Ship Detection in SAR Images via Kullback-Leibler Divergence-Based Matrix Information Geometry Detector

Abstract

Ship detection in synthetic aperture radar (SAR) imagery is an important application in the field of marine remote sensing. As the most common method in ship detection, classical constant false alarm rate (CFAR) methods rely on the target-to-clutter contrast and accurate estimates of distribution models, making it difficult to adapt to complex and variable sea surface backgrounds. Considering the limitations of the CFAR methods, this paper proposes a new detector for ship detection based on matrix information geometry (MIG) theory. The proposed detector models each SAR sample data as a Hermitian positive-definite (HPD) matrix, and uses the geometric mean of all HPD matrices corresponding to the background window to estimate the clutter, thus transforming the target detection problem into a measure of the Kullback-Leibler divergence (KLD) between two points on the matrix manifold. Different from the traditional CFAR detectors which use intensity information to differentiate between targets and clutter, the proposed detector utilizes the intrinsic geometry of the matrix manifold and employs KLD with good differentiation to measure the variance from targets to clutter.