Feature Enhancement Pyramid and Shallow Feature Reconstruction Network for SAR Ship Detection

Recently, convolutional neural network based methods have been studied for ship detection in optical remote sensing images. However, it is challenging to apply them microwave synthetic aperture radar (SAR) First, most of the regions inshore scene include scattered spots and noises, which dramatically interfere with detection. Besides, SAR images contain targets different sizes, especially small ships dense distribution. Unfortunately, fewer distinguishing features making difficult be detected. In this article, we propose a novel called feature enhanced pyramid shallow reconstruction (FEPS-Net) solve above problems. We design enhancement pyramid, includes spatial module enhance position information suppress background noise, alignment problem misalignment during fusion. Additionally, images, extract semantic from ships. The effectiveness proposed demonstrated by experiments on two publicly available datasets: dataset high-resolution dataset. experimental results show that FEPS-Net has advantages over current state-of-the-art methods.