Random forest applied for dimension reduction and classification in hyperspectral data
Hyperspectral image contains a huge amount of data,there is great redundancy information in hyperspectral data and band selection can remove it effectively and reduce computational cost accordingly.As a type of integrated learning method,Random forest algorithm has been applied to classification and feature extraction,and great results have been made.OMIS hyperspectral image of experimental plot in Xiaotangshan,Beijing was used in the present work.Random forest algorithm was used to calculate the value of each feature index.For feature selection strategy oriented for efficiency and accuracy,the feature with low value was removed to obtain optimum combination of bands.Accuracy in the random forest classifier was found to be up to 72.82%,the SVM classification accuracy was 65.21%.Therefore random forest algorithm could adapt to hyperspectral image data and has better precision than SVM.
国家科技期刊平台
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