An Information-Geometric Optimization Method for Ship Detection in SAR Images

Abstract

This study aims to investigate the information-geometric optimization method for ship detection in synthetic aperture radar (SAR) images. The detection strategy relies on an optimization-based representation of images, which considers the relationship between local and global statistical information and is more robust to sea clutter. The algorithm has three main stages. First, a constant false alarm rate (CFAR) detector is used to accomplish the initial detection. It is designed to acquire the initial distribution parameters of pure sea clutter and ship targets. Second, a Riemannian metric is constructed according to the local statistical information. Its purpose is to design the energy function of the optimization model. Third, the detection problem is formulated as a maximum decision rule. The experimental results show that the model can effectively detect ships in SAR images, is not sensitive to clutter, and can eliminate the effect of inhomogeneous sea state conditions. Compared with similar algorithms, the proposed algorithm has higher detection precision and good stability in ship detection in SAR images.