多线性主成分分析和张量分析的SAR图像目标识别  

SAR Image Target Recognition Based on Multi-linear Principal Component Analysis and Tensor Analysis

作　　者：宦若虹[1] 陶一凡 陈月 杨鹏 鲍晟霖 Huan Ruohong;Tao Yifan;Chen Yue;Yang Peng;Bao Shenglin(College of Computer Science and Technology,Zhejiang University of Technology,Hangzhou,310023,China)

机构地区：[1]浙江工业大学计算机科学与技术学院,杭州310023

出　　处：《数据采集与处理》2018年第5期872-879,共8页Journal of Data Acquisition and Processing

基　　金：国家自然科学基金(61302129)资助项目

摘　　要：为了提高合成孔径雷达图像目标识别效果,提出一种基于多线性主成分分析和张量分析的合成孔径雷达图像目标识别方法。该方法首先构建四阶张量训练样本,利用多线性主成分分析得到多线性投影矩阵;再通过投影矩阵构建核心张量,对核心张量进行线性判别分析;最后对测试样本分类识别。实验中,将本文提出的多线性主成分分析和张量分析方法在MSTAR公共数据库上进行识别实验,并与主成分分析和二维主成分分析方法进行识别率比较。实验结果表明,本文方法有效保留了图像的空间结构信息,提高了目标正确识别率。For enhancing the target recognition effect of synthetic aperture radar image,a method of synthetic aperture radar image target recognition based on multi-linear principal component analysis and tensor analysis is proposed in this paper.Firstly,a four-order tensor training sample is constructed.Then,multi-linear principal component analysis is used to get the multi-linear projection matrix,and the core tensor is obtained from the multi-linear projection matrix.Finally,linear discriminant analysis is used to train the core tensor and classify the test samples.In the experiments,the proposed multilinear principal component analysis and tensor analysis method in this paper is applied to MSTAR public database for recognition experiments,and compared with principal component analysis and two-dimensional principal component analysis in recognition rate.Experimental results show that the method effectively preserves the image structure information and improves the target recognition rate.

关 键 词：合成孔径雷达 目标识别 多线性主成分分析 张量分析 

分 类 号：TN959.17[电子电信—信号与信息处理]