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