Vegetation Classification in Hyperspectral Image with CART Decision Tree

To improve the accuracy of vegetation classification,the influences of training sample sizes and terrain should be considered when extracting vegetation information from the hyperspectral image.Taking the Changbai Mountain as the study area,this paper built a decision tree model based on CART(Classification and Regression Tree) algorithm to classify vegetation in hyperspectral image.In order to reduce the influence of the mixed pixels,using PPI(Pixel Purity Index) to extract pure pixel as the training samples.CART decision tree was built based on these classification feature variables,such as vegetation index,texture,terrain and so on,the tree was applied on vegetation classification and the result was compared with the maximum likelihood classification.The result showed that CART decision tree method combined with spectrum,texture and terrain,had a better effect of classification.