A Novel Feature Weaving Pyramid Network to Improve CNN-Based SAR Ship Recognition Accuracy

In the field of maritime surveillance, ship recognition in synthetic aperture radar (SAR) images is a significant and fundamental step. Compared with traditional methods, convolutional neural networks (CNNs) tend to be the mainstream in SAR ship recognition. However, these methods ignore one core issue. Multi-scale features can enhance the expression ability of features, which are currently not well-exploited. In response to this problem, a novel feature weaving pyramid network (FWPN-Net) is proposed. FWPN-Net contains a multi-scale feature weaving module (MFWM), which can integrate high level semantic information and low level detailed information to obtain better representations of multi-scale SAR ship features. Experimental results on benchmark dataset OpenSARShip show that the proposed FWPN-Net performs better than classic CNN methods and modern SAR ship recognition CNN method.