Scattering Information Fusion Network for Oriented Ship Detection in SAR Images

Han Wang, Silei Liu, Yixuan Lv, Shengyang Li

Abstract: Synthetic aperture radar (SAR) image ship detection is a popular area of ocean remote sensing, which has broad application prospects in ocean monitoring, maritime rescue, and other tasks. Recently, deep learning has been used in this field, but convolutional neural network (CNN)-based SAR ship detection still faces some challenges. First, due to the characteristic of CNN’s local convolution, the global information of the ship is not sufficiently learned and the detection is vulnerable to complex background interference. Second, SAR ship imaging varies greatly under different imaging conditions and postures, so CNN is difficult to adapt to scattering change imaging. To solve these problems, we propose a scattering information fusion network (SIFNet) for oriented ship detection in SAR images consisting of a multiscale contextual semantic information fusion (MCSIF) module and a scattering points information learning (SPIL) module. The MCSIF module enhances the acquisition of global information, enabling the network to extract more efficient feature maps. The SPIL module takes advantage of the fact that scattering points can stably represent the key features of the ship under different imaging conditions to make detection more robust through scattering information learning. Experiments show that our method achieves the highest F1-score and AP50 on both high-resolution SAR images dataset (HRSID) and rotated ship detection dataset in SAR image (RSDD-SAR) datasets.