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In view of the low recognition accuracy and long time used of traditional synthetic aperture radar (SAR) image recognition algorithms, a SAR image recognition algorithm based on non-subsampled contourlet transform (NSCT) and support vector machine (SVM) was proposed. Firstly, the image is decomposed at multiple scales through NSCT to obtain the high-frequency and low-frequency components. Then histogram of oriented gradient (HOG) was extracted from the high-frequency component, and LBP (Local Binary Pattern) algorithm was used to extract texture features from the low-frequency component. After that the extracted high and low frequency features are combined and divided by support vector machine. Finally, the algorithm proposed is tested by testing set. Experimental results show that this method can not only effectively improve the SAR image recognition accuracy, the recognition rate on the MSTAR database reaches 90.7%, but also robust to the coherent speckle.