基于快速提升KLDA准则的MSTARSAR目标特征提取与识别研究

 核线性判别准则(KLDA)是一种非线性特征提取准则。利用KLDA提取MSTAR SAR图像特征，既达到较理想的识别概率，又可克服SAR图像对方位的敏感性。但此时训练样本最多，KLDA的计算代价高。为了解决这一问题，提出一种快速特征向量选择法（FFVS）。FFVS把类别和方位相似的SAR图像分成若干组，然后快速选择各组中部分图像组成一个集合且其到高维特征空间的映射作为一组基。利用该组基的线性组合表示任一样本和投影算子，降低了KLDA中核矩阵的阶数，达到降低计算代价的目的。实验结果表明，FFVS与KLDA组合能达到理想的识别结果。

Study on Fast Improving KLDA Criterion for MSTAR SAR Feature Extraction and Recognition

Kernel linear discriminant analysis (KLDA) is essentially a nonlinear feature extraction criterion. This work uses KLDA to extract the feature of MSTAR SAR images, through which the recognition rate is high and the intrinsic azimuth sensitivity in SAR image is overcome. At the same time, the KLDA computation cost is too high on the condition of more training samples. In order to deal with it, a Fast feature vector select (FFVS) scheme is adopted that divides the total images into several groups according to the dissimilarity of target’s classes and poses in image.  The FFVS can fast select a part of images from each group as a subset whose mapping in high dimension feature space forms a basis.  Each sample and the projection operator can be expressed by a linear combination of the basis, so the size of KLDA kernel matrix is decreased and the computation cost is reduced.  Experimental results show a good recognition performance is achieved by use of the hybrid algorithm that combines FFVS and KLDA.