Random forest classifier for remote sensing classification
Random forest classifier for remote sensing classification
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DOI：
10.1080/01431160412331269698
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发表时间：
2005-01-10
影响因子：
3.4
通讯作者：
Pal, M
中科院分区：
文献类型：
--
作者：
Pal, M
增长的决策树的合奏，并允许他们投票最流行的类产生了显着增加的土地覆盖分类的分类精度。本研究的目的是呈现随机森林分类器获得的结果，并在分类准确性、训练时间和用户定义参数方面将其性能与支持向量机（SVMs）进行比较。Landsat增强型专题制图仪Plus（ETM+）数据的一个地区在英国与七个不同的土地覆盖。从这项研究的结果表明，随机森林分类器的分类精度和训练时间方面表现同样出色的支持向量机。这项研究还得出结论，随机森林分类器所需的用户自定义参数的数量比支持向量机所需的数量少，更容易定义。
Growing an ensemble of decision trees and allowing them to vote for the most popular class produced a significant increase in classification accuracy for land cover classification. The objective of this study is to present results obtained with the random forest classifier and to compare its performance with the support vector machines (SVMs) in terms of classification accuracy, training time and user defined parameters. Landsat Enhanced Thematic Mapper Plus (ETM+) data of an area in the UK with seven different land covers were used. Results from this study suggest that the random forest classifier performs equally well to SVMs in terms of classification accuracy and training time. This study also concludes that the number of user-defined parameters required by random forest classifiers is less than the number required for SVMs and easier to define.