极化SAR图像舰船目标检测研究综述

刘涛 杨子渊 蒋燕妮 高贵

刘涛, 杨子渊, 蒋燕妮, 等. 极化SAR图像舰船目标检测研究综述[J]. 雷达学报, 2021, 10(1): 1–19. doi: 10.12000/JR20155
引用本文: 刘涛, 杨子渊, 蒋燕妮, 等. 极化SAR图像舰船目标检测研究综述[J]. 雷达学报, 2021, 10(1): 1–19. doi: 10.12000/JR20155
LIU Tao, YANG Ziyuan, JIANG Yanni, et al. Review of ship detection in polarimetric synthetic aperture imagery[J].Journal of Radars, 2021, 10(1): 1–19. doi: 10.12000/JR20155
Citation: LIU Tao, YANG Ziyuan, JIANG Yanni, et al. Review of ship detection in polarimetric synthetic aperture imagery[J].Journal of Radars, 2021, 10(1): 1–19. doi: 10.12000/JR20155

极化SAR图像舰船目标检测研究综述

doi: 10.12000/JR20155
基金项目: 国家自然科学基金(61771483)
详细信息
    作者简介:

    刘涛:刘 涛(1978–),男,山东人,博士,海军工程大学教授,博士生导师。主要研究方向为雷达极化统计理论、极化信息处理、雷达极化检测与识别、电子战系统建模与仿真等。E-mail: liutao1018@sina.com

    杨子渊(1997–),男,湖北人,海军工程大学博士研究生。主要研究方向为雷达极化信息处理、合成孔径雷达运动目标检测、新体制雷达等。E-mail: yzy_199702@sina.com

    蒋燕妮(1987–),女,湖北人,海军工程大学博士研究生。主要研究方向为雷达极化信息处理、合成孔径雷达舰船目标尾迹检测、高频雷达海态信息反演等。E-mail: asiajiang2005@163.com

    高贵:高 贵(1981–),内蒙古人,博士,西南交通大学地球科学与环境工程学院副院长,教授,博士生导师。主要研究方向为遥感信息处理、人工智能、新体制雷达系统工程研制。E-mail: dellar@126.com

    通讯作者:

    刘涛 liutao1018@sina.com

    高贵 dellar@126.com

  • 责任主编:陈思伟 Corresponding Editor: CHEN Siwei
  • 中图分类号: TN95

Review of Ship Detection in Polarimetric Synthetic Aperture Imagery (in English)

Funds: The National Natural Science Foundation of China (61771483)
More Information
    Author Bio:

    LIU Tao received his B.S. and Ph.D. degrees from the National University of Defense Technology (NUDT), Changsha, China, in 2001 and 2007, respectively. Since 2007, he has been with the School of Electronic Engineering, Naval University of Engineering (NUE), Wuhan, China, where he is currently a professor. His research interests include the statistical theory of radar polarization, polarization information processing, Synthetic Aperture Radar (SAR) automatic target recognition, statistical modeling of SAR image, SAR ship detection, Interferometric SAR (InSAR), SAR Ground Moving Target Indication (GMTI), and artificial intelligence

    YANG Ziyuan received his B.S. degree in radar engineering from NUE, Wuhan, China, in 2019. He is currently pursuing his Ph.D. degree in information and communication engineering at NUE. His research interests include radar polarization information processing, electronic warfare system modeling, and SAR-GMTI

    JIANG Yanni received her Master’s degree in radio physics from Wuhan University, Wuhan, China, in 2011. She is currently teaching, as well as studying for a Doctoral degree in communication and information systems, in NUE, Wuhan, China. Her research interests include radar polarization information processing and high-frequency radar signal processing

    GAO Gui received his B.S. degree in information engineering and M.S. and Ph.D. degrees in remote sensing information processing from NUDT, Changsha, China, in 2002, 2003, and 2007, respectively. From 2017, he was with Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, China, where he is currently a professor. His current research interests include radar signal processing, InSAR, target detection, marine environment, and SAR-GMTI

