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Petter Pilesjö

Petter Pilesjö

Professor

Petter Pilesjö

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

Summary, in 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.

Department/s

  • Dept of Physical Geography and Ecosystem Science

Publishing year

1997

Language

English

Pages

217-229

Publication/Series

Canadian Journal of Remote Sensing

Volume

23

Issue

3

Document type

Journal article

Publisher

Taylor & Francis

Topic

  • Environmental Sciences

Status

Published

ISBN/ISSN/Other

  • ISSN: 0703-8992