Abstract: Considering to the texture differences among foresty types, texture features of forestry were firstly extracted from remote sensing imagery with discrete wavelet transformation, and the imagery was then classified combining objectoriented classification method with the original spectral values and texture features. Six texture measure factors which can reflect the difference efficiently among plant difference are sieved out based on analyzing of wavelet decomposition level, sliding window and texture measure. This method could solve the problems of forestry type classification, tree species classification, etc. The overall classification accuracy reached 92.7 %, and it is much better than traditional method of classification based on pixel values.