D-MFPN: A Doppler Feature Matrix Fused with a Multilayer Feature Pyramid Network for SAR Ship Detection

Ship detection from synthetic aperture radar (SAR) images has become a major research field in recent years. It plays role monitoring the ocean, marine rescue activities, and safety warnings. However, there are still some factors that restrict further improvements detecting performance, e.g., multi-scale ship transformation unfocused caused by motion. In order to resolve these issues, this paper, doppler feature matrix fused with multi-layer pyramid network (D-MFPN) is proposed for SAR detection. The D-MFPN takes single-look complex image data as input consists of two branches: branch designs enhance positioning capacity large ships combined an attention module refine map’s expressiveness, aims build characterizes ship’s motion state estimating center frequency modulation rate offset. To confirm validity each branch, individual ablation experiments conducted. experimental results on Gaofen-3 satellite dataset illustrate D-MFPN’s optimal performance defocused tasks compared six other competitive convolutional neural (CNN)-based detectors. Its satisfactory demonstrate application value deep-learning model features