Classification of high-dimensional hyperspectral data with many spectral bands for the derivation of good accuracy is an important problem in hyperspectral remote sensing .The most of classification algorithms are based on spectral information .Here, in order to achieve an high classification accuracy, we can use the spatial information of data. Integration of hidden morkov random field that optimize spatial information by minimizing energy functions, with support vector machine that is an powerful method for classification of hyperspectral data, can improve classification accuracy in final classified map  properly. The purpose of this study is to improve the classification accuracy with a limited of training samples by combination of support vector machine algorithm and hidden morkov random field. In this study, tow hyperspectral dataset from Hyperion and AVIRIS sensors has been used. After the applying radiometric corrections like correcting embedded lines and remove bad bands , atmospheric correction Hyperion dataset done by FLAASH method and AVIRIS dataset by IAR algorithm. MNF transformation was used in order to dimensionally reduction and the endmembers were extracted from PPI band and then in order to spectral classification, used from SVM method. Finally, to improve classification accuracy in the final classified map, hidden Markov random field (HMRF) was used. So that after the extracting of Components from PCA and MNF Transformations, computing of some statistic parameters of classes in SVM classified map in order to use in inputs model and so configuration of iterations, SVM-HMRF model was applied.

The results show that the proposed model (SVM-HMRF) has improved overall classification accuracy in both of data sets. For example, the improved classification accuracy on some of land uses, were around 25 percent. Also regions of final classified map is much more homogeneous and salt and pepper nose drastically reduced.