面向SAR图像目标识别的鲁棒处理算法  

Robust processing algorithm for SAR image target recognition

作　　者：刘中杰[1,2] 曹云峰[3] 庄丽葵[3] 丁萌[4] 王西超[1] 

机构地区：[1]南京航空航天大学自动化学院,江苏南京210016 [2]空军驻京昌地区军事代表室,北京100041 [3]南京航空航天大学航天学院,江苏南京210016 [4]南京航空航天大学民航学院,江苏南京210016

出　　处：《系统工程与电子技术》2013年第12期2489-2494,共6页Systems Engineering and Electronics

基　　金：国家自然科学基金(61203170);中国博士后基金特别资助(2013T60539);江苏省普通高校研究生科研创新计划(CXLX12_0160)资助课题

摘　　要：现有合成孔径雷达图像的目标识别方法通常要进行预处理,预处理对于识别率影响较大。但是,针对不同的合成孔径雷达目标图像,预处理算法的自适应性很难得到保证。将基于核的主成分分析与稀疏表示相结合,只需很少的观测数据就能得到高识别率的目标识别结果,节省了数据存储量和计算量。首先,阐述了压缩感知的基本理论;其次,提出了基于核主成分分析和稀疏表示的合成孔径雷达图像目标识别算法;最后,选取MSTAR数据库中的5类目标进行实验。仿真结果表明,在没有方位角预测的情况下,该算法仍能有效地识别目标,与其他识别算法相比,在同等噪声污染的图像下,具有较高的识别率。With the existing synthetic aperture radar （SAR） image target recognition algorithm, image pre-processing has to be usually carried out. Preprocessing has a significant impact on the recognition rate. Howev-er, the adaptability of the preprocessing algorithm is difficult to be guaranteed. This paper proposes to apply the theory of kernel principal component analysis （KPCA） and sparse representation to the image to he recognited, thus achieving a target recognition result possessing a high recognition rate with only a few observation data and saving the data storage and computation. This paper describes the basic theory of compressed sensing first, and proposes an SAR image target recognition algorithm based on KPCA and sparse representation. An experiment is carried out with five kinds of SAR targets in the MSTAR database. The simulation results show that this pro-posed algorithm is still able to recognize the target effectively without prediction of the attitude angle. Compared with other recognition algorithms, it has a higher recognition rate to the image under the noise pollution on an e-qual basis.

关 键 词：目标识别 压缩感知 稀疏表示 核主成分分析 合成孔径雷达图像 

分 类 号：TP753[自动化与计算机技术—检测技术与自动化装置]