Can Cui
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Can Cui has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Bo Li
Maritime College, Guangdong Ocean University, Zhanjiang 524088, China
2 shared publications - Haixia Wang
Navigation College, Dalian Maritime University, Dalian 116026, China
2 shared publications - Jin Xu
Naval Architecture and Shipping College, Guangdong Ocean University, Zhanjiang 524088, China
2 shared publications - Baigang Zhao
Navigation College, Dalian Maritime University, Dalian, China
1 shared publication - Peng Liu
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication - Yang Zhao
Navigation College, Dalian Maritime University, Dalian 116026, China
1 shared publication
Latest Publications
- Open Access
- 21 March 2020
- Published by MDPI AG in Algorithms
- Vol. 13 (3) , 69
Oil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.
- 21 March 2020
- Published by MDPI AG in Algorithms
- Vol. 13 (3) , 69
Open AccessOil Spill Monitoring of Shipborne Radar Image Features Using SVM and Local Adaptive Threshold
In the case of marine accidents, monitoring marine oil spills can provide an important basis for identifying liabilities and assessing the damage. Shipborne radar can ensure large-scale, real-time monitoring, in all weather, with high-resolution. It therefore has the potential for broad applications in oil spill monitoring. Considering the original gray-scale image from the shipborne radar acquired in the case of the Dalian 7.16 oil spill accident, a complete oil spill detection method is proposed. Firstly, the co-frequency interferences and speckles in the original image are eliminated by preprocessing. Secondly, the wave information is classified using a support vector machine (SVM), and the effective wave monitoring area is generated according to the gray distribution matrix. Finally, oil spills are detected by a local adaptive threshold and displayed on an electronic chart based on geographic information system (GIS). The results show that the SVM can extract the effective wave information from the original shipborne radar image, and the local adaptive threshold method has strong applicability for oil film segmentation. This method can provide a technical basis for real-time cleaning and liability determination in oil spill accidents.
- Open Access
- 18 July 2019
- Published by MDPI AG in Remote Sensing
- Vol. 11 (14) , 1698
Oil Spill Segmentation in Ship-Borne Radar Images with an Improved Active Contour Model
Oil spills cause serious damage to marine ecosystems and environments. The application of ship-borne radars to monitor oil spill emergencies and rescue operations has shown promise, but has not been well-studied. This paper presents an improved Active Contour Model (ACM) for oil film detection in ship-borne radar images using pixel area threshold parameters. After applying a pre-processing scheme with a Laplace operator, an Otsu threshold, and mean and median filtering, the shape and area of the oil film can be calculated rapidly. Compared with other ACMs, the improved Local Binary Fitting (LBF) model is robust and has a fast calculation speed for uniform ship-borne radar sea clutter images. The proposed method achieves better results and higher operation efficiency than other automatic and semi-automatic methods for oil film detection in ship-borne radar images. Furthermore, it provides a scientific basis to assess pollution scope and estimate the necessary cleaning materials during oil spills.
- 18 July 2019
- Published by MDPI AG in Remote Sensing
- Vol. 11 (14) , 1698
Open AccessOil Spill Segmentation in Ship-Borne Radar Images with an Improved Active Contour Model
Oil spills cause serious damage to marine ecosystems and environments. The application of ship-borne radars to monitor oil spill emergencies and rescue operations has shown promise, but has not been well-studied. This paper presents an improved Active Contour Model (ACM) for oil film detection in ship-borne radar images using pixel area threshold parameters. After applying a pre-processing scheme with a Laplace operator, an Otsu threshold, and mean and median filtering, the shape and area of the oil film can be calculated rapidly. Compared with other ACMs, the improved Local Binary Fitting (LBF) model is robust and has a fast calculation speed for uniform ship-borne radar sea clutter images. The proposed method achieves better results and higher operation efficiency than other automatic and semi-automatic methods for oil film detection in ship-borne radar images. Furthermore, it provides a scientific basis to assess pollution scope and estimate the necessary cleaning materials during oil spills.