SAR ATR based on Generalized Principal Component Analysis Integrating Class Information

Generalized principal component analysis integrating class information (ICGPCA) is proposed for feature extraction in this paper. Firstly we compute wavelet coefficients of images using DB2 wavelet and extract the approximate sub-image of wavelet transformation, and then extract the feature of the sub-image using ICGPCA which maximizes the between-class scatter and minimizes the within-class scatter. Experimental results adopting nearest neighbour classifier (NNC) and support vector machine (SVM) classifier show that the proposed method can extract effective features with lower dimensions, consequently enhance the correct probability of recognition and decrease the recognition computation effectively. The recognition rate without target azimuth information arrives at nearly 97%.