Dual Branch Deep Network for Ship Classification of Dual-Polarized SAR Images

Ship classification is usually a challenging task due to the small sizes of ship targets and the lack of significant differences between different categories. In terms of synthetic aperture radar (SAR) images, most existing deep learning (DL)-based methods are not designed from the angle of polarimetric characteristics to achieve ship classification. Thus, when facing the ship classification task of dual-polarized SAR images, these networks are often unsatisfactory. To cure this shortcoming, we here propose a novel dual branch deep network (DBDN) specifically designed for dual-polarized SAR ship classification. Our approach consists of three key modules: the image construction module (ICM), the feature extraction module (FEM), and the feature fusion and classifier module (FFCM). In ICM, two novel pseudo-RGB images are constructed for the first time, i.e., the polarimetric features-guided pseudo-RGB image (PF-RGB) and the texture features-guided pseudo-RGB image (TF-RGB), which can more accurately and comprehensively reflect ships' characteristics. FEM enables the network to focus on important ship features and suppress irrelevant noise through transferred layers and designed ConvNeXt-Attention block (CNABlock), enhancing the discriminative capability of different ships. Finally, FFCM extracts and combines various ship features for classification, wherein the enhanced inverted residual block (EIRBlock) and the channel spatial attention module (CSAM) components are proposed as well. The performance of DBDN is evaluated on the OpenSARShip2.0 dataset, and experimental results show that DBDN achieves excellent performance in all evaluation metrics in comparison with some state-of-the-art (SOTA) algorithms. For example, compared to the recently proposed method deep SAR-net (DSN), DBDN further improves the accuracy by 4.74% and 4.07% in the three-class and six-class classification tasks, respectively.