We introduce the projection pursuit regression (PPR)analysis for remote sensing image classification and describe the implementation of the PPR model used in remote sensing image classification. Using a TM image of the Guangzhou area for our classification tests

we get satisfactory classification result

after optimizing the parameters in the projection pursuit regression model by shuffled frog leaping algorithm. Furthermore

we discuss the setting of the projection center as well as the influence of the optimal algorithms and the number of ridge functions on the classification accuracy in the PPR model. The results show that the model is easy to realize and very stable.The number of ridge functions in the projection pursuit regression model has no significant influence on the classification accuracy.