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

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

国防科技大学电子科学学院电子信息系统复杂电磁环境效应国家重点实验室长沙 410073

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
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出版历程
- 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

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

State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China

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

Corresponding author: ZHAO Lingjun, nudtzlj@163.com

摘要

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。
- 合成孔径雷达 /
- 目标检测与识别 /
- 飞机目标 /
- 深度学习 /
- 可解释人工智能
Abstract: Synthetic Aperture Radar (SAR), with its coherent imaging mechanism, has the unique advantage of all-day and all-weather imaging. As a typical and important topic, aircraft detection and recognition have been widely studied in the field of SAR image interpretation. With the introduction of deep learning, the performance of aircraft detection and recognition, which is based on SAR imagery, has considerably improved. This paper combines the expertise gathered by our research team on the theory, algorithms, and applications of SAR image-based target detection and recognition, particularly aircraft. Additionally, this paper presents a comprehensive review of deep learning-powered aircraft detection and recognition based on SAR imagery. This review includes a detailed analysis of the aircraft target characteristics and current challenges associated with SAR image-based detection and recognition. Furthermore, the review summarizes the latest research advancements, characteristics, and application scenarios of various technologies and collates public datasets and performance evaluation metrics. Finally, several challenges and potential research prospects are discussed.
- Synthetic Aperture Radar (SAR) /
- Detection and recognition /
- Aircraft targets /
- Deep learning /
- eXplainable Artificial Intelligence (XAI)

HTML全文

图 1 典型SAR ATR系统示意图

Figure 1. Schematic diagram of the typical SAR ATR system

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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]

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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]

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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]

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图 5 飞机目标部件结构

Figure 5. Component structure of aircraft

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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]

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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

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图 8 SAR图像飞机目标示例

Figure 8. Examples of the aircraft in SAR imagery

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图 9 尺度多样、弱小目标示例

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

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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)

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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

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图 12 复杂背景干扰示例

Figure 12. The interference from the complex background conditions

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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

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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

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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]

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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

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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

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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

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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

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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。
目标检测	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。
目标识别	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。

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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)^[95–97]	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。

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