目的 舰船目标检测是合成孔径雷达（SAR）图像在海事监测领域中的一项重要应用。由于海面微波散射的复杂性，SAR图像中海杂波分布具有非均匀性、非平稳性等特点，传统的基于恒虚警率（CFAR）的SAR图像舰船检测算法难以适应复杂多变的海杂波环境，无法实现实时有效的智能检测任务。鉴于此，本文提出了基于信息几何的SAR图像船舰目标检测方法，旨在分析统计流形及其在参数空间中的几何结构，探讨信息几何在SAR图像目标检测应用中的切入点，从新的角度提升该应用领域的理论与技术水平。方法 首先，运用威布尔分布族对SAR图像中的海杂波进行统计建模，利用最大似然方法估计SAR图像局部邻域像素的分布参数，并将不同参数下的统计分布作为威布尔流形上的不同点；其次，融合高斯分布的费歇耳度量来构造威布尔流形空间中概率分布之间的测度，实现目标与背景区域的差异性表征；最后，利用最大类间方差法，实现SAR图像舰船目标检测。结果 实验和分析表明，相比于传统的基于恒虚警率的检测算法，信息几何方法可以有效地区分舰船目标和海杂波背景，降低虚警率，实现舰船目标显著性表示与检测。结论 由于舰船目标的复杂后向散射特性，如何有效地表征这一差异，是统计类检测算法的关键所在。本文依据信息几何理论，将概率分布族的参数空间视为微分流形，在参数流形上构造合适的黎曼度量，对SAR图像中各像素局部邻域进行测度表征，可以显著性表示目标与背景杂波之间的统计差异，实现舰船目标检测。

Objective Among synthetic aperture radar (SAR) image applications, automatic ship detection in SAR images is an active research field and plays a crucial role in various related military and civil applications, such as ocean traffic surveillance, protection against illegal fisheries, and ship rescuing. Many algorithms have been developed for ship detection in SAR images. Among them, constant false alarm rate (CFAR) algorithms, which have minimal operational complexity and a regular structure, are the most commonly used for ship detection in SAR imagery. CFAR-based methods are simple and effective, and the corresponding adaptive threshold preserves a constant false alarm probability. However, due to the non-homogeneity of sea clutter in the intensity domain, which is caused by the complexity of microwave scattering on the ocean surface, traditional CFAR-based detection methods cannot easily adapt to the variability and complexity of the sea clutter environment, and they cannot realize a robust detection of targets within sea clutter. Another approach to detect targets in a sea clutter background is to extract the features of targets in SAR images. The detection strategy relies on the feature description and analysis of targets in high-resolution SAR images. However, each feature representation for targets has its strengths and weaknesses and should be evaluated according to practical application scenarios. Additionally, the resolution of most SAR images is often not sufficiently high to extract effective detailed target information. In view of these situations, a high-performance ship detector based on information geometry is proposed in this study. Method Information geometry originated from the study of the intrinsic properties of manifolds of probability distributions. This theory is a combination of mathematical statistic models and geometrical methods. The development of geometrical theory and numerical techniques has extended the applicability of information geometry to the field of signal/image analysis. The purpose of this research is to obtain an improved understanding and analysis of the statistical manifold and its geometric structure in parameter space. This work explores the application of information geometry theory in ship detection from SAR images and analyzes detection problems from a new perspective. The manifold model is a good representation of the structural information of the pixel distribution controlled by a set of parameter. On this basis, an effective ship detection approach in SAR images is developed in this study. First, the Weibull distribution is used to model clutter, and the maximum likelihood estimation method is adopted to estimate the distribution parameters of the local neighborhood pixels of the SAR image. Second, the statistical distribution under different parameters is regarded as the difference point in the Weibull manifold. Third, a novel Riemannian metric is constructed to realize distance measurement between probability distributions in manifold space. Finally, the targets are extracted using an automatic threshold selection method. Result According to the theory of modern geometry, two points that are similar in Euclidean space may be far apart in non-Euclidean space. A significant statistical difference exists between ship targets and sea clutter because of the complex backscattering feature of ships. The proposed method based on information geometry utilizes this feature and geometrical methods to implement non-Euclidean metrics between classes (ship targets and background clutter to achieve saliency representation and detection of targets). Detection experiments are conducted on real SAR imagery. The results of the conventional Weibull-based CFAR detector is also provided for comparison to validate the effectiveness of the proposed method in real data. Conventional CFAR detection methods fail to yield satisfactory results due to low signal-to-clutter ratio and varying local clutter. Compared with conventional CFAR approaches, the proposed method can enhance targets and measure the local dissimilarity between a target and its neighborhood by using the information geometrical structure. Experimental results also show that the proposed method based on information geometry is effective in discriminating between ships and sea clutter and has good performance in ship detection in SAR images. Conclusion Information geometry began as the application of differential geometry to statistical theory. It has been applied to study the geometrical structure of a manifold of probability distributions. Information geometry has developed and continues to develop with the types of geometric statement used and in its application areas. In reality, no geometric statement is true or false by nature. Sometimes, it is merely a question of choice. Given the discovery of the geometric meaning of Fisher information, which contributes to the development of information geometry in a concise and intuitive manner, the geometric structure of a set of positive densities in a given statistical manifold space has elicited the interest of many researchers. Moreover, the Riemannian metric is not unique. Many important families of probability distributions possess a series of metric structures. Each metric corresponds to a different geometric structure. For these reasons, extensive research has focused on identifying new geometrical structures of parametric statistical models. It provides statistical science with a highly efficient method for constructing abstract models that maximize the use of space in signal/image processing. The aim of this study is to show the benefits of statistical manifolds suitable for ship detection in SAR imagery and based on information geometry theory. The principal tool in this work is the metric construction by means of building new metrics from old ones. Theoretical analysis and experimental results show that information geometry provides detection problems with a new perspective to view the structure of the investigated statistical manifold.