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Maritime object classification with SAR imagery using quantum kernel methods

arXiv 2512.11367 ↗First posted 12 Dec 2025quant-ph cs.LGcited by 0

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Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 28 of its 85 references that are in the map. First posted on arXiv 12 Dec 2025.

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References · 28

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16 more
  1. 2021Training Quantum Embedding Kernels on Near-Term Quantum Computerscited by 69
  2. 2021Towards understanding the power of quantum kernels in the NISQ eracited by 43
  3. 2018The SEN1-2 Dataset for Deep Learning in SAR-Optical Data Fusioncited by 42
  4. 2022Bandwidth Enables Generalization in Quantum Kernel Modelscited by 32
  5. 2021Importance of Kernel Bandwidth in Quantum Machine Learningcited by 31
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  7. 2022xView3-SAR: Detecting Dark Fishing Activity Using Synthetic Aperture Radar Imagerycited by 25
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  12. 2024In Search of Quantum Advantage: Estimating the Number of Shots in Quantum Kernel Methodscited by 6
  13. 2024QSpeckleFilter: a Quantum Machine Learning approach for SAR speckle filteringcited by 2
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  15. 2025Comparative Analysis of Quantum and Classical Support Vector Classifiers for Software Bug Prediction: An Exploratory Studycited by 1
  16. 2025Quantum Meets SAR: A Novel Range-Doppler Algorithm for Next-Gen Earth Observationcited by 1

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