DocumentCode :
2062941
Title :
Multispectral classification of LANDSAT TM data using a cooperative learning neural network
Author :
Kawamura, Makoto ; Tsujiko, Y.
Author_Institution :
Dept. of Regional Planning, Toyohashi Univ. of Technol., Japan
fYear :
1993
fDate :
18-21 Aug 1993
Firstpage :
508
Abstract :
The authors propose a multilayer multistep backpropagation algorithm which consists of some category extraction networks and a unification network for the land cover classification of remotely sensed data. Each extraction network is used to select the most suitable category. All output patterns of the extraction networks are unified in the unification network. This methodology called a cooperative learning can ensure and accelerate the learning convergence
Keywords :
backpropagation; feedforward neural nets; geophysical techniques; geophysics computing; image recognition; learning (artificial intelligence); remote sensing; LANDSAT TM; category extraction; cooperative learning neural network; geophysical measurement technique; image processing; land cover; land surface terrain mapping; multilayer multistep backpropagation algorithm; multispectral image classification; neural net; optical IR infrared; remote sensing; unification network; Acceleration; Computer hacking; Data mining; Multi-layer neural network; Neural networks; Pixel; Remote sensing; Satellites; Statistical analysis; Technology planning;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Geoscience and Remote Sensing Symposium, 1993. IGARSS '93. Better Understanding of Earth Environment., International
Conference_Location :
Tokyo
Print_ISBN :
0-7803-1240-6
Type :
conf
DOI :
10.1109/IGARSS.1993.322596
Filename :
322596
Link To Document :
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