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

TL;DR

This paper introduces a novel method combining the dual-tree complex wavelet transform with a Cauchy penalty to enhance Radon domain features, significantly improving ship wake detection accuracy in SAR images.

Contribution

It proposes a new inverse problem formulation using DT-CWT and a Cauchy penalty, solved with FB splitting, for superior ship wake detection in SAR imagery.

Findings

Achieves over 90% detection accuracy in SAR images.

Improves detection accuracy by 7% over previous methods.

Outperforms state-of-the-art techniques in various metrics.

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.