摘要
星载合成孔径雷达(synthetic aperture radar, SAR)是空间遥感信息获取的主要手段之一。凭借全天时、全天候和穿透性等技术优势,星载SAR如今已成为推动国防建设和助力经济发展的重要引擎,在军事侦察、应急保障和信息服务等领域均具有广泛应用。星载SAR信号主要涉及回波获取、成像处理和图像应用等环节,本文以星载SAR数据链路为主线,综合分析星载SAR领域的发展现状、前沿动态、热点问题等。首先回顾星载SAR系统及其数据集的发展现状,对比国内外星载SAR系统的关键参数,梳理不同空间分辨率、极化方式和工作频段的星载SAR数据集。其次分析成像技术体制的创新,重点阐述星载SAR在多维度观测和高分宽幅成像方面的进展。最后介绍智能处理技术与SAR图像应用的融合,探讨机器学习和深度学习在SAR数据处理和分析的潜力。本文总结了星载SAR技术的现状、未来发展趋势以及面临的主要挑战,对相关领域的研究具有重要的参考价值。
Mounted on satellites or spaceborne platforms,spaceborne synthetic aperture radar(SAR)utilizes transmitted signals to capture detailed surface information,irrespective of the time of day or weather conditions.The combination of allweather,all-time operational capacity and deep penetration into the atmosphere makes spaceborne SAR an indispensable tool for a broad spectrum of applications,including military reconnaissance,environmental monitoring,disaster manage⁃ment,and resource management.The signals from spaceborne SAR involve echo acquisition,imaging processing,and image applications.This paper focuses on the data link of spaceborne SAR and comprehensively analyzes the current state of development,cutting-edge trends,and pressing issues in the field of spaceborne SAR.It begins by reviewing the current state of spaceborne SAR systems,with an emphasis on the development of SAR platforms and the datasets they produce.A comparative analysis of the key parameters of domestic and international spaceborne SAR systems is presented,highlight⁃ing the technological capabilities,operational constraints,and strategic goals behind these systems.Notable parameters include the system’s frequency bands,spatial resolution,polarization modes,and swath width,which all influence the potential applications and limitations of the data generated.Recent advances in spaceborne SAR imaging technology have significantly expanded the capabilities of these systems.One of the most prominent innovations is multidimensional obser⁃vation,which allows SAR systems to acquire data from multiple viewpoints or angles within a single acquisition cycle.It involves several key innovations,including multiband,multipolarity,and double/multiple base cooperative detection,all of which contribute to enhanced imaging and a more comprehensive understanding of the observed area.Each of these com⁃ponents offers unique advantages that complement each other in providing more detailed,accurate,and varied insights into the Earth’s surface.Multiband SAR refers to the use of different frequency bands in a single SAR system or through the combination of different systems operating in different bands.The use of multiple frequency bands allows for the capture of complementary information about the target area.Multipolarity refers to the use of multiple polarization modes in SAR sys⁃tems,where the transmitted and received radar waves are polarized in different orientations.This approach significantly enhances the ability to distinguish between different surface types and materials based on their interaction with polarized electromagnetic waves.Double or multiple base cooperative detection refers to the integration of data from multiple SAR platforms or sensor locations,operating cooperatively to observe the same area from different angles or baselines.By lever⁃aging multiple sensors operating in different locations or with different baselines,multibase cooperative detection enhances the depth and precision of SAR observations,providing richer datasets for change detection and surface movement analy⁃sis.Another critical advancement is the development of high-resolution wide-swath imaging,which involves multichannel technique,varied pulse repetition frequency,and MIMO SAR.The multichannel technique involves the use of multiple receiving channels within the SAR system,allowing for simultaneous reception of signals from different parts of the radar beam.By utilizing multiple channels,SAR systems can cover a larger area with greater detail,as the signals from various channels are processed in parallel.The varied pulse repetition frequency is a technique used to adjust the interval between successive radar pulses based on the specific operational requirements of the SAR system.By dynamically changing the pulse repetition rate,the system can optimize the trade-off between resolution and coverage,depending on the target’s dis⁃tance from the radar and the desired imaging resolution.MIMO SAR represents a