基于随机森林的大姚县 TM 遥感影像分类研究

摘要

Random Forest is a new classification algorithm , which is combined with a classifier of multi-ple decision trees .By using Landsat-TM data of Dayao County , Chuxiong Province , and taking three classification methods including maximum likelihood , support vector machine and random forest , the su-periority of random forest classifier are analyzed .The results show that the classification precision of sup-port vector machine and random forest classification is obviously superior to the maximum likelihood , and they have little difference in the precision .At the classified time , maximum likelihood is significantly fas-ter than random forests and support vector machine , and support vector machine is the slowest one .Ac-cording to comprehensive analysis , random forest method is the best one , it does not only ensure the clas-sified precision , but also guarantee efficiency;it is more suitable for actual production applications.%随机森林（ Random Forest ）是一种组合多棵决策树分类器的新的分类算法。以楚雄州大姚县为例，采用Landsat－TM数据，通过最大似然、支持向量机、随机森林3种分类器进行分类对比研究。结果表明，支持向量机和随机森林的分类精度明显优于最大似然法，两者分类精度相差不大；在分类时间上，最大似然法明显比随机森林和支持向量机快，支持向量机最慢。综合分析，随机森林算法表现更优，它在保证分类精度的前提下，也能保证一定的时间效率，更适宜实际生产应用。