基于多角度SAR图像阴影特征的虚假目标识别方法

doi:10.12305/j.issn.1001-506X.2026.10.14

系统工程与电子技术 ›› 2026, Vol. 48 ›› Issue (10): 3417-3426.doi: 10.12305/j.issn.1001-506X.2026.10.14

• 传感器与信号处理 • 上一篇

基于多角度SAR图像阴影特征的虚假目标识别方法

王洪刚¹(), 王莹²(), 刘艳芳³(), 胡振鑫¹(), 周嘉宁⁴()

1. 北京跟踪与通信技术研究所，北京 100094
2. 上海航天技术研究院，上海 201109
3. 北京化工大学信息科学与技术学院，北京 100029
4. 中国贸促会专利商标事务所，北京 100031

收稿日期:2025-09-18 接受日期:2026-02-25 出版日期:2026-10-25 发布日期:2026-09-30
通讯作者: 刘艳芳 E-mail:by2002162@buaa.edu.cn;wangying_az4837@sina.com;18613844887@163.com;liuyanfang0817@sina.com;zhoujn@ccpit-patent.com.cn
作者简介:王洪刚（1973—），男，副研究员，博士，主要研究方向为试验鉴定、虚假目标识别
王　莹（1981—），女，高级工程师，博士，主要研究方向为航天产品制造及相关技术
胡振鑫（1983—），男，副研究员，博士，主要研究方向为试验鉴定、虚假目标识别
周嘉宁（2000—），女，硕士，主要研究方向为多角度SAR图像虚假目标抑制

False target recognition based on shadow characteristics in multi-angle SAR images

Honggang Wang¹(), Ying Wang²(), Yanfang Liu³(), Zhenxin Hu¹(), Jianing Zhou⁴()

1. Beijing Institute of Tracking and Telecommunication Technology，Beijing 100094，China
2. Shanghai Academy of Spaceflight Technology，Shanghai 201109，China
3. College of Information Science and Technology，Beijing University of Chemical Technology，Beijing 100029，China
4. CCPIT Patent and Trademark Law Office，Beijing 100031，China

Received:2025-09-18 Accepted:2026-02-25 Online:2026-10-25 Published:2026-09-30
Contact: Yanfang Liu E-mail:by2002162@buaa.edu.cn;wangying_az4837@sina.com;18613844887@163.com;liuyanfang0817@sina.com;zhoujn@ccpit-patent.com.cn

摘要/Abstract

摘要：

主动式干扰在合成孔径雷达图像中生成的虚假目标逼真度高、识别难度大。在多角度图像序列中，真实目标及阴影随视角变化，虚假目标及阴影因生成模板固定而保持不变，该特征可为干扰检测提供有效依据。提出一种基于多角度合成孔径雷达图像阴影特征的虚假目标识别方法。首先，结合超像素分割设计双阈值恒虚警率检测器，结合局部动态阈值实现了目标和阴影的高精度检测。其次，基于目标及阴影检测结果，通过基于超像素约束的尺度不变特征转换匹配、单应性变换及基于多角度图像的虚假目标识别指标，完成目标及阴影区域的多角度合成孔径雷达图像变化检测与虚假目标识别。最后，多角度虚假目标仿真实验结果表明，真实目标在多角度图像中目标方向、目标与阴影方向的夹角以及目标散射面积均存在明显变化，所提方法可以有效识别虚假目标。

关键词: 多角度星载合成孔径雷达, 超像素, 增强尺度不变特征转换, 虚假目标识别

Abstract:

Active jamming generates false targets in synthetic aperture radar (SAR) images with high fidelity and low identifiability. In multi-angle spaceborne SAR images, the shadows of real targets vary with the angle, whereas the shadows of false targets remain constant due to their fixed template. This feature can provide a reliable basis for jamming detection. A false target identification method based on shadow characteristics in multi-angle SAR images is proposed. First, a double threshold-constant false alarm (DT-CFAR) detector integrated with superpixel segmentation is designed, achieving high-precision detection of targets and shadows through local dynamic thresholds. Second, based on the target and shadow detection results, multi-angle SAR image change detection and false target identification are accomplished by superpixel-constrained scale invariant feature transform (SIFT), homography transform, and multi-angle image-based false target recognition metrics. Finally, multi-angle false target simulation experiments demonstrate that there are significant changes in the target direction, the angle between the target and shadow directions, and the target scattering area of real targets in multi-angle images, and that the proposed method can effectively recognize false targets.

Key words: multi-angle spaceborne synthetic aperture radar, superpixel, enhanced scale invariant feature transform, false target recognition

中图分类号:

TN 957

王洪刚, 王莹, 刘艳芳, 胡振鑫, 周嘉宁. 基于多角度SAR图像阴影特征的虚假目标识别方法[J]. 系统工程与电子技术, 2026, 48(10): 3417-3426.

