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Neural networks, multitemporal landsat thematic mapper data and topographic data to classify forest damages in the Czech republic

Author:
  • J. Ardö
  • P. Pilesjö
  • A. Skidmore
Publishing year: 1997
Language: English
Pages: 217-229
Publication/Series: Canadian Journal of Remote Sensing
Volume: 23
Issue: 3
Document type: Journal article
Publisher: NRC Research Press

Abstract english

This study uses multitemporal Landsat Thematic Mapper data and topographic data for the purpose of classifying coniferous forest damage in the Czech Republic using an artificial neural network. Comparing the neural network-based classification with earlier studies and a multinominal logistic regression using identical training and test data indicates that the back propagation algorithm is comparable, but not superior, to conventional methods. The dependence on the randomly set input weights and the more time-consuming back propagation training make neural network less useful for classification of forest damages than conventional classification algorithms. However, the ability to integrate and extract information from multisource data with different or unknown distributions are advantages of neural networks.

Keywords

  • Environmental Sciences

Other

Published
  • ISSN: 0703-8992
E-mail: jonas [dot] ardo [at] nateko [dot] lu [dot] se

Professor

Dept of Physical Geography and Ecosystem Science

+46 46 222 40 31

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Dept of Physical Geography and Ecosystem Science

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Department of Physical Geography and Ecosystem Science
Lund University
Sölvegatan 12
S-223 62 Lund
Sweden

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