Maritime object classification with SAR imagery using quantum kernel methods

TL;DR

This paper explores the application of quantum kernel methods to classify maritime objects in SAR imagery, comparing their performance to classical kernels and highlighting potential advantages and limitations.

Contribution

It is the first to apply quantum kernel methods to maritime classification in SAR imagery, providing insights into their effectiveness and overfitting issues.

Findings

Quantum kernel methods match or outperform classical kernels with real SAR data.

Quantum encoding of complex SAR data tends to overfit and underperform.

Quantum kernels show promise but face current limitations in maritime SAR classification.

Abstract

Illegal, unreported, and unregulated (IUU) fishing causes global economic losses of 10-25 billion USD annually and undermines marine sustainability and governance. Synthetic Aperture Radar (SAR) provides reliable maritime surveillance under all weather and lighting conditions, but classifying small maritime objects in SAR imagery remains challenging. We investigate quantum machine learning for this task, focusing on quantum kernel methods (QKMs) applied to real and complex SAR chips extracted from the SARFish dataset. We tackle two binary classification problems, the first for distinguishing vessels from non-vessels, and the second for distinguishing fishing vessels from other types of vessels. We compare QKMs applied to real and complex SAR chips against classical Laplacian, RBF, and linear kernels applied to real SAR chips. We restrict the comparison to be between just kernel based models so that the comparison is as fair and meaningful as possible. Using noiseless numerical simulations of the quantum kernels, we find that with the real SAR chips, QKMs are capable of obtaining equal or better performance than the classical kernels in the best case. However, the specific quantum kernel used to encode the complex SAR data overfits and performs poorly. This work presents the first application of QKMs to maritime classification in SAR imagery and offers insight into the potential and current limitations of quantum-enhanced learning for maritime surveillance.