Abstract: In the past two decades, Synthetic Aperture Radar(SAR) has received more and more attention. Many supervised algorithms have been proposed for automatic target recognition. In this paper, an unsupervised learning algorithm named K-means clustering is adopted, By coding the patch of the image and adjusting the receptive field size of the patch,the distinguish features can be learned from the input data for automatic target recognition. Furthermore, more training data is generated by using the data augmentation via rotating the azimuth of the target and adding a random integer to the original image for improving the training performance of the proposed algorithm. Experimental results on the public MSTAR database have shown that the proposed method can achieve the state-of-art accuracy, which is 96.67%.