A Group-Wise Feature Enhancement-and-Fusion Network with Dual-Polarization Feature Enrichment for SAR Ship Detection

Abstract

Ship detection in synthetic aperture radar (SAR) images is a significant and challenging task. However, most existing deep learning-based SAR ship detection approaches are confined to single-polarization SAR images and fail to leverage dual-polarization characteristics, which increases the difficulty of further improving the detection performance. One problem that requires a solution is how to make full use of the dual-polarization characteristics and how to excavate polarization features using the ship detection network. To tackle the problem, we propose a group-wise feature enhancement-and-fusion network with dual-polarization feature enrichment (GWFEF-Net) for better dual-polarization SAR ship detection. GWFEF-Net offers four contributions: (1) dual-polarization feature enrichment (DFE) for enriching the feature library and suppressing clutter interferences to facilitate feature extraction; (2) group-wise feature enhancement (GFE) for enhancing each polarization semantic feature to highlight each polarization feature region; (3) group-wise feature fusion (GFF) for fusing multi-scale polarization features to realize polarization features’ group-wise information interaction; (4) hybrid pooling channel attention (HPCA) for channel modeling to balance each polarization feature’s contribution. We conduct sufficient ablation studies to verify the effectiveness of each contribution. Extensive experiments on the Sentinel-1 dual-polarization SAR ship dataset demonstrate the superior performance of GWFEF-Net, with 94.18% in average precision (AP), compared with the other ten competitive methods. Specifically, GWFEF-Net can yield a 2.51% AP improvement compared with the second-best method.