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Enhanced Marine Radar Oil Spill Detection via Feature Guidance and BBO-SA Hybrid Optimization.

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Title: Enhanced Marine Radar Oil Spill Detection via Feature Guidance and BBO-SA Hybrid Optimization.
Authors: Jia, Baozhu1,2,3 (AUTHOR), Guo, Zekun1,2,3 (AUTHOR), Xu, Jin1,2,3 (AUTHOR) jinxu@gdou.edu.cn, Dong, Xinru1,4 (AUTHOR), Chu, Lilin1,2,3 (AUTHOR), Li, Zheng1,2,3 (AUTHOR), Wang, Haixia3,4 (AUTHOR)
Source: Remote Sensing. May2026, Vol. 18 Issue 10, p1551. 22p.
Subjects: Radar, Metaheuristic algorithms, Territorial waters, Clustering algorithms, Oil spill management, Emergency communication systems, Feature extraction
Abstract: Highlights: What are the main findings? The unsupervised region-of-interest extraction mechanism proposed in this study combines DBSCAN clustering with three types of features to effectively distinguish between sea clutter and oil film regions under unlabeled conditions. To address the poor adaptability of traditional threshold-based segmentation, an improved BBO-SA hybrid optimization algorithm is introduced. By combining this with an adaptive temperature update strategy based on stagnation detection and suboptimal solution acceptance rates, the algorithm achieves synergistic optimization that balances global search and local exploration. What are the implications of the main findings? This method provides a technical solution for emergency oil spill monitoring in nearshore waters that requires no large number of labeled samples and can operate automatically, effectively reducing reliance on manual feature design and expert experience while enhancing the robustness and practicality of oil slick detection. By projecting the detection results onto a polar coordinate sector display format, this method enables the integration and fusion of data with electronic nautical charts, the Automatic Identification System (AIS), and other information, thereby providing a reference for oil spill emergency decision-making. X-band marine radar offers unique advantages for monitoring nearshore oil spills. However, oil films and sea clutter exhibit high pixel intensity overlap in radar images. Traditional threshold segmentation and machine learning methods have certain limitations in terms of feature extraction, Region of Interest (ROI) guidance, threshold optimization adaptability, and unsupervised capabilities. To address these issues, a method of oil film detection for ship radar based on multi-dimensional feature-guided extraction and hybrid optimization search is proposed. By combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering with multidimensional features, this method automatically extracts ROIs under unlabeled conditions, effectively suppressing sea clutter interference. Subsequently, an improved Beaver Behavior Optimizer (BBO) and simulated annealing (SA) hybrid algorithm (BBO-SA) is introduced within the ROIs, along with a designed adaptive temperature update strategy, to achieve coordinated optimization of global and local searches. The experimental results demonstrate that the method described in this paper performs exceptionally well across all evaluation metrics, confirming its accuracy and robustness in oil film detection. It provides a viable technical approach for emergency monitoring of nearshore oil spills. [ABSTRACT FROM AUTHOR]
Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Enhanced Marine Radar Oil Spill Detection via Feature Guidance and BBO-SA Hybrid Optimization.
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– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Highlights: What are the main findings? The unsupervised region-of-interest extraction mechanism proposed in this study combines DBSCAN clustering with three types of features to effectively distinguish between sea clutter and oil film regions under unlabeled conditions. To address the poor adaptability of traditional threshold-based segmentation, an improved BBO-SA hybrid optimization algorithm is introduced. By combining this with an adaptive temperature update strategy based on stagnation detection and suboptimal solution acceptance rates, the algorithm achieves synergistic optimization that balances global search and local exploration. What are the implications of the main findings? This method provides a technical solution for emergency oil spill monitoring in nearshore waters that requires no large number of labeled samples and can operate automatically, effectively reducing reliance on manual feature design and expert experience while enhancing the robustness and practicality of oil slick detection. By projecting the detection results onto a polar coordinate sector display format, this method enables the integration and fusion of data with electronic nautical charts, the Automatic Identification System (AIS), and other information, thereby providing a reference for oil spill emergency decision-making. X-band marine radar offers unique advantages for monitoring nearshore oil spills. However, oil films and sea clutter exhibit high pixel intensity overlap in radar images. Traditional threshold segmentation and machine learning methods have certain limitations in terms of feature extraction, Region of Interest (ROI) guidance, threshold optimization adaptability, and unsupervised capabilities. To address these issues, a method of oil film detection for ship radar based on multi-dimensional feature-guided extraction and hybrid optimization search is proposed. By combining Density-Based Spatial Clustering of Applications with Noise (DBSCAN) clustering with multidimensional features, this method automatically extracts ROIs under unlabeled conditions, effectively suppressing sea clutter interference. Subsequently, an improved Beaver Behavior Optimizer (BBO) and simulated annealing (SA) hybrid algorithm (BBO-SA) is introduced within the ROIs, along with a designed adaptive temperature update strategy, to achieve coordinated optimization of global and local searches. The experimental results demonstrate that the method described in this paper performs exceptionally well across all evaluation metrics, confirming its accuracy and robustness in oil film detection. It provides a viable technical approach for emergency monitoring of nearshore oil spills. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Remote Sensing is the property of MDPI and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.3390/rs18101551
    Languages:
      – Code: eng
        Text: English
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      Pagination:
        PageCount: 22
        StartPage: 1551
    Subjects:
      – SubjectFull: Radar
        Type: general
      – SubjectFull: Metaheuristic algorithms
        Type: general
      – SubjectFull: Territorial waters
        Type: general
      – SubjectFull: Clustering algorithms
        Type: general
      – SubjectFull: Oil spill management
        Type: general
      – SubjectFull: Emergency communication systems
        Type: general
      – SubjectFull: Feature extraction
        Type: general
    Titles:
      – TitleFull: Enhanced Marine Radar Oil Spill Detection via Feature Guidance and BBO-SA Hybrid Optimization.
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            NameFull: Jia, Baozhu
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            NameFull: Guo, Zekun
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            NameFull: Chu, Lilin
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              M: 05
              Text: May2026
              Type: published
              Y: 2026
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