生成式对抗网络在SAR图像处理中的应用综述
Overview of the application of generative adversarial networks in SAR image processing
高丹 1吴晓芳 1温志津1
作者信息
- 1. 军事科学院 系统工程研究院,北京 100191
- 折叠
摘要
合成孔径雷达自动目标识别技术是SAR图像处理领域的研究热点,但数据样本不足的情况导致SAR-ATR应用研究受到局限.传统扩充SAR数据集的图像仿真技术模型复杂、计算量大,生成图像不够逼真.生成式对抗网络GAN不需要目标先验信息,可以直接从真实图像数据中生成逼真的图像,具有低损耗和端到端的优点,因此相较于传统方法其更适用于小样本SAR数据高质量扩充.围绕GANs在SAR图像处理中的研究应用展开叙述,介绍了获取目标SAR图像的方法,包括传统的仿真技术和基于深度学习的GANs技术;从目标图像和场景图像等2 个方面介绍了GANs训练的常用SAR数据集;针对不同数据集的应用场景,重点介绍了GANs网络在目标SAR图像生成、SAR超分辨率重建、SAR和光学影像融合等 3 个方面的最新研究进展;最后,结合深度学习和 SAR目标特性,给出了GANs网络在SAR图像应用方面的后续发展建议.
Abstract
Synthetic aperture radar(SAR)automatic target recognition(ATR)technology is a research hotspot in the field of SAR image processing,but the situation of insufficient data samples leads to the limitation of SAR-ATR application research.The traditional image simulation techniques for expanding SAR datasets have complex models,large computation,and the generated images are not realistic enough.Generative Adversarial Networks GANs do not need target prior information and can generate realistic images directly from real image data,which has the advantages of low loss and end-to-end,so it is more suitable for high quality expansion of small sample SAR data compared with traditional methods.The article focuses on the research and application of GANs in SAR image processing,and introduces the methods for acquiring target SAR images,including traditional simulation technology and GANs technology based on deep learning.The commonly used SAR datasets for GANs training are introduced from the aspects of target images and scene images.Aiming at the application scenarios of different datasets,the latest research progress of GAN networks in target SAR image generation,SAR super-resolution reconstruction,SAR and optical image fusion is mainly introduced.Finally,the article combines with deep learning and SAR target characteristics,we give the suggestions for the subsequent development of GANs network in SAR image applications.
关键词
合成孔径雷达/生成式对抗网络/SAR数据集/高逼真图像生成Key words
synthetic aperture radar/generative adversarial networks/SAR datasets/high-fidelity image generation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据