Neural networks, multitemporal landsat thematic mapper data and topographic data to classify forest damages in the Czech republic
(1997) In Canadian Journal of Remote Sensing 23(3). p.217-229- Abstract
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... (More)
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.
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- author
- Ardö, J. LU ; Pilesjö, P. LU and Skidmore, A. LU
- organization
- publishing date
- 1997
- type
- Contribution to journal
- publication status
- published
- subject
- in
- Canadian Journal of Remote Sensing
- volume
- 23
- issue
- 3
- pages
- 13 pages
- publisher
- Taylor & Francis
- external identifiers
-
- scopus:0031221032
- ISSN
- 0703-8992
- DOI
- 10.1080/07038992.1997.10855204
- language
- English
- LU publication?
- yes
- id
- ae03ad86-fdd8-4264-b074-7d7592daddcf
- date added to LUP
- 2016-04-07 14:27:22
- date last changed
- 2022-01-30 02:28:17
@article{ae03ad86-fdd8-4264-b074-7d7592daddcf, abstract = {{<p>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.</p>}}, author = {{Ardö, J. and Pilesjö, P. and Skidmore, A.}}, issn = {{0703-8992}}, language = {{eng}}, number = {{3}}, pages = {{217--229}}, publisher = {{Taylor & Francis}}, series = {{Canadian Journal of Remote Sensing}}, title = {{Neural networks, multitemporal landsat thematic mapper data and topographic data to classify forest damages in the Czech republic}}, url = {{http://dx.doi.org/10.1080/07038992.1997.10855204}}, doi = {{10.1080/07038992.1997.10855204}}, volume = {{23}}, year = {{1997}}, }