SAR图像飞机目标检测识别进展

郭倩; 王海鹏; 徐丰

doi:10.12000/JR20020

SAR图像飞机目标检测识别进展

DOI: 10.12000/JR20020

复旦大学电磁波信息科学教育部重点实验室上海 200433

基金项目: 国家自然科学基金(61991422)

详细信息

作者简介:
郭　倩(1996–)，女，山西平遥人，复旦大学电磁波信息科学教育部重点实验室博士研究生，主要研究方向为雷达成像与智能感知技术。E-mail: 18210720055@fudan.edu.cn

王海鹏(1979–)，男，河南遂平人，复旦大学电磁波信息科学教育部重点实验室教授，研究方向为雷达系统设计与算法开发、遥感图像处理与信息获取、机器学习与目标识别、智能图像处理等。E-mail: hpwang@fudan.edu.cn

徐　丰(1982–)，男，浙江东阳人，复旦大学博士学位，教授，复旦大学电磁波信息科学教育部重点实验室副主任，研究方向为SAR图像解译、电磁散射建模、人工智能，兼职：IEEE地球科学与遥感快报副主编、IEEE地球科学与遥感学会上海分会主席。E-mail: fengxu@fudan.edu.cn

通讯作者:
王海鹏 hpwang@fudan.edu.cn

责任主编：高鑫 Corresponding Editor: GAO Xin
中图分类号: TN957.51
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出版历程
- 收稿日期: 2020-03-17
- 修回日期: 2020-05-29
- 网络出版日期: 2020-06-01

Research Progress on Aircraft Detection and Recognition in SAR Imagery

Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (61991422)

More Information

Corresponding author: WANG Haipeng, hpwang@fudan.edu.cn

摘要

摘要: 目标检测与识别是高分辨合成孔径雷达(SAR)领域的热点问题。机场上飞机作为一种典型目标，其检测和识别有一定的独特性。该文回顾了SAR图像典型目标检测识别领域技术的发展过程，分析了SAR图像中飞机目标的散射机制及面临的技术难点，阐述了 SAR 飞机目标检测识别的系统流程、技术路线和关键科学问题，对基于传统与基于深度学习两个方面的飞机目标检测识别的研究进展进行了归纳总结，并讨论了各类方法的特点及存在的问题，展望了未来的发展趋势。该文认为如何将深度学习与目标电磁散射机理结合、提高网络或模型的泛化能力是提升SAR图像中目标检测识别精度的关键，并给出了一种基于散射信息与深度学习融合的飞机目标检测方法。
- 合成孔径雷达 /
- 飞机检测 /
- 飞机识别 /
- 散射信息 /
- 深度学习
Abstract: Target detection and recognition are popular issues in the field of high-resolution Synthetic Aperture Radar (SAR). As a typical target, aircraft detection and identification has certain uniqueness. This paper reviews the development of detection and recognition techniques for a typical target in SAR imagery, analyzes the scattering mechanism and technical difficulties of aircraft in SAR imagery, describes the system flow, technical routes, and key scientific problems of target aircraft detection and recognition in SAR imagery, summarizes the research progress from traditional methods to deep-learning-based methods for aircraft detection and recognition, discusses the characteristics and existing problems of various methods, and predicts the future development trend. This paper proposes that combining target electromagnetic scattering mechanism with deep convolutional neural network to improve the generalization capability of the model is the key to improve SAR detection and recognition performance. Moreover, this paper establishes an aircraft detection method based on the fusion of scattering information and deep convolutional neural network.
- Synthetic Aperture Radar(SAR) /
- Aircraft detection /
- Aircraft recognition /
- Scattering information /
- Deep learning

HTML全文

图 1 深度学习目标检测算法发展

Figure 1. Development of deep learning-based target detection algorithms

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图 2 飞机目标检测识别系统流程

Figure 2. Flow of aircraft detection and recognition system

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图 3 SAR图像散射特征

Figure 3. Scattering features of SAR imagery

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图 4 飞机主要部件及散射机制

Figure 4. Scattering mechanism of aircraft each component

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图 5 SAR图像中飞机目标示例

Figure 5. Examples of aircraft in SAR imagery

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图 6 传统SAR图像中目标特征提取算法分类

Figure 6. Classification of traditional methods

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图 7 传统飞机目标检测识别方法

Figure 7. Traditional aircraft detection and recognition methods

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图 8 融合散射信息与深度学习的SAR图像中飞机目标检测算法框架

Figure 8. Framework of the proposed aircraft detection algorithm in SAR imagery

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图 9 机场检测算法效果

Figure 9. Effectiveness of the proposed airport detection algorithm in SAR imagery

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图 10 高分3号图像飞机检测结果

Figure 10. Results of aircraft detection in Gaofen-3 SAR images

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图 11 TerraSAR-X图像飞机检测结果

Figure 11. Results of aircraft detection in TerraSAR-X SAR images

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表 1 飞机各部件散射机理

Table 1. Scattering mechanism of each component

部件	散射机理	具体描述
机头	反射/多次散射	机头是由一系列结构组成的，驾驶舱与地面成二面角结构
机身	反射/多次散射	机身包含多种纵向和横向元件：大梁、桁条、隔框和蒙皮等；发动机与机翼成二面角结构
尾翼	多次散射/边缘绕射	尾翼分垂直尾翼和水平尾翼两部分，形成二面体或三面体结构
机翼	边缘绕射	机翼包括副翼、襟翼和缝翼等结构，包含丰富的边缘信息
动力装置	腔体散射	发动机有典型的空腔结构

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表 2 机场检测算法效果论证

Table 2. Demonstration of airport detection algorithm effectiveness

算法	正确检测目标数目(个)	漏检目标数目(个)	虚警目标数目(个)	虚警率(%)	精确率(%)
未进行机场检测	580	28	103	15.1	84.9
进行机场检测	580	28	50	7.9	92.1

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表 3 基于GF-3与TerraSAR-X卫星数据的散射信息增强效果对比

Table 3. Results of algorithm without/with scattering information enhancement based on GF-3 and TerraSAR-X data

算法	检测结果	GF-3	TerraSAR-X	合计
未进行散射信息增强特征金字塔算法	正检个数	429	133	562
	漏检个数	42	4	46
	虚警个数	53	14	67
	虚警率(%)	11.0	9.5	10.7
	召回率(%)	91.1	97.1	92.4
	精确率(%)	89.0	90.5	89.3
散射信息增强特征金字塔算法	正检个数	445	135	580
	漏检个数	26	2	28
	虚警个数	43	7	50
	虚警率(%)	8.8	4.9	7.9
	召回率(%)	94.5	98.5	95.4
	精确率(%)	91.2	95.1	92.1

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表 4 SAR图像飞机目标检测识别算法对比

Table 4. Comparison of different aircraft detection and recognition methods in SAR imagery

算法	具体分类		具有的优势	存在的问题
传统的方法	基于目标结构特征		算法的稳定性较好	需要先验信息，难以推广应用
	基于目标散射特征	目标几何特征	算法的速度较快	容易受背景杂波干扰
		灰度统计特征	算法的稳定性较好	建立统一的目标统计模型难度大
		目标纹理特征	算法精度较高	鲁棒的目标局部纹理特征提取难度大
基于深度学习的方法	直接应用深度学习		算法的精度较高，算法速度较快	训练样本需求量大，网络泛化能力差
基于深度学习的方法	结合目标散射特征		算法的精度较高，稳定性较好	训练样本需求量大，训练过程较为复杂

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