Semisupervised Heterogeneous Domain Adaptation via Dynamic Joint Correlation Alignment Network for Ship Classification in SAR Imagery

Abstract

Improving ship classification performance in synthetic aperture radar (SAR) imagery by transferring knowledge from the related domain is a newly emerging research topic. Existing methods follow supervised or unsupervised homogeneous transfer learning techniques with certain restrictions on the use of features (homogeneous rather than heterogeneous) and data (ignoring excavate the potential of unlabeled target domain data), which may hinder further performance improvements. To address these problems, this letter proposes a dynamic joint correlation alignment (DJ-CORAL) network to conduct semi-supervised heterogeneous domain adaptation (HDA). Specifically, DJ-CORAL firstly transforms the heterogeneous features from the source and target domains into a common subspace to eliminate the heterogeneity, then simultaneously performs classifier adaptation and joint marginal and conditional distribution alignment to facilitate the domain shift minimization. Comprehensive experiments validate the superiority of the proposed DJ-CORAL network against state-of-the-art HDA methods. The codes are available at https://github.com/BUCT-RS-ML/DJ-CORAL.