Remote sensing has become an important exploratory method for the national census of traditional Chinese medicinal resources. We selected safflower（ Latin name: Carthamus tinctorius L.） as a sample species of cultivated Chinese medicinal plant species. We extracted textures from ZY-3 satellite images based on fractal theory and Gray Level Co-occurrence Matrix（ GLCM）methods to assist the supervised classification. Results showed that the overall accuracy increased by 0. 49% to 5. 31%. Kappa coefficient increased by 0. 01 to 0. 07 after textures were combined for classification processing. Accuracy with fractal-based textures improved at least twice as much as GLCM-based textures

particularly when fractal-based texture was extracted with a 5 × 5 sliding block in Matlab and then added. The classification accuracy of safflower increased to 100% when this parameter was combined with fractal-based textures; by contrast

this accuracy reduced by 0. 55% to 1. 28% with GLCM-based textures. Moreover

the s amples collected from the final recognition results based on fractal theory were relatively complete with a smaller degree of fragmentation and a higher distinction from other categories. Therefore

fractal-based textures can be used to assist in the recognition of cultivated Chinese medicinal plants. Textures based on fractal theory could effectively increase the classification accuracy of ZY-3 images at a higher extent than those based on GLCM.