Spectral Mixture Analysis（SMA） is a straightforward and efficient approach to the spectral decomposition of hyperspectral remotely sensed scenes

Once a SMA model is developed

land cover proportions can be estimated from pixel values through model inversion.In this paper

we propose to estimate abundances from hyperspectral image using support vector regression（SVR） method.SVR method for abundance estimation can be essentially regarded as function approximation and generalization problem.Differing from other nonlinear regressive approaches which require predefined nonlinear mapping functions

this method transferred each spectral pixel into a high-dimension feature space by a kernel function

which will result in a spectral pixel in a feature space consisting of possibly many nonlinear combinations of the spectral bands of the original spectral signature.In this way the higher order relationships between the mixed pixels are exploited in the feature space.Projection iterative method has been used for endmember abstraction from the image

and then simulating nonlinear training and testing data by Hapke’s approximation function.Experiment of simulating data and real hyperspectral image（Pushbroom Hyperspectral Imager

PHI） are conducted to validate the procedures.The experiments show that the method can provide better result of abundance estimation for hyperspectral image as compared with that of radial basis function-neural networks.In our simulating test

over 97% of the total pixels in the image lie within the bound of ±0.1

and the RMSE are no more than 3.5%.