基于极化SAR梯度和复Wishart分类器的舰船检测

殷君君; 罗嘉豪; 李响; 代晓康; 杨健

doi:10.12000/JR23198

基于极化SAR梯度和复Wishart分类器的舰船检测

doi: 10.12000/JR23198

1.
北京科技大学计算机与通信工程学院北京 100083
2.
北京无线电测量研究所北京 100854
3.
清华大学电子工程系北京 100084

基金项目: 国家自然科学基金(62222102, 62171023)，中央高校基本科研业务费(FRF-TP-22-005C1)

详细信息

作者简介:
殷君君，博士，教授，博士生导师，主要研究方向为极化雷达应用基础理论、极化合成孔径雷达图像理解、海洋遥感及生态环境变化监测等

罗嘉豪，硕士生，主要研究方向为极化合成孔径雷达图像理解与应用、海面舰船检测、雷达目标检测和深度学习

李　响，博士，高级工程师，主要研究方向为雷达目标检测和识别

代晓康，硕士，主要研究方向为极化合成孔径雷达图像理解、海面舰船检测、地面车辆检测

杨　健，博士，教授，博士生导师，主要研究方向为极化雷达理论及其应用等

通讯作者:
殷君君 junjun_yin@ustb.edu.cn

责任主编：刘涛 Corresponding Editor: LIU Tao
中图分类号: TN958
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出版历程
- 收稿日期: 2023-10-10
- 修回日期: 2023-11-15
- 网络出版日期: 2023-12-07
- 刊出日期: 2024-04-28

Ship Detection Based on Polarimetric SAR Gradient and Complex Wishart Classifier

1.
School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China
2.
Beijing Institute of Radio Measurement, Beijing 100854, China
3.
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China

Funds: The National Natural Science Foundation of China (62222102, 62171023), Fundamental Research Funds for the Central Universities (FRF-TP-22-005C1)

More Information

Corresponding author: YIN Junjun, junjun_yin@ustb.edu.cn

摘要

摘要: 舰船检测是极化SAR系统的重要应用之一。现有的舰船检测方法容易受到旁瓣泄露的干扰，使得舰船目标的形态难以提取，导致检测结果不符合真实情况。此外，在舰船过于密集、尺度不一致的情况下，相邻舰船由于旁瓣的影响有时会被认为是单个目标，从而造成漏检。针对这些问题，该文提出一种基于极化SAR梯度和复Wishart分类器的舰船检测方法。首先，将似然比检验(LRT)梯度引入对数比值梯度框架，使其适用于极化SAR数据；基于LRT梯度图进行恒虚警(CFAR)检测，提取舰船的边缘信息，消除伪影的同时抑制强旁瓣对舰船精细轮廓提取的影响。其次，利用复Wishart迭代分类器对舰船强散射部分进行检测，可排除大部分的杂波干扰且保持舰船形态细节。最后，将二者信息融合，从而可以保持舰船形态细节的同时克服旁瓣和伪信号的虚警。该文在3幅来自ALOS-2卫星的极化SAR图像上进行了对比实验，实验表明与其他方法相比，该文所提算法具有更少的虚警和漏检，且能够有效克服旁瓣泄露，保持舰船形态细节。
- 舰船检测 /
- 极化合成孔径雷达 /
- 比值梯度 /
- 似然比检验 /
- 复Wishart分类器
Abstract: Ship detection is one of the most important applications of polarimetric Synthetic Aperture Radar (SAR) systems. Current ship detection methods are susceptible to side flap interference, making it difficult to extract the target shape correctly. In addition, when ships are exceedingly dense and have different scales, adjacent ships may be considered as a single target because of the influence of strong sidelobes, causing missed detections. To address the issues of sidelobe interference and multi-scale dense ship detection, a ship detection method based on the polarimetric SAR gradient and the complex Wishart classifier is proposed. First, the Likelihood Ratio Test (LRT) gradient is introduced into the log-ratio gradient framework to apply it to the polarimetric SAR data. Then, a Constant False Alarm Rate (CFAR) detector is applied to the gradient image to map the ship boundaries accurately. Second, the complex Wishart iterative classifier is used to detect the strong scattering part of the ship, which can eliminate most clutter interference and maintain the ship’s shape details. Finally, the LRT detection and complex Wishart classifier detection results are fused. Thus, not only the strong sidelobe interference can be greatly suppressed, but the dense targets with different scales are also distinguished and accurately located. This study performs comparative experiments on three polarimetric SAR images from the ALOS-2 satellite. Experimental results show that compared with the existing methods, the proposed algorithm has fewer false alarms and missed detections and can effectively overcome the problems of sidelobe interference while maintaining the shape details.
- Ship detection /
- Polarimetric Synthetic Aperture Radar (PolSAR) /
- Ratio gradient /
- Likelihood Ratio Test (LRT) /
- Complex Wishart classifier

