曹振翔1, 2,,
毛宇翔1, 2,,
汪章怀1,,
王非凡1, 2,,
金兢1, 2,
1.合肥工业大学计算机与信息学院 合肥 230009
2.合肥工业大学智能互联系统安徽省实验室 合肥 230009
基金项目:国家自然科学基金(62071164, 61701157)，中国博士后科学基金(2020T130165, 2018M640581)，中央高校基本科研业务费专项(JZ2020HGTB0012)，安徽省自然科学基金(1808085QF206)
详细信息
作者简介:艾加秋(1985–)，男，江西永丰人。2012年6月获中国科学院大学信息与通信工程专业博士学位、现担任合肥工业大学电子信息学院副教授、硕士生导师，主要研究方向为人工智能、雷达图像处理、雷达系统设计、视频图像处理
曹振翔(1996–)，男，安徽合肥人，合肥工业大学硕士生，主要研究方向为雷达图像处理、海杂波信号处理。
毛宇翔(1997–)，男，安徽合肥人，合肥工业大学硕士生，主要研究方向为人工智能、雷达图像处理。
汪章怀(2000–)，男，安徽六安人，合肥工业大学本科生，主要研究方向为信号处理。
王非凡(1998–)，男，安徽阜阳人，合肥工业大学硕士生，主要研究方向为SAR图像分类。
金兢：金 兢(1986–)，男，浙江衢州人，博士，讲师，主要研究方向为计算机视觉、机器视觉测量、信号处理与分析、视频图像处理与分析、SLAM。
通讯作者:艾加秋 aijiaqiu1985@hfut.edu.cn
责任主编：计科峰 Corresponding Editor: JI Kefeng中图分类号:TN959.72
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出版历程
收稿日期:2020-09-16
修回日期:2020-11-19
网络出版日期:2020-12-14
An Improved Bilateral CFAR Ship Detection Algorithm for SAR Image in Complex Environment
AI Jiaqiu1, 2,,,CAO Zhenxiang1, 2,,
MAO Yuxiang1, 2,,
WANG Zhanghuai1,,
WANG Feifan1, 2,,
JIN Jing1, 2,
1. School of Computer Science and Information Engineering, Hefei University of Technology, Hefei 230009, China
2. Intelligent Interconnected Systems Laboratory of Anhui Province (Hefei University of Technology), Hefei 230009, China
Funds:The National Natural Science Foundation of China (62071164, 61701157), China Post Doctoral Science Foundation (2020T130165, 2018M640581), The Special Gund for Basic Scientific Research of Central University (JZ2020HGTB0012), Anhui Provincial Natural Science Foundation (1808085QF206)
More Information
Corresponding author:AI Jiaqiu, aijiaqiu1985@hfut.edu.cn
摘要
摘要:双边恒虚警率(BCFAR)检测算法通过高斯核密度估计器计算出合成孔径雷达(SAR)图像的空间信息，并将它与图像的强度信息相结合得到联合图像以进行目标检测。相较于只使用强度信息来进行目标检测的经典CFAR检测算法，双边CFAR有着更好的检测性能和鲁棒性。然而，在复杂环境下出现连片的高强度异质点时(例如防波堤、方位模糊和幻影等)，核密度估计器计算出的空间信息会出现较多误差，这会导致检测结果中出现大量虚警。此外，当遇到相邻像素点间相似度较低的弱目标时，双边CFAR会发生漏检。为了有效改善这些问题，该文设计一种复杂环境下改进的SAR图像双边CFAR舰船检测算法(IB-CFAR)。该文所提IB-CFAR主要分为3个阶段来实现，分别为基于非均匀量化法的强度层级划分、强度-空间域信息融合、杂波截断后的参数估计。基于非均匀量化法的强度层级划分可以提升弱目标的相似度和对比度信息，从而提升舰船检测率。强度-空间域信息融合在于将空间相似度、距离向和强度等信息进行融合，在进一步提升检测率的同时对舰船的结构信息进行精细化描述。杂波截断后的参数估计可以去除背景窗口中连片的高强度异质点，最大限度地保留真实海杂波样本，使参数估计更加精确。最后，根据估计出的参数建立精确的海杂波统计模型以进行CFAR检测。该文使用高分3号和TerraSAR-X数据来验证该算法的有效性和鲁棒性。实验结果表明，所提出的算法在包含较多密集分布的弱目标环境下表现良好，在此类环境下能获得97.85%的检测率和3.52%的虚警率，相比于现有的检测算法，检测率提升了5%，并且虚警率降低了10%，但在弱目标个数较少且背景十分复杂的环境下，则会出现少量虚警。
关键词:SAR图像目标检测/
改进的双边CFAR/
复杂环境/
弱目标/
非均匀量化/
强度-空间融合域
Abstract:The Bilateral Constant False Alarm Rate (BCFAR) detection algorithm calculates the spatial information of Synthetic Aperture Radar (SAR) image by the Gaussian kernel density estimator, and combines it with the intensity information of image to obtain the joint image for target detection. Compared with the classical CFAR detection algorithm which uses only intensity information for target detection, bilateral CFAR has better detection performance and robustness. However, with continuous high-intensity heterogeneous points (such as breakwater, azimuth ambiguity and phantom) in a complex environment, spatial information calculated by kernel density estimator will have more errors, which will lead to many false alarms in detection results. In addition, when it comes to a weak target with less similarity between adjacent pixels, it will miss detection. To effectively improve these problems, this paper designs an Improved Bilateral CFAR (IB-CFAR) algorithm in complex environment. The IB-CFAR proposed in this paper is mainly divided into three stages: intensity level division based on the nonuniform quantization method, intensity spatial domain information fusion and parameter estimation after clutter truncation. The intensity level division based on the nonuniform quantization method can improve the similarity and contrast information of weak targets, leading to improved ship detection rate. The information fusion of strength spatial domain is to fuse the spatial similarity, distance direction and strength information, which can further improve the detection rate and describe the ship structure information. Parameter estimation after clutter truncation can remove continuous high-intensity heterogeneous points in the background window and retain the real sea clutter samples to the maximum extent, which makes parameter estimation more accurate. Finally, according to the estimated parameters, an accurate sea clutter statistical model is established for CFAR detection. In this paper, the effectiveness and robustness of the proposed algorithm are verified by using GaoFen-3 and TerraSAR-X data.The experimental results show that the proposed algorithm performs well in the environment with more dense distribution of weak targets, and can obtain 97.85% detection rate and 3.52% false alarm rate in such environment. Compared with the existing detection algorithms, the detection rate increased by 5% and the false alarm rate reduced by 10%. However, when the number of weak targets is small and the background is very complex, few false alarms will appear.
Key words:Synthetic Aperture Radar (SAR) image target detection/
Improved Bilateral CFAR/
Complex environment/
Weak target/
Nonuniform quantization/
Intensity space fusion domain
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