Exploiting the Dual-Tree Complex Wavelet Transform for Ship Wake Detection in SAR Imagery

Abstract

In this paper, we analyse synthetic aperture radar (SAR) images of the sea surface using an inverse problem formulation whereby Radon domain information is enhanced in order to accurately detect ship wakes. This is achieved by promoting linear features in the images. For the inverse problem-solving stage, we propose a penalty function, which combines the dual-tree complex wavelet transform (DT-CWT) with the non-convex Cauchy penalty function. The solution to this inverse problem is based on the forward-backward (FB) splitting algorithm to obtain enhanced images in the Radon domain. The proposed method achieves the best results and leads to significant improvement in terms of various performance metrics, compared to state-of-the-art ship wake detection methods. The accuracy of detecting ship wakes in SAR images with different frequency bands and spatial resolution reaches more than 90%, which clearly demonstrates an accuracy gain of 7% compared to the second-best approach.

CSEF-Net: Cross-Scale SAR Ship Detection Network Based on Efficient Receptive Field and Enhanced Hierarchical Fusion

“…Therefore, some improved CFAR algorithms have been proposed, one of which is based on the implementation of more optimal statistical distributions, such as Lognormal, Gamma, or Alpha distributions [5][6][7]. There is also one that utilizes wavelet transform and template matching [8,9] for ship target detection. These methods are more suitable for fixed sea surface scenes with less interference, and require a lot of preliminary work to analyze the characteristics of the target and the background.…”