Jonas Ardö
Professor
Neural networks, multitemporal landsat thematic mapper data and topographic data to classify forest damages in the Czech republic
Författare
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.
Avdelning/ar
- Institutionen för naturgeografi och ekosystemvetenskap
Publiceringsår
1997
Språk
Engelska
Sidor
217-229
Publikation/Tidskrift/Serie
Canadian Journal of Remote Sensing
Volym
23
Issue
3
Länkar
Dokumenttyp
Artikel i tidskrift
Förlag
Taylor & Francis
Ämne
- Environmental Sciences
Status
Published
ISBN/ISSN/Övrigt
- ISSN: 0703-8992