cover classification of multispectral remote sensing data using artificial neural network techniques (EN)

Βαρουφάκης, Σταύρος (EL)
Βασιλάς, Νικόλαος (EL)
Χάρου, Ελένη (EL)

Τεχνολογικό Εκπαιδευτικό Ίδρυμα Αθήνας. Σχολή Τεχνολογικών Εφαρμογών. Τμήμα Μηχανικών Πληροφορικής Τ.Ε. (EL)

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)

other
conferenceItem

Geography (EN)
vector quantisation (EN)
γεωγραφία (EN)
image classification (EN)
Self-organizing maps (EN)
τηλεανίχνευση (EN)
remote sensing (EN)
ταξινόμηση εικόνας (EN)
αυτο-οργανούμενοι χάρτες (EN)
τεχνητή νοημοσύνη (EN)
artificial intelligence (EN)
διάνυσμα κβάντωσης (EN)

ΤΕΙ Αθήνας (EL)
Technological Educational Institute of Athens (EN)

13th International Conference on Digital Signal Processing (DSP97) (EN)

English

1997-06-02

DOI: 10.1109/ICDSP.1997.628531

IEEE (EN)



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