Distribution Discrepancy Maximization Metric Learning for Ship Classification in Synthetic Aperture Radar Images

Abstract

Supervised learning techniques are widely used in the task of ship classification in synthetic aperture radar (SAR) images in recent years. Learning distance metrics that describe the underlying distribution between data points based on the distance metric learning (DML) methods can further improve the performance of ship classification in SAR images. Traditional supervised DML methods usually learn distance metrics based on pairwise constraints, but ignore the importance of inter-class distribution discrepancy. In this study, we propose a novel DML method named distribution discrepancy maximization metric learning (DDMML) algorithm, which maximizes the maximum mean discrepancy (MMD) between different categories in the process of learning distance metrics. We adopt a high-resolution SAR ship database for experimental evaluation. The experimental results show that the proposed method outperforms the state-of-the-art DML methods.