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According to the classification of remote sensing images result in inaccurate classifier existing problems, this paper proposes a method for classification of remote sensing image vegetation based on decision tree classifier, this method is based on the composite decision tree Boost Tree, texture feature firstly extracted using remote sensing images blanket model in fractal theory, according to the characteristics of remote sensing image the classification, the construction of a new single decision tree algorithm to classify the remote sensing image. The improved decision tree classification based on fractal texture features and spectral features is realized by using the Landsat7 ETM data source as the research area in the five ring area of Beijing. The experimental results show that the texture features extracted from carpet models can express the surface features very well, and the classification accuracy of the improved decision tree supplemented with the texture information is improved compared with the spectral information only, and the classification effect is improved.