Objective

2

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

2

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

2

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

2

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.