Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting
IEEE Transactions on Geoscience and Remote Sensing
By: J.C.-W. Chan, C. Huang, and R. DeFries
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Abstract
Two ensemble methods, bagging and boosting, were investigated for improving algorithm performance. Our results confirmed the theoretical explanation [1] 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.
| Publication type | Article |
|---|---|
| Publication Subtype | Journal Article |
| Title | Enhanced algorithm performance for land cover classification from remotely sensed data using bagging and boosting |
| Series title | IEEE Transactions on Geoscience and Remote Sensing |
| DOI | 10.1109/36.911126 |
| Volume | 39 |
| Issue | 3 |
| Year Published | 2001 |
| Language | English |
| Publisher | IEEE |
| Contributing office(s) | Earth Resources Observation and Science (EROS) Center |
| Description | 3 p. |
| First page | 693 |
| Last page | 695 |
| Google Analytic Metrics | Metrics page |