    Corresponding author: LIU Tao, liutao1018@sina.comGAO Gui, dellar@126.com
  • 摘要: 极化合成孔径雷达(PolSAR)使用二维脉冲压缩技术获取高分辨力极化信息图像,目前已广泛应用在军事侦察、地形测绘、环境与自然灾害监视、海上舰船目标检测等领域。如何解决复杂海杂波的建模与参数估计、慢小目标检测、密集目标检测等问题仍然是当前PolSAR图像舰船目标检测的难点。该文归纳梳理了PolSAR图像舰船目标检测的4类主流方法:极化特征目标检测方法、慢速运动目标检测方法、舰船目标尾迹检测方法以及基于深度学习的目标检测方法等,同时给出了各类方法所存在的问题以及可能的解决方法,并预测了其未来研究重点和发展趋势。

     

  • 图  1  PolSAR舰船目标检测方法分类

    Figure  1.  Classification of PolSAR ship detection methods

    图  2  常用极化优化检测方法的性能对比[18]

    Figure  2.  Performance comparison of common polarimetric optimization detection methods[18]

    图  3  各类纹理分布PWF处理结果[21]

    Figure  3.  PWF processing results with different textural distributions[21]

    图  4  基于自适应截断法的PolSAR图像密集目标检测[28]

    Figure  4.  Dense ship detection in PolSAR images based on the adaptive truncation method[28]

    图  5  PNF与NPNF性能对比[8]

    Figure  5.  Performance comparison of PNF and NPNF[8]

    图  6  不同检测方法的小目标检测效果对比[36]

    Figure  6.  Comparison of detection results among different methods for small targets[36]

    图  7  不同舰船目标检测方法与SPN的对比结果[51]

    Figure  7.  Comparison results among different ship detectors with SPN[51]

    图  8  不同舰船目标检测方法与NPCM的对比[6]

    Figure  8.  Performance comparision among different ship detection methods with NPCM[6]

    图  9  PolSAR图像不同极化通道动目标检测效果图[69]

    Figure  9.  Moving target detection results in different polarimetric channels in PolSAR images[69]

    图  10  极化顺轨干涉动目标检测仿真结果[76]

    Figure  10.  Simulation results of polarimetric along-track interferometry moving target detection[76]

    图  11  PWF与Radon变换实现尾流检测[90]

    Figure  11.  Wake detection results by PWF and Radon transform[90]

    图  12  低秩稀疏分解与极化结合的舰船尾流检测结果[98]

    Figure  12.  Ship wake detection results of low-rank and sparse decomposition combined with polarization[98]

    图  13  P2P-CNN网络结构及其检测性能[107]

    Figure  13.  P2P-CNN network structure and detection performance[107]

    图  1  Classification of PolSAR ship detection methods

    图  2  Performance comparison of common polarimetric optimization detection methods[18]

    图  3  PWF processing results with different textural distributions[21]

    图  4  Dense ship detection in PolSAR images based on the adaptive truncation method[28]

    图  5  Performance comparison of PNF and NPNF[8]

    图  6  Comparison of detection results among different methods for small targets[36]

    图  7  Comparison results among different ship detectors with SPN[51]

    图  8  Performance comparision among different ship detection methods with NPCM[6]

    图  9  Moving target detection results in different polarimetric channels in PolSAR images[69]

    图  10  Simulation results of polarimetric along-track interferometry moving target detection[76]

    图  11  Wake detection results by PWF and Radon transform[90]

    图  12  Ship wake detection results of low-rank and sparse decomposition combined with polarization[98]

    图  13  P2P-CNN network structure and detection performance[107]

    表  1  PolSAR图像舰船目标检测方法适用场景和优缺点总结

    Table  1.   Applicable scenarios and pros & cons summary of PolSAR ship detection methods

