Sample Discriminant Analysis for SAR ATR

Feature extraction is a key step in synthetic-aperture-radar automatic target recognition. In this letter, we propose a novel feature extraction method named sample discriminant analysis (SDA) that is based on the manifold learning theory. The method directly extracts features from 2-D image matrices rather than vectors. Furthermore, SDA preserves the neighborhood information of the original data in dimension reduction. It also makes within-class samples closer and makes between-class samples father away in a low-dimensional space. Meanwhile, a sample discriminant coefficient is employed in the method to give each sample a weight related to its location and similarity to neighboring samples. Thus, the discriminative ability of the method is improved. Experimental results based on the moving and stationary target acquisition and recognition database show that the proposed method can improve recognition performance.