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Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification

Lien Loosvelt (UGent) , Jan Peters (UGent) , Henning Skriver, Hans Lievens (UGent) , Frieke Vancoillie (UGent) , Bernard De Baets (UGent) and Niko Verhoest (UGent)
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Keywords
Random Forests, Synthetic aperture radar (SAR), Multi-frequency, Multi-date, Land cover, Crop classification, Model uncertainty, Prediction probability, Data fusion, Entropy, LAND-COVER CLASSIFICATION, REMOTE-SENSING DATA, POLARIMETRIC SAR, UNSUPERVISED CLASSIFICATION, DISTRIBUTION MODELS, AGRICULTURAL CROPS, CATEGORICAL-DATA, NEURAL-NETWORK, RADAR DATA, ERROR

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Citation

Please use this url to cite or link to this publication:

MLA
Loosvelt, Lien, Jan Peters, Henning Skriver, et al. “Random Forests as a Tool for Estimating Uncertainty at Pixel-level in SAR Image Classification.” INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION 19 (2012): 173–184. Print.
APA
Loosvelt, L., Peters, J., Skriver, H., Lievens, H., Vancoillie, F., De Baets, B., & Verhoest, N. (2012). Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, 19, 173–184.
Chicago author-date
Loosvelt, Lien, Jan Peters, Henning Skriver, Hans Lievens, Frieke Vancoillie, Bernard De Baets, and Niko Verhoest. 2012. “Random Forests as a Tool for Estimating Uncertainty at Pixel-level in SAR Image Classification.” International Journal of Applied Earth Observation and Geoinformation 19: 173–184.
Chicago author-date (all authors)
Loosvelt, Lien, Jan Peters, Henning Skriver, Hans Lievens, Frieke Vancoillie, Bernard De Baets, and Niko Verhoest. 2012. “Random Forests as a Tool for Estimating Uncertainty at Pixel-level in SAR Image Classification.” International Journal of Applied Earth Observation and Geoinformation 19: 173–184.
Vancouver
1.
Loosvelt L, Peters J, Skriver H, Lievens H, Vancoillie F, De Baets B, et al. Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification. INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION. 2012;19:173–84.
IEEE
[1]
L. Loosvelt et al., “Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification,” INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION, vol. 19, pp. 173–184, 2012.
@article{2974967,
  author       = {Loosvelt, Lien and Peters, Jan and Skriver, Henning and Lievens, Hans and Vancoillie, Frieke and De Baets, Bernard and Verhoest, Niko},
  issn         = {0303-2434},
  journal      = {INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION},
  keywords     = {Random Forests,Synthetic aperture radar (SAR),Multi-frequency,Multi-date,Land cover,Crop classification,Model uncertainty,Prediction probability,Data fusion,Entropy,LAND-COVER CLASSIFICATION,REMOTE-SENSING DATA,POLARIMETRIC SAR,UNSUPERVISED CLASSIFICATION,DISTRIBUTION MODELS,AGRICULTURAL CROPS,CATEGORICAL-DATA,NEURAL-NETWORK,RADAR DATA,ERROR},
  language     = {eng},
  pages        = {173--184},
  title        = {Random forests as a tool for estimating uncertainty at pixel-level in SAR image classification},
  url          = {http://dx.doi.org/10.1016/j.jag.2012.05.011},
  volume       = {19},
  year         = {2012},
}

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