Ship Wake Detection Based on Polarimetric Enhancement and Deep Learning via a Simulated Full-Polarized Dataset

Abstract

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.