Abstract: Due to the spectral heterogeneity of mixed pixel spectral with non-linear characteristics from remote sensing，the traditional linear spectral mixture model，called as linear spectral mixed mode called as LSMM，cannot solve the mixed pixel for mapping land cover fraction effectively. In order to improve the performance of unmixing mixed pixels，the paper introduced the support vector regression named as SVR model to address the non-linear spectral problem. SVR model owns the advantages of searching optimum balance ability with small amount training sample among complex model and study ability to achieve high application performance.  In paper，the SVR model was carried out with five steps in a simulated experiment. First，from real remote sensing image，one thousand typical vegetation and bare land spectral pixels were extracted from the classified remote sensing image which resolution is 2.5 m，respectively. Second，the training sample set to form the different vegetation fraction pixels，such as 0%，1%，2%，…，100%，was constructed from the vegetation and bare land. The simulated remote sensing images were produced for the further study. Finally，SVR model was carried out for unmixing the mixed pixels to extract the vegetation fraction. The two sensitive parameters，C and g，to ensure the SVR performance which was determined by grid-regression method. In order to validate the influence of unmixing accuracy with different amount training sample，1%，2%，…，10% sample SVR experiment were also carried out. The highest regression accuracy and most optimum model parameters produced by SVR regression model was 6% sample amount. Hence，small sample amount is enough for mixed pixels unmixing using SVR method，which is better than that of LSMM which need amount of endmembers deriving from remote sensing image to obtain the average spectrum of each land cover to estimate land cover fraction. SVR method can obtain higher accuracy （RMSE=5.95%，R²=0.958） than the conventional LSMM method （RMSE=7.71%，R²=0.932). Analysis with each vegetation fraction，SVR model also show higher accuracy and stable ability showing excellent characteristics of non-linear spectrum unmixing. Hence，the high training ability can gained with the limitation sample amount. From the whole scene and each true value fraction，SVR method show better performance than that of LSMM due to the non-linear mixed pixel complexity which is the inevitable for LSMM. For SVR，the small amount training sample can be used to represent the non-linear spectral space to determine the optimum hyperplane. Hence，the SVR is suitable to unmix the mixed pixels to solve the non-linear spectral problem. Furthermore，the SVR method can be applied for multi-endmember model from the actual remote sensing in the future to validate the performance of SVR unmixing model.

Key words: support vector regression, linear spectral mixed model, non-linear, mixed pixel unmixing