volume | PIER Journals

Abstract

This article introduces a novel ship detection method for Synthetic Aperture Radar (SAR) images that leverages the principles of Finsler information geometry. It employs the curvature features of a statistical manifold as a discriminative mechanism to diminish the impact of sea clutter and augment the contrast between a target and its background. The ambiguity of the local microstructure and statistical characteristics is partially resolved by using information theory to select metric definitions and curvature representation of non-European space. This method models sea clutter using the Gamma Distribution Function (GDF), transforming the detection challenge into an anomaly detection framework within the GDF space. This approach establishes a theoretical detection framework rooted in Finsler information geometry by integrating statistical modeling with Finsler geometry. It harnesses the Finsler characteristics of GDF space to extract the curvature feature representations for each GDF. Detection is achieved by applying one-class support vector machines (SVMs) to a matrix of curvature values derived from these representations. The detection algorithm unfolds in two primary phases. Initially, it utilizes a family of probability distributions to capture geometrical information. Subsequently, curved features are employed for target detection. Through rigorous experimentation with real datasets, the method demonstrates enhanced resilience to sea clutter and outperforms existing techniques for analyzing distribution families, validating its effectiveness and robustness.

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Dr. Ke Wang

Prof. Meng Yang

Dr. Feng Cheng