A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
University of Science and Technology Beijing · Nanjing Forestry University
Abstract
Machine learning algorithms are increasingly used in various remote sensing applications due to their ability to identify nonlinear correlations. Ensemble algorithms have been included in many practical applications to improve prediction accuracy. We provide an overview of three widely used ensemble techniques: bagging, boosting, and stacking. We first identify the underlying principles of the algorithms and present an analysis of current literature. We summarize some typical applications of ensemble algorithms, which include predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature. Finally, we suggest…
Citation impact
- FWCI
- 49.13
- Percentile
- 100%
- References
- 123
Authors
3Topics & keywords
- Ensemble learning
- Boosting (machine learning)
- Computer science
- Machine learning
- Downscaling
- Artificial intelligence
- Algorithm
- Random forest
- Climate action