Study on fine classification of vegetation using GF-1 image based on feature optimization
Fine classification of vegetation is of great significance for understanding the role of different vegetation in maintaining ecological environment security.This paper selects the domestic Gaofen 1(GF-1)satellite image,constructs a multi feature space based on spectrum,texture and vegeta-tion index under the image,selects ReliefF and CFS algorithms for feature screening,and finally com-bines the random forest algorithm and libsvm model to study the impact of feature selection on classifica-tion accuracy,and obtain the best multi feature classification algorithm model for fine classification of vegetation.The results show that feature selection can improve the classification accuracy to a certain ex-tent.Compared with ReliefF algorithm,CFS algorithm can better simplify the dimension of feature sub-set,and obtain better preferred subset to improve classification accuracy.The refined classification meth-od of vegetation based on CFS-libsvm has higher classification accuracy.
fine classification of vegetationReliefF algorithmCFS algorithmfeature optimization
NETL
NSTL
万方数据
维普
