摘要
The oil spilled worldwide causes ecological disasters that result in enormous damages to the quality of marine environment, and great expenses on clear-up operations are needed. Due to its wide coverage and day-night all-weather observation capability, Synthetic Aperture Radar （SAR） is an important tool for oil spill monitoring and detection. C-band SAR is well adapted to detect oil pollution because oil slicks dampen the Bragg waves and reduce radar backscattering coefficients. In order to detect the area of oil slicks, the algorithm consists of these steps： Preprocessing, Masking of land areas, Detection of dark spots, Spot feature extraction, Dark spot classification. In this paper, the authors examined two coastal regions around Hong Kong and Yantai, China. The obtained results performed on Envisat ASAR images have demonstrated that it is efficient to detect oil spill around the coastal regions. The methodology still needs to be refined with the collection of more SAR data in the near future.
The oil spilled worldwide causes ecological disasters that result in enormous damages to the quality of marine environment, and great expenses on clear-up operations are needed. Due to its wide coverage and day-night all-weather observation capability, Synthetic Aperture Radar （SAR） is an important tool for oil spill monitoring and detection. C-band SAR is well adapted to detect oil pollution because oil slicks dampen the Bragg waves and reduce radar backscattering coefficients. In order to detect the area of oil slicks, the algorithm consists of these steps： Preprocessing, Masking of land areas, Detection of dark spots, Spot feature extraction, Dark spot classification. In this paper, the authors examined two coastal regions around Hong Kong and Yantai, China. The obtained results performed on Envisat ASAR images have demonstrated that it is efficient to detect oil spill around the coastal regions. The methodology still needs to be refined with the collection of more SAR data in the near future.
基金
The research is partly supported by a CUHK Direct Grant project under contract No 0455188
