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.% 主成分分析法(PCA)等基于 L2范数最小均方准则的目标特征提取方法在合成孔径雷达(SAR)图像目标识别中得到广泛应用,L2范数易受 SAR 图像中野值的干扰,影响目标特征提取效果.介绍一种基于 L1范数双向二维主成分分析法(B2DPCA-L1)的目标特征提取方法.L1范数对野值有较强的鲁棒性,通过在 L1范数框架下实现 B2DPCA,有效地改善了样本中野值对特征提取的影响,同时减少了特征矩阵维数,提高了目标识别率.实验表明,所提出方法的识别性能优于基于 L2范数的特征提取方法.
展开▼
