面向舰船目标检测的单通道复值SAR图像统计建模方法研究

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。 

Statistical Modeling Methods of Single-channel Complex-valued SAR Images for Ship Detection

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.