SAR图像飞机目标智能检测识别技术研究进展与展望

罗汝 赵凌君 何奇山 计科峰 匡纲要

罗汝, 赵凌君, 何奇山, 等. SAR图像飞机目标智能检测识别技术研究进展与展望[J]. 雷达学报(中英文), 2024, 13(2): 307–330. doi: 10.12000/JR23056
引用本文: 罗汝, 赵凌君, 何奇山, 等. SAR图像飞机目标智能检测识别技术研究进展与展望[J]. 雷达学报(中英文), 2024, 13(2): 307–330. doi: 10.12000/JR23056
LUO Ru, ZHAO Lingjun, HE Qishan, et al. Intelligent technology for aircraft detection and recognition through SAR imagery: Advancements and prospects[J]. Journal of Radars, 2024, 13(2): 307–330. doi: 10.12000/JR23056
Citation: LUO Ru, ZHAO Lingjun, HE Qishan, et al. Intelligent technology for aircraft detection and recognition through SAR imagery: Advancements and prospects[J]. Journal of Radars, 2024, 13(2): 307–330. doi: 10.12000/JR23056

SAR图像飞机目标智能检测识别技术研究进展与展望

doi: 10.12000/JR23056
基金项目: 国家自然科学基金(62001480),湖南省自然科学基金(2021JJ40684),卫星信息智能处理与应用技术重点实验室自主研究基金(2022-ZZKY-JJ-10-02)
详细信息
    作者简介:

    罗 汝,博士生,研究方向为SAR图像解译、可解释人工智能、深度学习、目标检测与识别技术

    赵凌君,副教授,研究方向为遥感信息处理、合成孔径雷达目标自动识别等

    何奇山,博士生,研究方向为SAR目标检测识别、深度学习

    计科峰,博士,教授,博士生导师。研究方向为合成孔径雷达(SAR)目标电磁散射特性建模、特征提取、检测识别以及多源空天遥感图像智能处理与解译基础理论、核心关键技术以及系统集成与应用等

    匡纲要,博士,教授,博士生导师,研究方向为遥感图像智能解译、SAR图像目标检测与识别

    通讯作者:

    赵凌君 nudtzlj@163.com

  • 责任主编:孙显 Corresponding Editor: SUN Xian
  • 中图分类号: TN957.51

Intelligent Technology for Aircraft Detection and Recognition through SAR Imagery: Advancements and Prospects

Funds: The National Natural Science Foundation of China (62001480), Hunan Provincial Natural Science Foundation of China (2021JJ40684), Research Funding of Satellite Information Intelligent Processing and Application Research Laboratory (2022-ZZKY-JJ-10-02)
More Information
  • 摘要: 合成孔径雷达(SAR)采用相干成像机制,具有全天时、全天候成像的独特优势。飞机目标作为一种典型高价值目标,其检测与识别已成为SAR图像解译领域的研究热点。近年来,深度学习技术的引入,极大提升了SAR图像飞机目标检测与识别的性能。该文结合团队在SAR图像目标特别是飞机目标的检测与识别理论、算法及应用等方面的长期研究积累,对基于深度学习的SAR图像飞机目标检测与识别进行了全面回顾和综述,深入分析了SAR图像飞机目标特性及检测识别难点,总结了最新的研究进展以及不同方法的特点和应用场景,汇总整理了公开数据集及常用性能评估指标,最后,探讨了该领域研究面临的挑战和发展趋势。

     

  • 图  1  典型SAR ATR系统示意图

    Figure  1.  Schematic diagram of the typical SAR ATR system

    图  2  添加形状先验前后飞机目标重建结果比对[17]

    Figure  2.  Comparison of aircraft target reconstruction results with/without shape prior[17]

    图  3  基于伽马分布的CFAR飞机目标检测结果[19]

    Figure  3.  An application example of aircraft detection with Gamma-based CFAR algorithm[19]

    图  4  基于梯度纹理显著性的SAR图像飞机目标检测结果[20]

    Figure  4.  Example of aircraft detection based on gradient textural saliency map in SAR imagery[20]

    图  5  飞机目标部件结构

    Figure  5.  Component structure of aircraft

    图  6  基于GMM的飞机目标散射结构特征建模结果[24]

    Figure  6.  Modeling results of scattering structure feature of aircraft based on GMM[24]

    图  7  SAR图像飞机、车辆、船只目标示例

    Figure  7.  Examples of typical targets in SAR imagery, including aircraft, vehicle, and ship

    图  8  SAR图像飞机目标示例

    Figure  8.  Examples of the aircraft in SAR imagery

    图  9  尺度多样、弱小目标示例

    Figure  9.  Examples of aircraft with multi-scale and weak imaging

    图  10  不同型号飞机目标外观相似示例(图中展示了来自Gaofen-3和HISEA-1成像的KC-135和C-135两型飞机)

