A Simulation Study to Evaluate the Performance of the Cauchy Proximal Operator in Despeckling SAR Images of the Sea Surface

TL;DR

This paper investigates despeckling SAR ocean images with ship wakes using a novel Cauchy proximal operator, demonstrating superior performance over traditional regularisation methods through simulated experiments.

Contribution

It introduces a closed-form Cauchy proximal operator for despeckling SAR images, enabling improved regularisation in sparse algorithms.

Findings

Proposed method outperforms L1 and TV regularisation in despeckling quality.

Cauchy proximal operator effectively handles speckle noise in SAR images.

Simulation results confirm the method's superiority across various scenarios.

Abstract

The analysis of ocean surface is widely performed using synthetic aperture radar (SAR) imagery as it yields information for wide areas under challenging weather conditions, during day or night, etc. Speckle noise constitutes however the main reason for reduced performance in applications such as classification, ship detection, target tracking and so on. This paper presents an investigation into the despeckling of SAR images of the ocean that include ship wake structures, via sparse regularisation using the Cauchy proximal operator. We propose a closed-form expression for calculating the proximal operator for the Cauchy prior, which makes it applicable in generic proximal splitting algorithms. In our experiments, we simulate SAR images of moving vessels and their wakes. The performance of the proposed method is evaluated in comparison to the L1 and TV norm regularisation functions. The results show a superior performance of the proposed method for all the utilised images generated.