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Statistical Modeling Methods of Single-channel Complex-valued SAR Images for Ship Detection
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摘要: 合成孔径雷达(SAR)成像模式丰富、覆盖范围广、分辨率高，可以长期、动态、宏观地对海洋进行监测。在完全发展的相干斑假设条件下，传统单通道SAR图像舰船目标检测方法主要研究幅度信息。然而，其部分假设条件在高分辨率情形下并非严格成立，因此无法有效利用单通道SAR图像的相位或复值信息。该文面向舰船目标检测应用，将单通道复值SAR图像统计建模方法划分为幅度、相位和复值统计建模3个部分，首先简要综述了单通道SAR图像幅度统计建模方法，然后详细阐述了单通道SAR图像相位和复值统计建模方法，并重点介绍了其建模过程和参数估计方法。在此基础上，该文给出了作者研究小组在基于复值统计信息的单通道SAR图像舰船目标检测方面的部分最新研究结果，并分析展望了下一步研究方向。Abstract: Synthetic Aperture Radar (SAR), which features rich imaging modes, wide coverage, and high resolution, is an effective technique for long-term, dynamic, and large-scale monitoring of the ocean. Under the assumption of fully developed speckle, traditional ship detection methods in single-channel SAR images focus mainly on amplitude information. Since conventional assumptions are not strictly true in high-resolution situations, this prevents the full investigation of phase or complex-valued information in single-channel SAR images. In this paper, with a focus on ship detection applications, we categories the methods used in the statistical modeling of single-channel complex-valued SAR images as amplitude-, phase-, or complex-valued-based. After providing a brief overview of amplitude statistical modeling methods, we focus on phase and complex-valued statistical modeling methods of single-channel SAR images, describing their modeling processes and parameter estimation methods. We then present the results of our recent ship detection research based on complex-valued statistical information in single-channel SAR images and make suggestions regarding future research.
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图 1 TerraSAR-X舰船目标和海杂波幅度图像示例
Figure 1. TerraSAR-X ship target and sea clutter amplitude images
图 2 TerraSAR-X舰船目标和海杂波相位图像
Figure 2. TerraSAR-X ship target and sea clutter phase images
图 3 TerraSAR-X舰船目标和海杂波相位直方图(以玫瑰图形式展示，红线表示平均方向)
Figure 3. TerraSAR-X ship target and sea clutter phase histograms (Presented in rose charts. Red line indicates mean direction)
图 4 不同参数下von Mises分布示例
Figure 4. PDFs for von Mises distribution at different parameters
图 5 TerraSAR-X舰船目标和海杂波邻域相位方向差图像
Figure 5. TerraSAR-X ship target and sea clutter NPDD images
图 6 TerraSAR-X舰船目标和海杂波邻域相位方向差直方图(以玫瑰图形式展示，红线表示平均方向)
Figure 6. TerraSAR-X ship target and sea clutter NPDD histograms (Presented in rose charts. Red line indicates mean direction)
图 8 舰船目标和海杂波的实部和虚部图像
Figure 8. Real and imaginary parts of ship target and sea clutter images
图 9 舰船目标和海杂波实部和虚部的直方图
Figure 9. Real and imaginary histograms of ship target and sea clutter images
图 11 基于复信号峰度的复广义高斯分布形状参数估计方法流程
Figure 11. Flowchart of shape parameter estimation of CGGD based on CSK
图 12 Novey的方法和基于复信号峰度的方法的均方误差结果对比
Figure 12. MSE results of Novey’s method and our method
图 13 Novey的方法和基于复信号峰度的方法的单次估计平均时间消耗对比
Figure 13. Average time consumption comparison for a single test of Novey’s method and our method
图 14 不同成像条件下海杂波和舰船目标的复信号峰度对比
Figure 14. CSK plots of sea clutter of typical sea clutter and ship targets from different acquisitions
图 15 Sentinel-1图像中的射频干扰现象(绿圆表示舰船目标)
Figure 15. Ships affected by RFIs in Sentinel-1 images (The green circles represent ships)
图 16 高斯分布迭代分割过程的示例
Figure 16. Illustration of the iteration process for a Gaussian distribution
图 17 舰船目标切片复信号峰度迭代分割和Otsu分割对比结果
Figure 17. Comparison of Otsu and CSK iteration segmentation results
表 1 TerraSAR-X舰船目标和海杂波相位图像循环统计量结果
Table 1. Circular statistical results of TerraSAR-X ship target and sea clutter
循环统计量 均值 方差 标准差 平均长度 偏度 峰度 (a) 舰船目标1 2.21 0.95 2.46 0.05 0 –0.01 (b) 海杂波1 –2.72 0.96 2.49 0.04 –0.01 0.01 (c) 舰船目标2 –0.34 0.98 2.84 0.02 0.01 –0.01 (d) 海杂波2 –2.87 0.98 2.83 0.02 0.01 0 
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表 2 TerraSAR-X舰船目标和海杂波的邻域相位方向差图像循环统计量结果
Table 2. Circular statistical results of TerraSAR-X ship target and sea clutter NPDD images
循环统计量 均值 方差 标准差 平均长度 偏度 峰度 (a) 舰船目标1 0 0.27 0.79 0.73 0 0.46 (b) 海杂波1 0 0.21 0.69 0.79 0 0.53 (c) 舰船目标2 0 0.37 0.96 0.63 0 0.35 (d) 海杂波2 0 0.21 0.69 0.79 0.01 0.53
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