Exploiting the dual-tree complex wavelet transform for ship wake detection in SAR imagery

Ma, W., Achim, A. and Karakus, O. ORCID: https://orcid.org/0000-0001-8009-9319 2021. Exploiting the dual-tree complex wavelet transform for ship wake detection in SAR imagery. Presented at: IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2021), Toronto, ON, Canada, 6-11 June 2021. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 10.1109/ICASSP39728.2021.9414898

Full text not available from this repository.

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	Computer Science & Informatics
Publisher:	IEEE
ISBN:	9781728176055
Last Modified:	19 May 2023 02:07
URI:	https://orca.cardiff.ac.uk/id/eprint/145192

Citation Data

Cited 1 time in Scopus. View in Scopus. Powered By Scopus® Data

Actions (repository staff only)

Edit Item