A Hybrid Quantum Optimization Framework for Oil Spill Detection Using Marine Radar.
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| Title: | A Hybrid Quantum Optimization Framework for Oil Spill Detection Using Marine Radar. |
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| Authors: | Dong, Haihui1,2 (AUTHOR), Loon, Siow Chee1,2 (AUTHOR), Xu, Jin2,3 (AUTHOR) jinxu@gdou.edu.cn, Wang, Haixia1,3 (AUTHOR) |
| Source: | Remote Sensing. Oct2026, Vol. 18 Issue 19, p3433. 29p. |
| Subjects: | Quantum computing, Optimization algorithms, Image processing, Thresholding algorithms, Oil spill management, Radar |
| Abstract: | Highlights: What are the main findings? A two-stage framework combining QGJO-GRNN for candidate extraction and QHHO for threshold optimization is proposed to overcome low contrast and noise in marine radar imagery. The proposed method has achieved good performance in oil spill detection of shipborne marine radar. What are the implications of the main findings? The proposed method has achieved good performance in oil spill detection of shipborne marine radar. The findings clarify that the practical gain mainly stems from ROI-guided cascade processing and scattering-prior-driven sample collection, while quantum encoding primarily improves convergence stability, offering guidance for future multi-scene validation and extension to more complex thresholding objectives. Oil spills at sea pose a serious threat to marine ecosystems and coastal economic development. Therefore, rapid and accurate detection of oil spills is crucial for emergency response and environmental protection. Marine radar offers unique advantages in short-range, continuous, and all-weather monitoring, making it an important supplementary tool for oil spill detection. However, oil film targets in marine radar images exhibit low contrast and a low signal-to-noise ratio, and are easily affected by factors such as clutter and co-frequency interference, making it difficult for traditional detection methods to achieve accurate extraction. To address these issues, this paper proposes a two-stage framework for oil spill detection utilizing marine radar, which consists of candidate region extraction using a QGJO-optimized feature-weighted GRNN and precise segmentation using QHHO-based threshold optimization. The methodology begins with candidate region extraction, wherein pixel-level multidimensional scattering features are designed and scattering priors are leveraged to automate sample collection without manual annotation. A feature-weighted generalized regression neural network (GRNN) is subsequently constructed, with its parameters and feature weights jointly optimized via the Quantum Golden Jackal Optimization (QGJO) algorithm, thereby enabling efficient delineation of oil-film candidate regions. The subsequent object detection stage then formulates oil slick extraction as a single-threshold binary classification problem, for which the Quantum Harris–Hawk Optimization (QHHO) algorithm is adopted to autonomously determine the optimal segmentation threshold. Finally, morphological post-processing is performed to suppress residual noise and produce precise, continuous oil film contours. Results verify that the proposed method effectively reconciles detection completeness with false-alarm control, constituting an efficient and deployable solution for shipborne radar-based oil spill monitoring. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | Highlights: What are the main findings? A two-stage framework combining QGJO-GRNN for candidate extraction and QHHO for threshold optimization is proposed to overcome low contrast and noise in marine radar imagery. The proposed method has achieved good performance in oil spill detection of shipborne marine radar. What are the implications of the main findings? The proposed method has achieved good performance in oil spill detection of shipborne marine radar. The findings clarify that the practical gain mainly stems from ROI-guided cascade processing and scattering-prior-driven sample collection, while quantum encoding primarily improves convergence stability, offering guidance for future multi-scene validation and extension to more complex thresholding objectives. Oil spills at sea pose a serious threat to marine ecosystems and coastal economic development. Therefore, rapid and accurate detection of oil spills is crucial for emergency response and environmental protection. Marine radar offers unique advantages in short-range, continuous, and all-weather monitoring, making it an important supplementary tool for oil spill detection. However, oil film targets in marine radar images exhibit low contrast and a low signal-to-noise ratio, and are easily affected by factors such as clutter and co-frequency interference, making it difficult for traditional detection methods to achieve accurate extraction. To address these issues, this paper proposes a two-stage framework for oil spill detection utilizing marine radar, which consists of candidate region extraction using a QGJO-optimized feature-weighted GRNN and precise segmentation using QHHO-based threshold optimization. The methodology begins with candidate region extraction, wherein pixel-level multidimensional scattering features are designed and scattering priors are leveraged to automate sample collection without manual annotation. A feature-weighted generalized regression neural network (GRNN) is subsequently constructed, with its parameters and feature weights jointly optimized via the Quantum Golden Jackal Optimization (QGJO) algorithm, thereby enabling efficient delineation of oil-film candidate regions. The subsequent object detection stage then formulates oil slick extraction as a single-threshold binary classification problem, for which the Quantum Harris–Hawk Optimization (QHHO) algorithm is adopted to autonomously determine the optimal segmentation threshold. Finally, morphological post-processing is performed to suppress residual noise and produce precise, continuous oil film contours. Results verify that the proposed method effectively reconciles detection completeness with false-alarm control, constituting an efficient and deployable solution for shipborne radar-based oil spill monitoring. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 20724292 |
| DOI: | 10.3390/rs18193433 |