groundbreaking innovation in radar tech⁃nology that employs multiple transmitting and receiving antennas simultaneously.By using a combination of multiple input and output signals,MIMO SAR enhances the radar system’s ability to gather detailed information from a large area while maintaining high resolution.This technique allows for the simultaneous acquisition of data from different angles,which improves the swath width and imagery resolution.The increasing volume,complexity,and diversity of data produced by spaceborne SAR systems have created a demand for advanced processing and analytical techniques capable of handling large datasets and extracting meaningful insights efficiently.Traditional image processing methods,which often rely on manual intervention and domain expertise,have limitations in terms of speed,scalability,and adaptability.By contrast,intelligent processing leveraging machine learning(ML)and deep learning(DL)has revolutionized SAR data analysis,enabling automated,accurate,and scalable solutions for various applications,including classification,target detection,change detection,and anomaly detection.These intelligent techniques enhance SAR systems by improving their data inter⁃pretation capabilities,reducing the reliance on manual processes and enabling real-time data analysis.Common ML meth⁃ods include support vector machine,Markov random field,dictionary learning,decision trees,and unsupervised cluster⁃ing.Compared to traditional image processing techniques,ML methods offer significant advantages in rapidly selecting from large volumes of known information.These advantages include fewer hyperparameters,high processing efficiency,and strong adaptability,making ML methods particularly suitable for tasks that involve complex data analysis and real-time decision making.DL is an advanced artificial intelligence approach characterized by its ability to learn effective features from large datasets in a hierarchical manner,significantly reducing the complexity and error associated with manual feature extraction.Common DL architectures include convolutional neural networks,deep belief networks,stacked autoencoders,and transformer networks.DL-based image data processing methods,through the stacking of multiple layers of neural net⁃works,can automatically extract more abstract and higher-level target features directly from raw data,thereby enhancing the overall accuracy of prediction and recognition tasks.Over the past decades,significant progress has been made in spaceborne SAR technology,with notable developments in several key areas.These include the advancement of constellation-based SAR and lightweight SAR systems,high-resolution and wide-swath imaging,multipolarization and arbi⁃trary frequency band imaging,intelligent data processing,and the application of interferometric SAR(InSAR)and differ⁃ential InSAR for complex scene analysis.However,several challenges persist,including the control of attitude and orbit errors,the transmission and storage of massive data volumes,target interpretation in complex scenes,and susceptibility to electromagnetic interference and external noise.These issues continue to pose significant obstacles to the further advance⁃ment and operational deployment of spaceborne SAR systems,necessitating ongoing research and technological innovation to address them.
作者
李春升
徐华平
张家伟
孙兵
尤亚楠
刘慧
Li Chunsheng;Xu Huaping;Zhang Jiawei;Sun Bing;You Ya’nan;Liu Hui(School of Electronic Information Engineering,Beihang University,Beijing 100191,China;School of Information Science and Engineering,Yanshan University,Qinhuangdao 066004,China;School of Artificial Intelligence,Beijing University of Posts and Telecommunications,Beijing 100876,China;School of Intelligence Science and Technology,Beijing University of Civil Engineering and Architecture,Beijing 102616,China)
出处
《中国图象图形学报》
北大核心
2025年第6期2257-2274,共18页
Journal of Image and Graphics
基金
国家自然科学基金项目(U2241202)
河北省自然科学基金项目(F2023203013)
河北省教育厅高等学校科技计划项目(QN2024203)。
作者简介
李春升,男,教授,主要研究方向为星载SAR总体、成像处理和图像解译。E-mail:lics@buaa.edu.cn;通信作者:徐华平,女,教授,主要研究方向为星载SAR信号处理、干涉SAR和SAR图像处理。E-mail:xuhuaping@buaa.edu.cn;张家伟,男,讲师,主要研究方向为SAR波形设计、成像处理和最优化算法。E-mail:zhangjw@ysu.edu.cn;孙兵,男,副教授,主要研究方向为新体制雷达设计与仿真、SAR信号处理与图像质量评估。E-mail:bingsun@buaa.edu.cn;尤亚楠,男,副教授,主要研究方向为遥感图像智能解译、目标智能检测识别和多源数据融合。E-mail:youyanan@bupt.edu.cn;刘慧,女,副教授,主要研究方向为微波视觉三维及高维成像、凝视成像SAR/Lidar三维点云处理、城市建筑三维点云精细重构。E-mail:liuhui@bucea.edu.cn。
