摘要
成像雷达具有全天时、全天候的观测能力,能够通过成像处理获得目标雷达图像信息,是对地观测、侦察监视等民用和军用领域中的重要遥感设备。高分辨率雷达图像能够提供目标的详细轮廓和精细结构,有利于后续目标分类识别等应用。对获取的雷达图像,如何利用信号和信息处理等理论方法进一步提升分辨率,突破分辨率瑞利极限,具有重要的科学研究和实际应用价值。另一方面,作为电磁波的重要属性之一,极化在目标特性的获取和挖掘中发挥着重要作用,能够为目标超分辨率重建带来丰富信息。为此,该文梳理了极化雷达图像目标超分辨率重建的概念及性能评价指标,并重点归纳整理了极化雷达图像目标超分辨率重建方法及其应用。最后,总结了现有方法的局限性并展望了未来的技术发展趋势。
Radar possesses the capability for all-day,all-weather observation and can generate radar target images through image processing.It serves as an indispensable piece of remote sensing equipment in various civil and military applications,including earth observation,and surveillance.High-resolution radar images can provide a detailed outline and fine structure of the target,which is conducive to subsequent applications such as target classification and recognition.For the acquired radar images,how to use theoretical methods such as signal and information processing to further improve the resolution and break through the Rayleigh limit has important scientific research and practical application value.On the other hand,polarization,a crucial attribute of electromagnetic waves,plays a significant role in the acquisition and analysis of target characteristics,and can provide rich information for super-resolution reconstruction.Accordingly,this work initially elucidates the concept of polarimetric radar image super-resolution reconstruction,summarizes the performance evaluation metrics,and primarily focuses on the methods of polarimetric radar image superresolution reconstruction and their applications.Lastly,the limitations of existing methods are summarized and potential future trends in technology are forecasted.
作者
李铭典
肖顺平
陈思伟
LI Mingdian;XIAO Shunping;CHEN Siwei(College of Electronic Science and Technology,National University of Defense Technology,Changsha 410073,China)
出处
《电子与信息学报》
EI
CAS
CSCD
北大核心
2024年第5期1806-1826,共21页
Journal of Electronics & Information Technology
基金
国家自然科学基金(62122091,61771480)
湖南省自然科学基金(2020JJ2034)。
关键词
雷达图像
极化
超分辨率重建
信号处理
深度学习
Radar image
Polarization
Super-resolution reconstruction
Signal processing
Deep learning