Distribution Shift Metric Learning for Fine-Grained Ship Classification in SAR Images
Yongjie Xu, Haitao Lang
Abstract
Fine-grained ship classification in synthetic aperture radar (SAR) images is a challenging task, since SAR images can only provide limited discriminative information due to the limitation of SAR imaging mechanism. Distance metric learning (DML) methods have the ability to improve the discriminative ability of the feature representations through preserving the supervisory information of ship samples. In this article, we proposed a novel DML method, termed as distribution shift metric learning (DML-ds), which improves the original Laplacian regularized metric learning by adding an interclass distribution shift regularization term. Extensive experiments and in-depth analysis demonstrate that the proposed DML-ds can effectively increase the interclass separability and the intraclass compactness, thereby improving the fine-grained ship classification performance in SAR images, and outperforms most of state-of-the-art methods.