    检测分类 适用场景 具体检测方法 优点 缺点
    目标极化特征检测 主要适用于舰船目标本体极化特征和杂波特征有一定差异情形下的检测 简单极化合成检测技术 早期PolSAR目标检测方法,简单易实现且效果优于单极化SAR图像目标检测方法 一定程度上利用了幅度或相位信息,对极化信息的利用不够充分
    基于极化最优化的检测技术 1. 充分利用了各极化通道相关信息
    2. 通过对PolSAR图像进行优化,达到最佳对比效果
    要根据实际情况选择不同的极化优化准则,同时滑窗的选择也对性能影响较大
    基于散射机理的检测技术 具有较强的物理可解释性 对目标类型和海况状态的适应性需要提高
    基于空间邻域的检测技术 融合空域与极化信息,大幅增强目标检测能力 后期数据处理需要降维以避免维数灾难
    慢速运动目标检测 主要适用于目标杂波极化特征差异较小但有一定速度差异的情形,同时也能提取目标运动信息以获取实时海面态势 单通道慢动目标检测 利用交叉极化信息增强慢动目标的检测效果 静止目标模糊抑制和慢动目标检测难以同时实现
    虚拟干涉慢动目标检测 解决了实际干涉数据来源缺乏的问题 子孔径分解的个数与重叠度的选择较为困难
    多通道慢动目标检测 融合多平台和极化信息的优势,极大增强运动目标检测能力 复杂结构和成本限制工程应用
    舰船目标尾迹检测 不是检测舰船目标本体,而是检测运动产生的尾迹,主要针对小目标和
    隐身目标
    尾迹边缘线性特征检测 检测问题抽象成线形特征检测问题,方法相对简单 杂波背景下不同类型弱尾迹的检测困难
    尾迹区域背景差异检测 尾迹区域和背景海面由于散射机理不同,在频谱特性、统计特征、极化特性等方面均存在较大差异,效果好 理论分析和方法实现更加复杂
    基于深度学习的目标检测 不需要人工提取目标特征,在PolSAR图像智能解译中取得了巨大成功 基于卷积神经网络的检测方法 结构灵活、能够自动提取结构化特征,不仅能提取图像的低维特征,而且能提取图像的高维特征 1. 需要大量样本进行训练
    2. 可解释性有待研究
    下载: 导出CSV

    表  1  Applicable scenarios and pros & cons summary of PolSAR ship detection methods 

    Detection method Advantage Disadvantage
    Target polarimetric characteristic detection method (mainly applied to the detection of ship and clutter when the polarimetric characteristics are different) Simple polarimetric channel
    synthesis technology
    The early PolSAR target detection method is simple and easy to realize, and the effect is better than the single PolSAR image target detection method To some extent, the amplitude or phase information is used, but the polarimetric information is not fully utilized
    Polarimetric optimization
    detection technology
    1. The information of each polarimetric channel is fully utilized
    2. PolSAR images are optimized to achieve the best contrast effect
    Different optimization criteria should be selected according to the actual situation, and the selection of a sliding window also has a significant influence on the performance
    Detection technology based on the
    scattering mechanism
    It has strong physical interpretability Adaptability to target types and sea state needs to be improved
    Spatial neighborhood detection
    method
    The fusion of spatial and polarimetric information considerably enhances the target detection capability Dimensions need to be reduced to avoid a dimensional disaster
    Slow-moving target detection method (mainly applied to the situation where the polarimetric characteristic difference between target and clutter is small but thesre is a certain velocity difference; simultaneously, it can also extract the target motion information to obtain the real-time sea surface situation) Single-channel PolSAR-GMTI Cross-polarimetric information is used to enhance the detection effect of slow-moving targets Suppression of stationary targets and detection of slow-moving targets are difficult to achieve simultaneously
    Virtual interferometric
    slow-moving target detection
    The problem of the lack of actual interference data source is solved It is difficult to choose the number of subaperture decomposition and overlap degree
    Multichannel PolSAR-GMTI Integrating the advantages of multi-platform and polarization information considerably enhances the ability to move target detection Complex structure and cost limit engineering applications
    Ship wake detection method (it is used not to detect the ship target body but to detect the wake generated by the movement, mainly for small targets and stealth targets) Linear feature detection of
    wake edge
    The detection problem is abstracted into the linear feature detection problem, and the method is relatively simple It is difficult to detect different types of weak wakes in the cluttered background
    Background difference detection
    in the wake region
    Due to the different scattering mechanisms of the wake region and background sea surface, there are considerable differences in spectrum characteristics, statistical characteristics, polarimetric characteristics, and other aspects, and the effect is good The theoretical analysis and method implementation are more complex
    Target detection algorithm based on deep learning (it has achieved great success in intelligent PolSAR image interpretation without extracting target features manually) Detection method based on CNN Flexible structure can automatically extract structured features, including both low-dimensional and high-dimensional features of images 1. It needs a large number of samples to train with
    2. Interpretability needs to be investigated
    下载: 导出CSV
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  • 收稿日期:  2020-12-31
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