电波科学学报2019，Vol.34Issue(1)：60-64,5.DOI:10.13443/j.cjors.2018062101
一种基于自适应核字典学习的SAR目标识别方法
SAR target recognition methed based on adaptive kernel dictionary learning
摘要
Abstract
A synthetic aperture radar (SAR) target recognition method based on adaptive kernel dictionary learning is proposed in order to enhance the ability of sparse representation to extract non-linear feature information. Firstly, the SAR image feature information is mapped into a high-dimensional kernel space through a kernel function, and then the dictionary is learned in the high-dimensional kernel space. Next, the sparsity is dynamically calculated according to the information of each dictionary update. Finally, the SAR target recognition is achieved by minimizing the reconstruction error. The simulation results on MSTAR data sets show that the feature information extracted by this method can be highly indexed and has better performance on SAR target recognition.关键词
SAR图像/目标识别/自适应核字典学习/核稀疏/最小重构误差Key words
SAR image/target recognition/adaptive kernel dictionary learning (AKDL)/sparsity/minimum reconstruction error分类
信息技术与安全科学引用本文复制引用
王彩云,黄盼盼,胡允侃..一种基于自适应核字典学习的SAR目标识别方法[J].电波科学学报,2019,34(1):60-64,5.基金项目
国家自然基金青年科学基金 (61301211) （61301211）
江苏省研究生教育教学改革课题 (JGZZ17_008) （JGZZ17_008）
