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2-dimensional principal component analysis (2DPCA) has received more and more attentions in recent years, since it can evaluate the covariance matrix more accurate than PCA in extracting features from 2-dimensional images. However, a drawback of 2DPCA is that it needs more features than PCA because 2DPCA only eliminates the correlations between rows. In this paper, two-stage 2DPCA is proposed to extract features from synthetic aperture radar (SAR) images to further compress the dimension of features and decrease the recognition computation. Experimental results based on MSTAR data indicate that two-stage 2DPCA can decrease feature dimensions significantly, and the target recognition performance can be improved at the same time.