A viable method to implement oil spill detection and monitoring based on marine radar is proposed. The primary data of this study are obtained from the X-band marine radar of the teaching–training ship, YUKUN, of the Dalian Maritime University on July 21, 2010, when a pipeline burst and an oil spill accident occurred at the Xingang Port in Dalian. Aiming at the working characteristics of marine radar, the adaptive median filter algorithm is improved to eliminate the radar shared-frequency interference by adding the identification of noise points and resetting the neighborhood window. A power attenuation correction method is proposed to solve the uneven distribution in resolution and echo intensity by acquiring the average power distribution of radar images simultaneously. Oil spill will be easily detected from different sea backgrounds after morphological processing, gray segmentation, and image smoothing. Comparison with the images extracted from a thermal infrared sensor on the same monitoring point demonstrates the validity of the extraction method for oil spill based on X-band marine radar.
The harm of oil spills has caused extensive public concern. Remote sensing technology has become one of the most effective means of monitoring oil spill. However, how to evaluate the information extraction capabilities of various sensors and choose the most effective one has become an important issue. The current evaluation of sensors to detect oil films was mainly using in-situ measured spectra as a reference to determine the favorable band, but ignoring the effects of environmental noise and spectral response function. To understand the precision and accuracy of environment variables acquired from remote sensing, it is important to evaluate the target detection sensitivity of the entire sensor-air-target system corresponding to the change of reflectivity. The measurement data associated with the evaluation is environmental noise equivalent reflectance difference (NEΔRE ), which depends on the instrument signal to noise ratio(SNR) and other image data noise (such as atmospheric variables, scattered sky light scattering and direct sunlight, etc.). Hyperion remote sensing data is taken as an example for evaluation of its oil spill detection capabilities with the prerequisite that the impact of the spatial resolution is ignored. In order to evaluate the sensor’s sensitivity of the film of water, the reflectance spectral data of light diesel and crude oil film were used. To obtain Hyperion reflectance data, we used FLAASH to do the atmospheric correction. The spectral response functions of Hyperion sensor was used for filtering the measured reflectance of the oil films to the theoretic spectral response. Then, these spectral response spectra were normalized to NEΔRE, according to which, the sensitivity of the sensor in oil film detecting could be evaluated. For crude oil, the range for Hyperion sensor to identify the film is within the wavelength from 518nm to 610nm (Band 17 to Band 26 of Hyperion sensors), within which the thin film and thick film can also be distinguished. For light diesel oil film, the range for Hyperion sensor to identify the film is within the wavelength from 468nm to 752nm (Band 12 to Band 40 of Hyperion sensors).