Abstract: To solve the Small Sample Size (SSS) problem and the "inferior" problem of traditional Fisher Linear Discriminant Analysis (FLDA) when it is applied to Synthetic Aperture Radar (SAR) image recognition tasks, a new image feature extraction technique was proposed based on weighted two-directional and two-dimensional linear discriminant analysis (W(2D)2LDA). First, the scatter matrices in the two-directional and two-dimensional linear discriminant analysis criterion were modified by adding weights. Then, feature matrix was extracted by W(2D)2LDA. The experimental results with MSTAR dataset verify the effectiveness of the proposed method, and it can strengthen the feature's discrimination and obtain better recognition performance with fewer memory requirements simultaneously.

Key words: Synthetic Aperture Radar (SAR), target recognition, Linear Discriminant Analysis (LDA), inferiority, Small Sample Size (SSS

摘要： 为解决传统Fisher线性鉴别分析(LDA)在SAR图像目标识别中存在的“小样本”问题和“次优性”问题，提出一种基于加权的两向二维线性鉴别分析方法(W(2D)2LDA)。该方法对两向二维线性鉴别分析准则中散度矩阵的构造进行加入权值的改进，采用加权的两向二维鉴别准则函数进行特征提取,从理论上有效解决了 “次优性”问题，并缓解了“小样本”问题。对美国运动与静止目标获取与识别(MSTAR)计划录取的SAR图像数据进行的仿真实验结果表明,该算法增强了提取特征的可鉴别性，能够以较小的特征维数和运算量获得更高的识别率,验证了该算法的有效性。

关键词: 合成孔径雷达, 目标识别, 线性鉴别分析, 次优性, 小样本