Cascaded Feature Fusion Pyramid Network for Ship Detection in Dualpolarization SAR Images

Synthetic aperture radar (SAR) has been widely applied in maritime target detection. However, most existing SAR ship detection algorithms based on convolutional neural network (CNN) only use single polarization SAR images for detection, neglecting to further improve the detection performance by utilizing the rich polarization information of the SAR images. To deal with this issue, this paper proposes a Cascaded Feature Fusion Pyramid Network (CFFPN) for ship detection in dual-polarization SAR images. The CFFPN builds a cascaded feature fusion module (CFFM) to fuse the enriched polarization information in SAR images. Extensive evaluations conducted on the the dual-polarization SAR ship detection dataset showcase the remarkable effectiveness of CFFPN, achieving an average precision (AP) of 93.4%. This outperforms the other five competitive methods. Notably, CFFPN exhibits a notable improvement of 1.3% in AP compared to the second-best method.