一种复杂环境下改进的SAR图像双边CFAR舰船检测算法

艾加秋 曹振翔 毛宇翔 汪章怀 王非凡 金兢

艾加秋, 曹振翔, 毛宇翔, 等. 一种复杂环境下改进的SAR图像双边CFAR舰船检测算法[J]. 雷达学报, 2021, 10(4): 499–515. doi: 10.12000/JR20127
引用本文: 艾加秋, 曹振翔, 毛宇翔, 等. 一种复杂环境下改进的SAR图像双边CFAR舰船检测算法[J]. 雷达学报, 2021, 10(4): 499–515. doi: 10.12000/JR20127
AI Jiaqiu, CAO Zhenxiang, MAO Yuxiang, et al. An improved bilateral CFAR ship detection algorithm for SAR image in complex environment[J]. Journal of Radars, 2021, 10(4): 499–515. doi: 10.12000/JR20127
Citation: AI Jiaqiu, CAO Zhenxiang, MAO Yuxiang, et al. An improved bilateral CFAR ship detection algorithm for SAR image in complex environment[J]. Journal of Radars, 2021, 10(4): 499–515. doi: 10.12000/JR20127

一种复杂环境下改进的SAR图像双边CFAR舰船检测算法

doi: 10.12000/JR20127
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

An Improved Bilateral CFAR Ship Detection Algorithm for SAR Image in Complex Environment

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)
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  • 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。

     

  • 图  1  弱目标在双边CFAR联合图像中的值与在原始图像中的强度值之间的对比

    Figure  1.  The comparison between the value of weak target in the joint image of bilateral CFAR and the intensity value in the original image

    图  2  本文所提IB-CFAR的检测流程图

    Figure  2.  The proposed IB-CFAR detection flow chart

    图  3  非均匀强度分级原理图,黄色区域代表第${L_N}$

    Figure  3.  Schematic diagram of non-uniform strength grading, which yellow area represents the level N

    图  4  各项性能指标在不同${L_N}$等级下的变化曲线

    Figure  4.  Variation curves of various performance indicators at different levels

    图  5  非均匀量化方法增强弱目标内部的相似度信息

    Figure  5.  Non-uniform quantization method enhances similarity information within weak targets

    图  6  在包含强相干斑噪声环境下双边CFAR对杂波的抑制效果

    Figure  6.  Clutter suppression effect of bilateral CFAR in the presence of strong speckle noise

    图  7  双边CFAR和本文所提IB-CFAR在高强度异质点分布密集的背景下处理得到的融合图像对比图

    Figure  7.  The comparison of the fusion images of bilateral CFAR and IB-CFAR under the background of dense distribution of high-intensity heterogeneous points

    图  8  在包含强相干斑噪声环境下所提出的IB-CFAR对杂波的抑制效果

    Figure  8.  Clutter suppression effect of IB-CFAR in the presence of strong speckle noise

    图  9  OR-CFAR和本文所提IB-CFAR在高强度异质点环境下对异质点进行杂波截断的性能评估

    Figure  9.  The high-intensity outliers elimination performance evaluation of OR-CFAR and the proposed IB-CFAR

    图  10  2017年2月25日,高分3号在UFS模式下获得的上海港附近海域的SAR图像

    Figure  10.  Gaofen-3 test image of the homogeneous sea area near Shanghai harbor acquired by UFS mode on February 25, 2017

    图  11  2009年7月31日,TerraSAR-X在SM模式下所获得的巴拿马运河地区的高分辨率、多视、HH极化SAR图像

    Figure  11.  High-resolution, multi-look, HH polarized SAR image of the Panama Canal region acquired by the X-band TerraSAR SM mode on July 31, 2009

    图  12  2018年9月1日,高分3号在FSI模式下获得的长江入海口非均匀海况下的SAR图像

    Figure  12.  Gaofen-3 test image of the heterogeneous open sea of Yangtze River Estuary acquired by the FSI mode on September 1, 2018

    图  13  复杂环境下的检测结果比较,其中包含密集分布的30个目标,并且图像中还存在重影和防波堤

    Figure  13.  Comparison of detection results in complex environment, which contains 30 targets with dense distribution, and there are ghost and anti wave in the image

    图  14  原强度域图像和融合域图像在相同坐标点下的对比图

    Figure  14.  Comparison of original intensity domain image and fusion domain image at the same coordinate point

    图  15  检测结果对比

    Figure  15.  Comparison of detection results

    图  16  复杂环境下的检测结果比较

    Figure  16.  Comparison of detection results in complex environment

    图  17  各类CFAR检测器的ROC曲线

    Figure  17.  ROC curves of various CFAR detectors

    表  1  实验中所使用到的SAR图像的详细信息

    Table  1.   Details of SAR images used in the experiment

    NameGaofen-3Terra SARGaofen-3
    Acquisition date2017-02-252009-07-312018-09-01
    Acquired regionThe sea area near Shanghai harbourPanama CanalSea of Yangtze River Estuary
    LocationE121.9, N31.0W79.55, N8.93E121.3, N31.5
    Imaging modeUltra Fine Stripmap (UFS)StripMap (SM)Fine Stripmap I (FSI)
    BandCXC
    PolarizationVHHHHH
    Resolution3 m3 m5 m
    Number of looks1501
    下载: 导出CSV

    表  2  各CFAR检测器的性能分析

    Table  2.   Performance analysis of CFAR detectors

    各CFAR检测器Fig. 13(a)Fig. 15(a)
    ${D_{\rm{r}}}$[%]FomFAR[%]PrecisionTime[s]${D_{\rm{r}}}$[%]FomFAR[%]PrecisionTime[s]
    CA-CFAR 9.350.0342 2.710.0854 6.25 3.540.035401.0000 1.62
    TP-CFAR92.900.834213.510.8747 12.0385.230.342538.230.3720 3.15
    LN-CFAR82.670.5813 3.840.6235 15.3273.420.2314 3.220.3321 5.36
    K-CFAR89.240.544211.230.5442180.2568.530.255215.210.256627.28
    双边CFAR52.650.200268.260.2301 17.6352.430.100258.250.1567 6.67
    IS-CFAR91.280.798632.850.7986 25.1238.460.334247.360.434210.23
    IB-CFAR97.850.9223 3.520.9243 27.5298.240.589413.280.607411.09
    下载: 导出CSV
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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
  • 刊出日期:  2021-08-28

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