Honggang Wang, Ying Wang, Yanfang Liu, Zhenxin Hu, Jianing Zhou. False target recognition based on shadow characteristics in multi-angle SAR images[J]. Systems Engineering and Electronics, 2026, 48(10): 3417-3426.

图/表 17

图1

图2

图3

图4

图5

图6

表1

图7

图8

图9

图10

表2

图11

图12

图13

表3

图14

参考文献 32

1	Guo X Y, Tian T, Tan H Y, et al. A deceptive jamming technology against SAR based on optical-to-SAR template translation[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (5): 7350.
2	Yuan T, He F, Dong Z, et al. Suppress mainlobe deceptive jamming target under unambiguous range compensation based on FDA-MIMO radar[J]. IEEE Trans. on Aerospace and Electronic Systems, 2024, 60 (5): 7361. doi: 10.1109/taes.2024.3407729
3	Zhang Y, Ding D Z, He Z, et al. On the deceptive jamming technique against video synthetic aperture radar[J]. IEEE Trans. on Geoscience and Remote Sensing, 2025, 63, 3551797.
4	Zhang H Y, Quan S N, Xing S Q, et al. Shadow-based false target identification for SAR images[J]. Remote Sensing, 2023, 15 (21): 5259. doi: 10.3390/rs15215259
5	李兆弘, 徐华平, 段书航, 等. 基于干涉相位的 SAR 有源欺骗干扰检测的性能分析[J]. 雷达学报, 2024, 13 (6): 1327.
6	Lang W H, Tang Y L, Mei S Q, et al. An anti-2D deceptive jamming method for multi-baseline interferometric SAR based on co-localization jammer[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 5388. doi: 10.1109/JSTARS.2022.3185254.
7	Xiao Z P, He F, Sun Z Y, et al. Mitigation of suppressive interference in AMPC SAR based on digital beamforming[J]. Remote Sensing, 2024, 16 (15): 2812. doi: 10.3390/rs16152812
8	Chen S L, Liu L, Wang X B, et al. Research on SAR active anti-jamming imaging based on joint random agility of inter-pulse multi-parameters in the presence of active deception[J]. Remote Sensing, 2024, 16 (17): 3303. doi: 10.3390/rs16173303
9	Zhou K, Hou Z Y, Wu Z G, et al. SAR frequency-shift jamming suppression method by shaping the cross-ambiguity function[J]. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (3): 7387. doi: 10.1109/TAES.2025.3539643
10	Feng Q Q, Xu H P, Wu Z F, et al. Deceptive jamming suppression for SAR based on time-varying initial phase[C]//IEEE International Geoscience and Remote Sensing Symposium, 2016: 4996.
11	冯祥芝, 许小剑. 随机线性调频斜率SAR抗欺骗干扰方法研究[J]. 系统工程与电子技术, 2009, 31 (1): 69. doi: 10.3321/j.issn:1001-506X.2009.01.018
12	赵宗锋. 基于信号波形设计的SAR抗干扰技术研究[D]. 长沙: 国防科学技术大学.
13	李江源, 王建国, 杨建宇. 基于数字储频式的对SAR类杂波干扰[J]. 电子科技大学学报, 2005, 34 (6): 739.
14	Li Y T, Jia X, Chen Y G, et al. Frequency agility MIMO-SAR imaging and anti-deception jamming performance[C]//31st URSI General Assembly and Scientific Symposium, 2014.
15	贾宝京. SAR欺骗干扰抑制和目标高分辨成像方法的研究[D]. 秦皇岛: 燕山大学, 2015.
16	Li S C, Xu Y, Chen H, et al. An algorithm of radar deception jamming suppression based on blind signal separation[C]//International Conference on Computational Problem-Solving, 2011: 167.
17	Zhao B, Huang L, He C L, et al. SAR deception jamming identification via differential feature enhancement[C]//CIE International Conference on Radar, 2016.
18	Zhao B, Huang L, Zhang J H. Single channel SAR deception jamming suppression via dynamic aperture processing[J]. IEEE Sensors Journal, 2017, 17 (13): 4225. doi: 10.1109/JSEN.2017.2695001
19	Zhao B, Huang L, Sun W Z. Target reconstruction in deceptively jammed SAR via ADMM[J]. IEEE Sensors Journal, 2019, 19 (11): 4331. doi: 10.1109/JSEN.2018.2873683
20	Lee J S, Pottier E. Polarimetric radar imaging: from basics to applications[M]. Boca Raton: CRC Press, 2017.
21	Lee J S, Hoppel K, Mango S A. Unsupervised estimation of speckle noise in radar images[J]. International Journal of Imaging Systems and Technology, 1992, 4 (4): 298. doi: 10.1002/ima.1850040409
22	Frery A C, Muller H J, Yanasse C C F, et al. A model for extremely heterogeneous clutter[J]. IEEE Trans. on Geoscience and Remote Sensing, 1997, 35 (3): 648. doi: 10.1109/36.581981
23	Luo X J, Zhang X Y, Bao J W, et al. Statistical modeling of shadows in SAR imagery[J]. Mathematics, 2023, 11 (21): 4437. doi: 10.3390/math11214437
24	Shi H X, Xu Z Q, Li G Z, et al. An SAR deceptive jamming suppression method based on PRI variation design and multichannel principle[J]. IEEE Trans. on Geoscience and Remote Sensing, 2025, 63, 5214217.
25	Liu Q H, Wang C J, Tian D Z, et al. A mainlobe jamming suppression method for an FDA-MIMO radar system with an ultralarge sparse aperture[J]. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (4): 11350. doi: 10.1109/taes.2025.3566087
26	Zhang L, Lu S T, Hu C B, et al. Superpixel generation for SAR imagery based on fast DBSCAN clustering with edge penalty[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2022, 15, 804. doi: 10.1109/JSTARS.2021.3131187
27	Van Den Bergh M, Boix X, Roig G, et al. SEEDS: superpixels extracted via energy-driven sampling[J]. International Journal of Computer Vision, 2015, 111 (3): 298. doi: 10.1007/s11263-014-0744-2
28	Achanta R, Shaji A, Smith K, et al. SLIC superpixels compared to state-of-the-art superpixel methods[J]. IEEE Trans. on Pattern Analysis and Machine Intelligence, 2012, 34 (11): 2274. doi: 10.1109/TPAMI.2012.120
29	Cui Z Y, Hou Z S, Yang H Z, et al. A CFAR target-detection method based on superpixel statistical modeling[J]. IEEE Geoscience and Remote Sensing Letters, 2021, 18 (9): 1605. doi: 10.1109/LGRS.2020.3006033
30	Lowe D G. Distinctive image features from scale-invariant keypoints[J]. International Journal of Computer Vision, 2004, 60 (2): 91. doi: 10.1023/B:VISI.0000029664.99615.94
31	Bu Y, Yu X X, Yang J, et al. Deceptive jamming suppression via segmented modified Radon-Fourier transform for moving target detection[J]. IEEE Trans. on Aerospace and Electronic Systems, 2025, 61 (2): 3106. doi: 10.1109/TAES.2024.3483788
32	Air Force Research Laboratory. The Air Force Moving and Stationary Target Recognition Database[EB/OL]. [2025-09-18]. https: //www.sdms.afrl.af.mil/index.

