Multiscale Ship Detection Method in SAR Images Based on Information Compensation and Feature Enhancement

Abstract

With the development of synthetic aperture radar (SAR)-based imaging technology, SAR ship detection has achieved notable breakthroughs. Due to the scale diversity and class imbalance of ships, ship detection in SAR images is still a substantial challenge. To solve these problems, this paper proposes a multiscale ship detection method for SAR images based on information compensation and feature enhancement. To improve the feature representations of multiscale ships, an information compensation module (ICM) and a feature enhancement module (FEM) are embedded into a feature pyramid network (FPN). Specifically, the ICM is constructed to extract and aggregate diverse spatial contextual information. At the same time, the FEM is introduced to solve the feature-level imbalance of the FPN by integrating multilevel ship features. In addition, a gradient density parameter is introduced to solve class imbalance problems. Experiments on a high-resolution SAR image dataset (HRSID) show that the proposed method achieves a comprehensive detection performance, i.e., 60.6% average precision (AP) and 67.1 frames per second (FPS) and outperforms other state-of-the-art methods.