Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting

Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. The authors' results confirmed the theoretical explanation of L. Breiman (1996) that bagging improves unstable, but not stable, learning algorithms. While boosting enhanced accuracy of a weak lear...

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Veröffentlicht in:IEEE transactions on geoscience and remote sensing Jg. 39; H. 3; S. 693 - 695
Hauptverfasser: Jonathan Cheung-Wai Chan, Chengquan Huang, DeFries, R.
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York, NY IEEE 01.03.2001
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:
Aggregates
Applied geophysics
Bagging
Boosting
Earth sciences
Earth, ocean, space
Exact sciences and technology
Geography
Image resolution
Internal geophysics
MODIS
Pressing
Radiometry
Sampling methods
Voting
algorithms
algorithm performance
radiometry
accuracy
remote sensing
classification
high resolution
ISSN:0196-2892, 1558-0644
Online-Zugang:Volltext
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Beschreibung
Zusammenfassung:Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. The authors' results confirmed the theoretical explanation of L. Breiman (1996) that bagging improves unstable, but not stable, learning algorithms. While boosting enhanced accuracy of a weak learner, its behavior is subject to the characteristics of each learning algorithm.
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ISSN:0196-2892
1558-0644
DOI:10.1109/36.911126