利用随机森林的城区机载LiDAR数据特征选择与分类
Airborne LiDAR Feature Selection for Urban Classification Using Random Forests
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摘要: 针对机载LiDAR系统数据分类中多源特征与城区分类目标相关性不明确的问题，在面向对象的数据特征挖掘基础上，提出了一种基于随机森林的机载LiDAR系统特征选择与分类方法，利用不同地区数据实验证明:本文方法能对机载LiDAR系统数据多源特征的重要性进行正确评估，通过特征选择，在减少特征的情况下仍能够达到较高的分类精度。Abstract: To the question that multisource features’contribution to classification is not explicit in airborne LiDAR system data，based on object oriented data mining，this paper proposed a method to select features for classification using Random Forest. It’s proved that the features' contribution can beevaluated correctly and the selected features can still make a high classification accuracy.
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