【作者】 高贵；

【导师】 李德仁； 匡纲要；

【作者基本信息】 国防科学技术大学 ， 信息与通信工程， 2007， 博士

【摘要】 针对SAR图像解译应用的需求,为了从大幅SAR图像中快速有效地发现目标ROI,深入研究了SAR图像目标ROI自动获取技术。以发展实用化的SAR图像目标ROI自动获取技术为目的,在大量高分辨率SAR实测数据的支持下,采用理论分析和实验验证的研究方法,对SAR图像杂波统计建模、SAR图像自动目标检测、SAR图像自动目标鉴别等关键技术进行了系统深入的研究。第一章总结了SAR图像目标ROI自动获取技术研究的背景、意义,简明扼要地评述了SAR图像目标ROI自动获取技术的研究进展,指出了现存的问题,概括了论文的主要工作及创新。第二章研究了SAR图像杂波统计建模问题。首先综述了SAR图像统计建模的相关技术;在此基础上,结合实测数据库,深入分析了SAR图像的乘性噪声模型,得出了一些对杂波统计模型应用及进一步研究有意义的结论;运用实测SAR图像中表征不同地物类型的大量数据,深入分析了SAR图像杂波的统计特性。得出了在目前已有实用的统计模型中,G~0分布最适于描述SAR图像杂波统计特性,它对于均匀、一般不均匀和极不均匀杂波区域都能较精确建模的结论。第三章以构建实用化的目标检测过程为目的,研究了SAR图像的自动目标检测问题。在全面系统地总结前人工作的基础上,利用SAR图像杂波统计建模的研究成果,重点研究了目标检测的CFAR技术。提出了一种基于自动筛选的智能CFAR目标检测算法。该算法避免了传统CFAR技术应用于目标检测时的局限性,能够智能判断目标检测所处的杂波环境,以G~0分布为背景杂波统计模型,建立了不同杂波环境下统一的目标CFAR检测,大大增强了目标检测的自动性;在理论分析智能CFAR算法计算量的基础上,提出了智能CFAR算法相应的快速算法,大大增加了算法的实用性。第四章以构建实用化的目标鉴别过程为目的,研究了SAR图像的自动目标鉴别问题。在全面系统地总结前人工作的基础上,对鉴别的特征提取、特征选择、鉴别器的设计等展开了系列的研究。提出了一种目标鉴别的新方案,该方案包括目标鉴别的框架、模型以及算法;提出了基于特征选取鉴别和基于编队提取鉴别“序贯”连接相结合的目标鉴别框架;在基于特征选取进行目标鉴别的方法中,提出了目标鉴别的“松耦合”模型;提出了“松耦合”模型下目标鉴别的特征提取方法,包括已有特征的筛选和3个新的对比度特征的提出;改进了一种基于GA的特征选择方法,克服了已有方法对特征优劣评价不全面的问题;设计了加权二次距离鉴别器,提高了鉴别的性能;研究了基于目标编队知识进行进一步杂波虚警剔除的方法。第五章系统地总结了全文的工作,并给出了进一步研究的建议。更多还原

【Abstract】 The topic of this thesis is to deeply investigate the automatic techniques of acquiring target’s ROI (Region-of-interests) from SAR images rapidly and effectively, which is among the demands of SAR image interpretation applications. Aiming at developing the practical techniques of automatically acquiring target’s ROI, some key techniques such as statistical modeling of SAR clutter images, automatic target detection and discrimination from SAR images are systematically studied by theoretic analysis and experimental validation with lots of real high-resolution SAR data.In chapter 1 the research background and significance of automatically acquiring target’s ROI from SAR images are summarized. Then, the progress of this research is reviewed briefly and the currently existing problems are pointed out. Finally, the main work and the innovations of this thesis are summarized.Chapter 2 deals with the problem of statistical modeling of clutter in SAR images. Firstly, the relevant techniques of statistical modeling of clutter in SAR images are reviewed in detail. Secondly, based on this review, the multiplicative noise model (product model) for SAR images is analyzed comprehensively to reach some significant conclusions for the application and development of statistical modeling of clutter in SAR images. The clutter is analyzed comprehensively with the real SAR data characterizing different terrain categories. A conclusion is reached that the G°distribution is most appropriate to describe the statistical property of clutter and is relatively accurate for statistically modeling the homogeneous, heterogeneous and extremely heterogeneous clutter regions.Aiming at building the practical automatic target detection process, the problem of automatic target detection in SAR images is studied in chapter 3. Based on an extensive survey of the existing studies and the conclusion of chapter 2, we focus on the CFAR detection algorithms. An intelligent and fast CFAR algorithm based on automatic censoring is proposed for target detection in SAR images. This algorithm avoids the limitation of the conventional CFAR algorithms and can intelligently decide the clutter environment of detection. By introducing the G°distribution as the statistical model of clutter in this algorithm, the uniform CFAR detection is established under different clutter environments to enhance the automatic degree of target detection. Based on the theoretical analysis of the computation cost of the intelligent CFAR algorithm, the corresponding fast algorithm of the intelligent CFAR algorithm is also proposed to improve the practicality of target detection.In chapter 4, in order to conduct the powerfully practical automatic target discrimination process, the problem of automatic target discrimination in SAR images is studied. Based on the extensive summarization of the existing studies, a series of studies including feature extraction and selection of target discrimination, the design of discriminator, and so on are done. A new scheme of target discrimination in SAR images, consisted of frames, models and algorithms, is proposed. Under such a scheme, a global frame, combining orderly the algorithm based on feature extraction and that based on knowledge, is then proposed. Moreover, in the method of target discrimination based on feature extraction, a "loose-coupling" model is given. The existing features are chosen and three new features about the contrast are given under the "loose-coupling". Meanwhile, an algorithm of feature selection based on Genetic Algorithm is also modified to solve the problem that the existing algorithm can not evaluate the goodness-of-features comprehensively. The weighted quadratic distance discriminator is designed to improve the performance of target discrimination. Finally, a method based on the knowledge of target groups to remove clutter false alarms is also given.Chapter 5 concludes the research of this thesis. Some problems and interesting area for future research are pointed out.更多还原