A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
A Review of Ensemble Learning Algorithms Used in Remote Sensing Applications
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DOI：
10.3390/app12178654
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
Yuzhen Zhang;Jingjing Liu;W. Shen
中科院分区：
文献类型：
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作者：
Yuzhen Zhang;Jingjing Liu;W. Shen
机器学习算法由于具有识别非线性相关性的能力，在各种遥感应用中得到了越来越多的应用。集成算法已被应用于许多实际应用中，以提高预测精度。我们提供了三种广泛使用的合奏技术的概述：装袋、助推和堆叠。我们首先确定算法的基本原理，并对当前的文献进行分析。我们总结了集成算法的一些典型应用，包括预测作物产量、估计森林结构参数、绘制自然灾害图以及气候参数和地表温度的空间降尺度。最后，我们对集成算法在实际应用中的应用提出了未来的方向。
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.