Hierarchical Sampling Representation Detector for Ship Detection in SAR Images

Abstract

Ship detection achieves great significance in remote sensing of synthetic aperture radar (SAR) and many efforts have been done in recent years. However, distinguishing ship targets precisely from the interference of multiplicative non-Gaussian coherent speckle is still a challenging task due to the discreteness, variability, and nonlinearity of ship scattering features. A detection framework based on hierarchical sampling representation is introduced to alleviate the phenomenon in this article. First, ships in SAR images exhibit multiplicative non-Gaussian coherent speckle, which introduces nonlinear characteristics under the imaging mechanism of SAR. Therefore, a statistical feature learning module is proposed with a learnable design to describe the nonlinear representations and expand the feature space. Second, our method designs a convex-hull representation to fit the irregular contours of ships represented by strong scattering points. Third, in order to supervise and optimize the regression of convex-hull representation, a sparse low-rank reassignment module is employed to evaluate the positive samples with SAR mechanism and reassign ones of high quality, which produces better results. Furthermore, experimental results on three authoritative SAR-oriented datasets for ship detection application present the comprehensive performance of our method.

Keywords

• Convex-hull
• ship detection
• sparse and low-rank
• statistical feature learning
• synthetic aperture radar (SAR)