结合三维电磁散射模型和深度学习的SAR目标识别框架设计
Framework design of SAR target recognition via combination of 3-D scattering center model and deep learning
丁柏圆 1周春雨1
作者信息
- 1. 中国人民解放军96901部队,北京 100094
- 折叠
摘要
不断提高合成孔径雷达(SAR)图像目标识别能力对于全天时、全天候战场情报侦察具有重要意义.近年来,深度学习模型在SAR目标识别领域得到了广泛应用和验证,但由于SAR图像样本往往十分有限,模型的适应性受到一定制约.提出结合三维电磁散射模型和深度学习的SAR目标识别框架,充分运用三维电磁散射模型在目标SAR数据生成以及物理属性描述方面的优势,提升深度学习模型的分类可靠性和适应性.
Abstract
It is of important meaning to consistently improve Synthetic Aperture Radar(SAR)target recog-nition capability for all-day,all-weather battlefield reconnaissance.In recent years,deep leaning models have widely used and verified in the field of SAR target recognition.However,as the samples of SAR images are often very limited,the adaptivity of the models is restricted to some extent.A framework for SAR target recognition is proposed via the combination of 3-D scattering center model and deep learning,which comprehensively employs the advantages of 3-D scattering center model for SAR data generation and physical properties descriptions thus improving the classification reliability and adaptivity of deep learning models.
关键词
合成孔径雷达/目标识别/三维电磁散射模型/深度学习Key words
SAR/target recognition/3-D scattering center model/deep learning引用本文复制引用
出版年
2024
NETL
NSTL
万方数据