过度拟合
分类器(UML)
随机森林
遥感
离群值
模式识别(心理学)
维数之咒
机器学习
计算机科学
决策树
数据挖掘
地理
人工智能
人工神经网络
作者
Mariana Belgiu,Lucian Drăguţ
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2016-02-14
卷期号:114: 24-31
被引量:4784
标识
DOI:10.1016/j.isprsjprs.2016.01.011
摘要
A random forest (RF) classifier is an ensemble classifier that produces multiple decision trees, using a randomly selected subset of training samples and variables. This classifier has become popular within the remote sensing community due to the accuracy of its classifications. The overall objective of this work was to review the utilization of RF classifier in remote sensing. This review has revealed that RF classifier can successfully handle high data dimensionality and multicolinearity, being both fast and insensitive to overfitting. It is, however, sensitive to the sampling design. The variable importance (VI) measurement provided by the RF classifier has been extensively exploited in different scenarios, for example to reduce the number of dimensions of hyperspectral data, to identify the most relevant multisource remote sensing and geographic data, and to select the most suitable season to classify particular target classes. Further investigations are required into less commonly exploited uses of this classifier, such as for sample proximity analysis to detect and remove outliers in the training samples.
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