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图 1 ROA示意图

Figure 1. Scheme of the ROA method

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图 2 所提算法流程图

Figure 2. Flow chart of the proposed method

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图 3 实验场景1

Figure 3. Experimental scene 1

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图 4 实验场景2 (第1行)和实验场景3 (第2行)

Figure 4. Experimental scene 2 (first line) and experimental scene 3 (second line)

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图 5 梯度结果

Figure 5. Calculation results for different gradients

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图 6 3种LRT梯度计算结果

Figure 6. Comparison of three LRT gradient calculation results

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图 7 LRT梯度的检测结果

Figure 7. The detection result of LRT gradient

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图 8 PWF的检测结果

Figure 8. The detection result of PWF

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图 9 复Wishart分类器的分类结果

Figure 9. The classification result of complex Wishart classifier

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图 10 LRT结果与复Wishart结果相加的伪彩色图

Figure 10. Pseudo-color display of the sum of LRT detection result and complex Wishart result

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图 11 融合检测结果

Figure 11. The fused result

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图 12 伪影和旁瓣示意图

Figure 12. Image of pseudo-signal and sidelobes

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图 13 基于LRT的CFAR检测结果

Figure 13. CFAR detection results based on LRT

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图 14 复杂海面融合检测结果

Figure 14. The fused result for complex sea surface scenario

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图 15 多尺度舰船检测结果

Figure 15. Multi-scale ship detection results

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图 16 实验场景2舰船检测结果

Figure 16. Ship detection results for experimental scenario 2

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图 17 实验场景3舰船检测结果

Figure 17. Ship detection results for experimental scenario 3

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图 18 实验场景1中的舰船检测结果

Figure 18. Ship detection results for experimental scenario 1

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表 3 实验场景1中对比方法的指标评价结果

Table 3. Results evaluation for experimental scenario 1

方法	召回率	准确率	F1	时间(s)
OS-CFAR	0.4174	0.5386	0.4703	1.548
Span	0.4430	0.8715	0.5874	0.142
PMS	0.4517	0.8631	0.5931	0.152
2P-CFAR	0.4660	0.8494	0.6018	17.339
Parzen	0.4694	0.8477	0.6042	0.145
T₁₁	0.4737	0.9302	0.6277	0.138
CA-CFAR	0.6793	0.8566	0.7577	4.325
λ₃	0.7116	0.8981	0.7940	32.468
OPCE	0.7167	0.8917	0.7947	19.845
GOPCE	0.7167	0.8917	0.7947	29.904
本文方法	0.7471	0.9177	0.8237	92.673

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表 2 实验场景3中对比方法的指标评价结果

Table 2. Results evaluation for experimental scenario 3

方法	虚警	漏检	召回率	准确率	F1	时间(s)
OS-CFAR	20	3	0.969	0.823	0.890	5.765
2P-CFAR	14	0	1.000	0.876	0.934	61.898
Span	12	1	0.990	0.890	0.937	0.283
CA-CFAR	11	1	0.990	0.898	0.942	15.530
Parzen	11	1	0.990	0.898	0.942	0.261
T₁₁	8	2	0.979	0.922	0.950	0.248
PSH	10	0	1.000	0.908	0.952	90.842
PMS	6	3	0.969	0.939	0.954	0.314
λ₃	3	1	0.990	0.970	0.980	91.453
OPCE	1	1	0.990	0.990	0.990	67.250
GOPCE	1	1	0.990	0.990	0.990	88.925
本文方法	0	1	0.990	1.000	0.995	276.324

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表 1 实验场景2中对比方法的指标评价结果

Table 1. Results evaluation for experimental scenario 2

方法	虚警	漏检	召回率	准确率	F1	时间(s)
PSH	10	4	0.895	0.773	0.829	92.245
2P-CFAR	2	5	0.865	0.941	0.901	61.870
CA-CFAR	2	5	0.865	0.941	0.901	15.420
Span	4	3	0.923	0.900	0.911	0.283
Parzen	4	2	0.950	0.905	0.927	0.254
T₁₁	5	1	0.976	0.889	0.930	0.252
OS-CFAR	2	3	0.923	0.947	0.935	5.981
PMS	3	2	0.950	0.927	0.938	0.322
OPCE	1	2	0.950	0.974	0.962	69.524
λ₃	2	1	0.976	0.952	0.964	91.350
GOPCE	0	2	0.950	1.000	0.974	89.484
本文方法	0	0	1.000	1.000	1.000	298.198

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