一种改进的高分辨率SAR图像超像素CFAR舰船检测算法

张帆 陆圣涛 项德良 袁新哲

张帆, 陆圣涛, 项德良, 等. 一种改进的高分辨率SAR图像超像素CFAR舰船检测算法[J]. 雷达学报, 待出版. doi: 10.12000/JR22067
引用本文: 张帆, 陆圣涛, 项德良, 等. 一种改进的高分辨率SAR图像超像素CFAR舰船检测算法[J]. 雷达学报, 待出版. doi: 10.12000/JR22067
ZHANG Fan, LU Shengtao, XIANG Deliang, et al. An improved superpixel-based CFAR method for high-resolution SAR image ship target detection[J]. Journal of Radars, in press. doi: 10.12000/JR22067
Citation: ZHANG Fan, LU Shengtao, XIANG Deliang, et al. An improved superpixel-based CFAR method for high-resolution SAR image ship target detection[J]. Journal of Radars, in press. doi: 10.12000/JR22067

一种改进的高分辨率SAR图像超像素CFAR舰船检测算法

doi: 10.12000/JR22067
基金项目: 国家自然科学基金(41976174)
详细信息
    作者简介:

    张 帆,教授,博士生导师,主要研究方向为SAR信号处理、SAR图像解译、高性能计算等

    陆圣涛,硕士生,主要研究方向为人工智能、雷达图像处理

    项德良,教授,主要研究方向为SAR/PolSAR信息处理、探地雷达等

    袁新哲,副研究员,主要研究方向为SAR海洋遥感

    通讯作者:

    项德良 xiangdeliang@gmail.com

  • 责任主编:张晓玲 Corresponding Editor: ZHANG Xiaoling
  • 中图分类号: TN959.72

An Improved Superpixel-based CFAR Method for High-resolution SAR Image Ship Target Detection

Funds: The National Natural Science Foundation of China (41976174)
More Information
  • 摘要: 合成孔径雷达(SAR)图像舰船目标检测一直受到学者广泛关注,恒虚警率(CFAR)检测算法作为雷达图像经典目标检测算法被广泛应用于SAR图像舰船目标检测中。然而经典CFAR检测性能容易受到相干斑噪声影响,基于滑窗的检测结果对滑窗的尺寸选择非常敏感,难以保证杂波背景中不存在目标像素,并且计算效率较低。针对上述问题,该文提出了一种新的基于超像素无窗快速CFAR的SAR图像舰船目标检测算法。首先,利用基于密度的快速噪声空间聚类(DBSCAN)超像素生成方法生成SAR图像的超像素。在SAR数据服从混合瑞利分布的假设下,定义了超像素相异度。然后利用超像素精确估计每个像素的杂波参数,即使在多目标情况下,也可以克服传统CFAR滑动窗口的缺点。此外,基于SAR图像变异系数,提出了一种基于变异系数的局部超像素对比度来优化CFAR检测,以此消除大量杂波虚警,如陆地区域人造目标。对5幅SAR图像的实验结果表明,与其他方法相比,该文方法对不同场景SAR图像海面舰船目标检测都十分稳健。

     

  • 图  1  传统CFAR检测算法通用流程

    Figure  1.  General flow of the traditional CFAR detection algorithm

    图  2  传统CFAR检测器滑动窗口示意图

    Figure  2.  Schematic diagram of the sliding window in the traditional CFAR detector

    图  3  本文提出的基于超像素无窗快速CFAR的目标检测算法流程

    Figure  3.  Flow chart the proposed superpixel non-window fast CFAR strategy

    图  4  杂波超像素选取策略

    Figure  4.  Strategies for clutter superpixels determination

    图  5  超像素相异度有效性分析示意图

    Figure  5.  Schematic diagram of superpixel dissimilarity effectiveness analysis

    图  6  实验采用的不同波段不同分辨率的SAR图像

    Figure  6.  The SAR images with different bands and different resolutions in the experiment

    图  7  5幅SAR图像的真值图

    Figure  7.  The ground truth map of the SAR images

    图  8  TerraSAR X波段SAR图像1检测结果图

    Figure  8.  The results of TerraSAR X band SAR image 1 with the proposed method

    图  12  Sentinel-1A C波段SAR图像检测结果图

    Figure  12.  The results of Sentinel-1A C band SAR image with the proposed method

    图  9  TerraSAR X波段SAR图像2检测结果图

    Figure  9.  The results of TerraSAR X band SAR image 2 with the proposed method

    图  10  GF-3 C波段SAR图像1检测结果图

    Figure  10.  The results of GF-3 C band SAR image 1 with the proposed method

    图  11  GF-3 C波段SAR图像2检测结果图

    Figure  11.  The results of GF-3 C band SAR image 2 with the proposed method

    图  13  3种对比方法对TerraSAR X波段SAR图像1的检测结果

    Figure  13.  The detection results of TerraSAR X band SAR image 1 with three compared methods

    图  14  3种对比方法对TerraSAR X波段SAR图像2的检测结果

    Figure  14.  The detection results of TerraSAR X band SAR image 2 with three compared methods

