摘要
为提高合成孔径雷达(SAR)图像目标识别性能,以传统稀疏表示分类(SRC)为基础,提出联合核稀疏表示分类(KSRC)和增强字典的方法。KSRC在SRC的基础上引入非线性核函数,从而提升分类器对于非线性数据关系的表征能力。增强字典在原始训练样本的基础上,通过噪声添加和部分遮挡扩展原始字典,提升其对典型扩展操作条件的适应能力。同时,增强字典在KSRC的作用下,可以进一步提升对其他相关扩展操作条件的覆盖程度,从而提升识别方法对于多类扩展操作条件的有效性。以MSTAR数据集为基础开展实验,设置了标准操作条件以及噪声干扰、部分遮挡、型号差异等扩展操作条件,实验结果显示了本文方法的优势性能。
In order to improve the performance of target recognition in SAR images,this paper proposes a method combining Kernel Sparse Representation-based Classification(KSRC)and augmented dictionary based on traditional Sparse Representation-based Classification(SRC).The KSRC introduces nonlinear kernel function on the basis of SRC,so as to improve the representation ability of the classifier for nonlinear data relationships.By using original training samples,the augmented dictionary expands the original dictionary through noise addition and partial occlusion to improve its adaptability to typical Extended Operating Conditions(EOC).At the same time,with the help of KSRC,the augmented dictionary further improves the coverage of other related EOCs,thus the effectiveness of the proposed method for other EOCs can be upgraded.Experiments carried on the MSTAR dataset under Standard Operating Conditions(SOC)and EOCs including noise interference and partial occlusion shown the superior performance of the proposed method.
作者
李振汕
丁柏圆
LI Zhenshan;DING Baiyuan(Information Technology College,Guangxi Police College,Nanning 530000,China;No.96901 Unit of PLA,Beijing 100000,China)
出处
《电光与控制》
CSCD
北大核心
2024年第8期44-49,共6页
Electronics Optics & Control
基金
国家自然科学基金(62001501)
广西新工科研究与实践项目(XGK202329)。
关键词
合成孔径雷达
目标识别
核稀疏表示分类
增强字典
扩展操作条件
SAR
target recognition
kernel sparse representation-based classification
augmented dictionary
extended operating condition