    Figure  10.  Examples of similar appearance of different aircraft (the KC-135 and C-135 from Gaofen-3 and HISEA-1 imaging)

    图  11  6种民用飞机的光学和SAR影像示例

    Figure  11.  Examples of optical and SAR images for six types of civil aircraft

    图  12  复杂背景干扰示例

    Figure  12.  The interference from the complex background conditions

    图  13  基于深度学习的通用目标检测识别算法示意图

    Figure  13.  Schematic diagram of general deep-learning based object detection and recognition algorithms

    图  14  SAR图像飞机目标检测与识别技术发展总结

    Figure  14.  The summary diagram of aircraft detection and recognition in SAR imagery

    图  15  高质量仿真数据获取技术流程[88]

    Figure  15.  Technical process of collecting high-quality simulation samples[88]

    图  16  SADD数据集上主流检测网络性能比较

    Figure  16.  Performance comparison of mainstream detection networks on SADD dataset

    图  17  不同训练数据使用率下主流分类网络性能比较

    Figure  17.  Performance comparison of mainstream classification networks under different sample utilization rates

    图  18  SAR图像飞机目标检测识别算法发展趋势示意图

    Figure  18.  Development trend diagram for aircraft detection and recognition algorithm in SAR imagery

    图  19  开放环境下高精度、强鲁棒的飞机目标检测识别模型构建示意图

    Figure  19.  Schematic diagram of constructing aircraft detection and recognition model with high precision and strong robust under open environment

    表  1  SAR图像飞机目标检测与识别实测数据集

    Table  1.   Public datasets for aircraft detection and recognition in SAR imagery

    应用领域数据集名称数据采集平台数据集内容及特点
    目标检测SADD数据集
    (Zhang et al., 2022)[62]
    德国
    TerraSAR-X
    ● 在X波段和HH模式下成像,图像分辨率从0.5 m到3.0 m。
    ● 数据集背景复杂、尺度目标多样,存在大量小尺寸目标,还包含了一部分负样本(机场附近的空地和森林等)。
    ● 数据总量为2966幅,其中飞机目标图像884幅,共计7835架飞机。图像大小为224像素×224像素。
    MSAR-1.0数据集
    (陈杰等,2022)[90]
    HISEA-1, Gaofen-3● 数据集的采集场景多样,包括飞机、油罐、桥梁和船只4类目标。
    ● 数据总量为28449幅,其中飞机目标图像108幅,共计6368架飞机。图像大小为256像素×256像素。
    目标识别多角度SAR数据集
    (王汝意等,2022)[83]
    无人机载SAR● 以角度间隔5°,采集了72个不同方位下的飞机目标实测数据。
    ● 数据集包含两类飞机目标:大棕熊100和“空中拖拉机”AT-504,数据总量为144幅,图像大小为128像素×128像素。
    SAR-ACD数据集
    (Sun et al., 2022)[78]
    Gaofen-3● 数据集包括6个民用飞机类别,14个其他飞机类别,共计4322架飞机。
    ● 目前民用飞机类别已开源,数据量共3032幅。其中,6类飞机目标:A220, A320/321, A330, ARJ21, Boeing737和Boeing787的图像分别为464, 512, 510, 514, 528, 504幅。
    ● 为飞机目标细粒度识别提供了数据基准。
    下载: 导出CSV

    表  2  SAR图像飞机目标仿真数据集

    Table  2.   Simulation SAR datasets of aircraft targets

    数据集仿真平台内容及特点
    SPGAN-SAR
    (Liu et al., 2018)[88]
    OpenSARSim[89]● 数据集包含飞机、船只和车辆3类目标,可细分为10个子类。每个子类包括504幅仿真图像,图像大小为158像素×158像素。
    IRIS-SAR数据集
    (Ahmadibeni et al., 2020)[9597]
    IRIS[98]● 包含6类目标,分别为48架民用飞机,58架小型螺旋桨飞机,82架喷气式飞机,29架民用和54架非民用直升机,24辆民用和28辆非民用车辆,以及32艘船只,共355个CAD模型。
    ● 展示了355个CAD模型在5个俯仰角(从15°开始,增量为15°),12个方位角(从0°开始,增量为30°)和3个探测距离(100 m, 200 m, 300 m)下生成的多角度SAR仿真数据集。
    ● 数据总量为63900幅,图像大小为512像素×512像素。可用于目标分类和图像去斑研究。
    下载: 导出CSV
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  • 收稿日期:  2023-04-25
  • 修回日期:  2023-06-26
  • 网络出版日期:  2023-07-13
  • 刊出日期:  2024-04-28

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