DPFF-Net: Dual-Polarization Image Feature Fusion Network for SAR Ship Detection

Intelligent ship detection algorithms for synthetic aperture radar (SAR) images have achieved significant results in Earth observation applications. By learning features such as scale, shape, and texture from samples, they can quickly locate and recognize ships in complex backgrounds. However, due to the lack of use of polarization features, the upper bound of detection performance is still limited, especially under poor image quality conditions such as ambiguous interference. To solve this, the dual-polarization image feature fusion network (DPFF-Net) is proposed. The key of it lies in adaptive mining, enhancement, and fusion of polarization features through the designed siamese structure, polarization-aware feature enhancement block (PAEB), and dynamic gated fusion block (DGFB). With fully utilizing complementary information hidden between copolarization and cross-polarization data, more comprehensive and accurate features are obtained and used as the detect head input. Thus, the proposed algorithm achieves state-of-the-art performance, and its effectiveness is validated by experiments on dual-polarization SAR datasets.