Ship wake detection using synthetic aperture radar (SAR) imagery provides a way to obtain small marine ship information, but it often becomes unavailable and unreliable during a high sea state. Polarimetric information provides a potential way to solve this problem, which can enhance the ship target as well as the ship wake features. However, three challenges still exist in ship wake detection in polarimetric SAR imagery: the unwanted influences of bright and singular points on ship wake detection, the lack of performance analysis of wake detection by new-type polarimetric enhancement methods, and the difficulty of using the assessment criteria for ship wake detection. In this paper, we try to solve the above problems. Firstly, fully polarized SAR imagery of both ship turbulent and Kelvin wake is simulated based on the two-scale composite model, and the Polarimetric Whitening Filter (PWF) and Polarimetric Detection Optimization Filter (PDOF) are applied to the simulated fully polarized SAR imagery to enhance the ship wake features. Secondly, since the bright and singular points resulting from the ship echoes and the polarimetric enhancement methods may lead to misdetections, a logarithm process and z-score normalization pre-processing has been applied to the images. Then, a new assessment criterion for wake detection performance has been formulated, and the probability of missing detections (PMDs) and the probability of false alarms (PFAs) have been defined for two different requirements. And a Radon transform-based ship wake detection method for both ship turbulence and Kelvin wake has been carried out in horizontal–horizontal (HH), vertical–vertical (VV), horizontal–vertical (HV), PWF and PDOF SAR imagery. Finally, an analysis of the wake detection performance has been carried out. The PWF and PDOF can improve the wake detection performance by an average of nearly 50 percent compared with the HH and VV.

Ship wake detection provides a supplementary way for ship target detection; however, the detection performances of small and slow targets under high sea state are always unsatisfying. Also, the potential of polarimetric enhancement and deep learning (DL) techniques in ship wake detection still remains to be further discovered. In this article, first, the polarimetric enhancement methods based on the polarimetric whitening filter (PWF) and polarimetric detection optimization filter (PDOF) have been researched. Since the measured full-polarized synthetic aperture radar (SAR) images of ship wake are rather limited and inadequate for DL techniques, a full-polarized wake detection dataset (FPWDD) has been established for the first time. It was constructed based on 6000 simulated polarimetric SAR (PolSAR) images of a typical small surface vehicle, including four kinds of target numbers, five kinds of wind speeds, five kinds of wind directions, five kinds of ship velocities, and 36 kinds of ship heading angles. It is composed of two kinds of images: the full covariance matrixes stored in the TIF format and the images after the Pauli decomposition stored in the RGB format. Furthermore, the ship wake detection has been performed based on the FPWDD and three widely applied target detection DL networks for the HH, HV, VV, PWF, and PDOF channels. Finally, an analysis of the wake detection performances of the five channels has been carried out, which has proved that the polarimetric enhancement methods can obviously enhance the wake detection performance of small surface vehicles, with most evaluation metrics about 10% higher than that of HH, VV, and HV.