DocumentCode :
3135785
Title :
Fast and efficient land-cover classification of multispectral remote sensing data using artificial neural network techniques
Author :
Vassilas, N. ; Charou, E. ; Varoufakis, S.
Author_Institution :
NRC Demokritos, Paraskevi, Greece
Volume :
2
fYear :
1997
fDate :
2-4 Jul 1997
Firstpage :
995
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
Keywords :
geography; image classification; learning (artificial intelligence); remote sensing; self-organising feature maps; vector quantisation; SOM; VQ; artificial neural network techniques; automatic classification; data compression; efficient clustering; land-cover classification; memory-efficient methodology; multispectral remote sensing data; self-organizing maps; supervised classification; time-efficient methodology; training set size reduction; unsupervised classification; vector quantization; Books; Clustering algorithms; Digital signal processing; Euclidean distance; Image coding; Pixel; Remote sensing; Satellites; Vector quantization;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Signal Processing Proceedings, 1997. DSP 97., 1997 13th International Conference on
Conference_Location :
Santorini
Print_ISBN :
0-7803-4137-6
Type :
conf
DOI :
10.1109/ICDSP.1997.628531
Filename :
628531
Link To Document :
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