    图  15  3种对比方法对GF-3 C波段SAR图像1的检测结果

    Figure  15.  The detection results of GF-3 C band SAR image 1 with three compared methods

    图  16  3种对比方法对GF-3 C波段SAR图像2的检测结果

    Figure  16.  The detection results of GF-3 C band SAR image 2 with three compared methods

    图  17  3种对比方法对Sentinel-1A C波段SAR图像的检测结果

    Figure  17.  The detection results of Sentinel-1A C band SAR image with three compared methods

    图  18  不同方法对TerraSAR X波段SAR图像1舰船检测的ROC曲线

    Figure  18.  The ROC curve of ship detection in TerraSAR X band SAR image 1 by different methods

    图  19  概率密度拟合性能测试结果图

    Figure  19.  Test results of probability density fitting performance

    表  1  杂波超像素选取算法

    Table  1.   The algorithm of clutter superpixels selection

     输入:SAR图像全体超像素集Rall,相异性阈值Thclu, TlcluSclutter中超像素数量最大值
     输出:每个潜在目标超像素的Sclutter
     算法步骤:
     (1) 根据每个超像素的平均强度值采用K-means聚类算法将Rall分成两个子集RtargetRbackground
     (2) for Rtarget中每一个潜在目标超像素SPi,do
     (3)   for SPi的每一个邻接超像素${\rm{SP}}_i^k $, ${\rm{SP}}_i^k $∈Rbackground, do
     (4)      计算超像素相异度 Ω(SPi, ${\rm{SP}}_i^k $)
     (5)    相异度值大于Thclu的超像素${\rm{SP}}_i^k $被添加进杂波超像素集Sclutter
     (6)    end
     (7)    for Sclutter杂波超像素集中超像素$S_{{\text{clutter}}}^i$的每一个邻接超像素,do
     (8)      计算$S_{{\text{clutter}}}^i$与每一个邻接超像素的相异度
     (9)    相异度值小于Tlclu的邻接超像素被添加进杂波超像素集Sclutter
     (10)   end
     (11)   重复执行步骤(7)—步骤(10)直到满足Sclutter中超像素数量达到预设的最大值
     (12) end
    下载: 导出CSV

    表  2  4种目标检测算法性能比较

    Table  2.   Quantitative comparisons of four target detection algorithms

    SAR图像双参数CFARSP-CFARSP-CFAR-TG本文方法
    FPR(%)TPR(%)FPR(%)TPR(%)FPR(%)TPR(%)FPR(%)TPR(%)
    TerraSAR X波段SAR图像13.1983.412.9523.640.5482.850.3187.93
    TerraSAR X波段SAR图像22.2677.232.5969.620.9875.410.2785.57
    GF-3 C波段SAR图像14.2399.503.7699.210.8994.950.0296.19
    GF-3 C波段SAR图像24.6199.440.2696.250.7995.950.0298.49
    Sentinel-1A C波段SAR图像2.7798.640.8585.670.6192.480.1997.36
    下载: 导出CSV

    表  3  本文方法对5幅SAR图像阴影超像素去除前后的检测性能比较

    Table  3.   Quantitative measures of the proposed method for the five SAR images with and without shadow superpixels removal

    SAR图像进行阴影超
    像素去除
    不进行阴影超
    像素去除
    FPR
    (%)
    TPR
    (%)
    FPR
    (%)
    TPR
    (%)
    TerraSAR X波段SAR图像10.3187.930.3286.98
    TerraSAR X波段SAR图像20.2785.570.2785.11
    GF-3 C波段SAR图像10.0296.190.0395.62
    GF-3 C波段SAR图像20.0298.490.0298.25
    Sentinel-1A C波段SAR图像0.1997.360.1997.36
    下载: 导出CSV

    表  4  本文方法在不同超像素数量情况下对TerraSARX波段SAR图像1检测性能比较

    Table  4.   Quantitative measures of the proposed method for TerraSAR X band SAR image 1 with different superpixel numbers

    超像素数量FPR(%)TPR(%)
    30000.3384.36
    40000.3686.81
    50000.3187.93
    60000.3787.88
    下载: 导出CSV

    表  5  4种目标检测算法时间比较

    Table  5.   Time costs of four ship detection algorithms

    SAR图像双参数CFAR(s)SP-CFAR(s)SP-CFAR-TG(s)本文方法(s)
    TerraSAR X波段SAR图像1113.4141.4218.7710.86
    TerraSAR X波段SAR图像21379.21498.5686.1331.75
    GF-3 C波段SAR图像11428.17461.7377.3129.82
    GF-3 C波段SAR图像2351.1298.7343.3622.93
    Sentinel-1A C波段SAR图像158.3556.1930.5114.31
    下载: 导出CSV

    表  6  本文方法在不同超像素数量情况下对TerraSAR X波段SAR图像1检测时间比较

    Table  6.   Time costs of the proposed method for TerraSAR X band SAR image 1 with different superpixel numbers

    超像素数量时间(s)
    30008.63
    40009.15
    500010.86
    600014.38
    下载: 导出CSV
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  • 收稿日期:  2022-04-09
  • 修回日期:  2022-05-16
  • 网络出版日期:  2022-05-29

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