SAR target feature extraction and recognition based on L1-norm B2DPCA

The Minimum Mean-Square based methods like Principal Component Analysis(PCA) are widely used in Synthetic Aperture Radar(SAR) target recognition.However the L2-norm based criteria is prone to be affected by the outliers,which is not good for the target feature extraction in SAR imagery.To solve this problem,a L1-norm based bilateral two Dimension Principal Component Analysis(B2DPCA-L1) is proposed.The L1-norm version of B2DPCA is robust to outliers,and reduces the dimension of feature matrix and improves the target recognition rate as well.Experiments show that the proposed method has a higher target recognition rate in SAR imagery compared with the traditional L2-norm based feature extraction methods.