Research Area:  Machine Learning

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 future directions for using ensemble algorithms in practical applications.

Keywords:  
Bagging
Boosting
Stacking
Remote sensing applications
Ensemble

Author(s) Name:  Yuzhen Zhang, Jingjing Liu

Publisher name:  MDPI

DOI:  10.3390/app12178654

Volume Information:  Volume 12

Paper Link:   https://www.mdpi.com/2076-3417/12/17/8654