Maritime object classification with SAR imagery using quantum kernel methods

2025-12-12

AI Summary

Addressing the challenge of classifying small maritime targets (e.g., vessel vs. non-vessel, fishing vessel vs. non-fishing vessel) in SAR imagery for IUU fishing monitoring, this work pioneers the application of Quantum Kernel Methods (QKM) to SAR maritime image classification. We employ noisy quantum circuit simulations to evaluate QKM against classical kernels—including Laplacian, RBF, and linear—across both real-valued and complex-valued SAR patch features. Results show that QKM achieves performance comparable to or exceeding classical kernels under ideal, noiseless simulation conditions; however, no statistically significant advantage is observed on realistic, complex-valued SAR data. This study thus provides the first empirical benchmark and methodological framework for quantum machine learning in remote sensing classification, revealing both the preliminary potential and current practical limitations of quantum-enhanced learning for maritime surveillance.

Technology Category

• Machine Learning: Quantum Machine Learning
• Computer Vision: Image and Video Retrieval
• Data Mining & Knowledge Management: Intelligent Query Processing

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Illegal, unreported, and unregulated (IUU) fishing causes global economic losses of $10-25 billion 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. Using noiseless numerical simulations of the quantum kernels, we find that QKMs are capable of obtaining equal or better performance than the classical kernel on these tasks in the best case, but do not demonstrate a clear advantage for the complex SAR data. 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.

Problem

• Classifying small maritime objects in SAR imagery
• Distinguishing vessels from non-vessels in SAR data
• Differentiating fishing vessels from other vessel types

Innovation

• Quantum kernel methods for SAR imagery classification
• Comparing quantum and classical kernels on real and complex data
• First application of quantum machine learning to maritime surveillance