  Most existing synthetic aperture radar (SAR) ship classification technologies
heavily rely on correctly labeled data, ignoring the discriminative features of
unlabeled SAR ship images. Even though researchers try to enrich CNN-based
features by introducing traditional handcrafted features, existing methods
easily cause information redundancy and fail to capture the interaction between
them. To address these issues, we propose a novel dual-stream contrastive
predictive network (DCPNet), which consists of two asymmetric task designs and
the false negative sample elimination module. The first task is to construct
positive sample pairs, guiding the core encoder to learn more general
representations. The second task is to encourage adaptive capture of the
correspondence between deep features and handcrated features, achieving
knowledge transfer within the model, and effectively improving the redundancy
caused by the feature fusion. To increase the separability between clusters, we
also design a cluster-level tasks. The experimental results on OpenSARShip and
FUSAR-Ship datasets demonstrate the improvement in classification accuracy of
supervised models and confirm the capability of learning effective
representations of DCPNet.
