For hyperspectral images,we proposed a feature extraction method call ed orthogonal exponential discriminant locality preserving projection (OEDLPP) based on discri minant locality preserving projection (DLPP).The OEDLPP algorithms not only retains the supervi sed characteristic of the DLPP algorithms,but also utilizes the matrix exponential to obtain more effective sample information,which can avoid the Small Samples Size problem.Me anwhile, Schimidt orthogonalization is used to the projection matrix in OEDLPP,which sol ved the problems of feature redundancy.Last but not least,we used support vector machi ne (SVM) to classify the hyperspectral images after using OEDLPP algorithms to extract featu re.Compared with several existing algorithms,such as principal component analysis (PCA),loc ality preserving projection (LPP),discriminant locality preserving projection (DLPP),exponential discriminant locality preserving projection (EDLPP) and orthogonal discriminant locality pres erving projection (ODLPP),the proposed algorithms has a certain superiority for obtaini ng the effective information of the sample,and the classification accuracy is improved by about 2%~3%.