仿真参数	参数值
轨道高度/km	600
工作波长/m	0.03125
天线中心视角/(°)	32.0338
信号采样率/Hz	12×10⁷
脉冲重复频率/Hz	3000

指标	不同角度图像
指标	角度1	角度2	角度3	角度4	角度5
$ \theta $/(°)	146.5	116.5	68.0	67.0	45.0
$ \Delta \theta $/(°)	−37.5	17.5	36.0	46.5	33.0
$ S $	304	341	306	441	268

目标图像	角度1			角度2
目标图像	$ \theta $/(°)	$ \Delta \theta $/(°)	$ S $	$ \theta $/(°)	$ \Delta \theta $/(°)	$ S $
（a）	146.5	−76.5	242	107.0	34.0	302
（b）	31.0	14.5	202	45.5	−48.0	283
（c）	23.0	8.0	212	70.5	70.0	269
（d）	59.0	62.0	283	54.5	66.5	277

[1]	马倩, 邹焕新, 李美霖, 成飞, 贺诗甜. 基于SNIC的双时相SAR图像超像素协同分割算法[J]. 系统工程与电子技术, 2021, 43(5): 1198-1209.
[2]	代晓康, 殷君君, 杨健. 基于Wishart距离和超像素的极化SAR图像车辆检测[J]. 系统工程与电子技术, 2021, 43(10): 2766-2774.
[3]	邵宁远, 邹焕新, 陈诚, 李美霖, 秦先祥. 面向变化检测的SAR图像超像素协同分割算法[J]. 系统工程与电子技术, 2019, 41(7): 1496-1503.

基于多角度SAR图像阴影特征的虚假目标识别方法

False target recognition based on shadow characteristics in multi-angle SAR images

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 17

参考文献 32

相关文章 3

编辑推荐

Metrics

本文评价