A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications

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.

Automated summary

Machine learning algorithms are increasingly used in remote sensing applications for their ability to identify nonlinear correlations. This article provides an overview of three widely used ensemble techniques: bagging, boosting, and stacking. It summarizes the underlying principles of the algorithms and analyzes the current literature. The article also presents typical applications of ensemble algorithms in predicting crop yield, estimating forest structure parameters, mapping natural hazards, and spatial downscaling of climate parameters and land surface temperature.