Transfer Learning for SAR Image Classification Via Deep Joint Distribution Adaptation Networks

Abstract

The problem of different characters of heterogeneous synthetic aperture radar (SAR) images leads to poor performances for transfer learning of SAR image classification. To address this issue, a semisupervised model named as deep joint distribution adaptation networks (DJDANs) is proposed for transfer learning from a source SAR image to a different but similar target SAR image, which aims to match the joint probability distributions between the source domain and target domain. In the proposed DJDAN, a marginal distribution adaptation network is developed to map features across the domains into an augmented common feature subspace, which aims to match the marginal probability distributions and unify the dimensions. Then, a conditional distribution adaptation network is proposed to transfer knowledge across the domains, which aims to reduce the discrepancies of the conditional probability distributions and enhance the effectiveness of feature representation. Moreover, one-versus-rest classification is utilized in the proposed framework, which aims to improve the discrimination between the inside and outside class. Experimental results demonstrate the effectiveness of the proposed deep networks.