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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Vydáno v:IEEE transactions on geoscience and remote sensing Ročník 39; číslo 3; s. 693 - 695
Hlavní autoři: Jonathan Cheung-Wai Chan, Chengquan Huang, DeFries, R.
Médium: Journal Article
Jazyk:angličtina
Vydáno: New York, NY IEEE 01.03.2001
Institute of Electrical and Electronics Engineers
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
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
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Popis
Shrnutí: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