High-performance target detection algorithms have been proposed for ship detection in Synthetic Aperture Radar (SAR) images in recent years. However, most of them are applied in the situation of single-polarization SA...
High-performance target detection algorithms have been proposed for ship detection in Synthetic Aperture Radar (SAR) images in recent years. However, most of them are applied in the situation of single-polarization SAR images and rarely consider the important polarization information in SAR images. In this paper, a polarization-guided strategy is presented to improve the detection performance in single polarization SAR images by predicting polarization type. In which, an extra polarization-guided head is employed following the backbone network to guide the feature maps from the backbone to explore polarization information. Then, the feature map with the polarization information is fused with those from backbone network to enhance feature extraction in feature pyramid networks (FPN), and yields better detection performance. Experiments conducted demonstrate the effectiveness of the proposed method, which can be easily merged with different detectors.
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