Observation of Oil Slicks on the Sea Surface by Using Marine Navigation Radar

Atanassov, V.; Mladenov, L.; Rangelov, R.; Savchenko, A. K.

doi:10.1109/igarss.1991.579326

Cited by 13 publications

(8 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, he attempted to extract the oil spill information from the shipborne radar image by selectively intercepting the sea clutter image [11]. In 1991, Atanassov et al verified the feasibility of using shipborne radar to identify the size, shape, and dynamic information of oil spills based on statistical analysis [12]. Since then, there have been commercial products used for oil spill monitoring and early warning, such as Sigma of Rutter company [13][14][15].…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

Pan

Jia

et al. 2021

JMSE

View full text Add to dashboard Cite

Oil spill accidents have seriously harmed the marine environment. Effective oil spill monitoring can provide strong scientific and technological support for emergency response of law enforcement departments. Shipborne radar can be used to monitor oil spills immediately after the accident. In this paper, the original shipborne radar image collected by the teaching-practice ship Yukun of Dalian Maritime University during the oil spill accident of Dalian on 16 July 2010 was taken as the research data, and an oil spill detection method was proposed by using LBP texture feature and K-means algorithm. First, Laplacian operator, Otsu algorithm, and mean filter were used to suppress the co-frequency interference noises and high brightness pixels. Then the gray intensity correction matrix was used to reduce image nonuniformity. Next, using LBP texture feature and K-means clustering algorithm, the effective oil spill regions were extracted. Finally, the adaptive threshold was applied to identify the oil films. This method can automatically detect oil spills in shipborne radar image. It can provide a guarantee for real-time monitoring of oil spill accidents.

show abstract

Section: Introductionmentioning

confidence: 99%

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

Pan

Jia

et al. 2021

JMSE

View full text Add to dashboard Cite

show abstract

“…Shipborne radar has ability of large-range detection in real-time with a wide application prospect in the field of oil spill monitoring. Its ability of detecting oil spills from the echo images have been demonstrated since 1988 (Tennyson 1988;Atanassov et al 1991), but progress slowly. In recent years, the related achievements have gradually increased (Zhu et al 2015;Xu et al 2019a, b;Liu et al 2019a, b).…”

Section: Introductionmentioning

confidence: 99%

Oil spill discrimination of multi-time-domain shipborne radar images using active contour model

Pan

et al. 2021

Geosci. Lett.

View full text Add to dashboard Cite

Accidental oil spills cause serious pollution to the ocean and are difficult to control in short time. It is an important guarantee for emergency disposal to effectively monitor oil spills. Remote sensing is the main means to monitor oil spills. High false alarm rate has been an important bottleneck of this technology. In this paper, a multi-time-domain shipborne radar images discrimination mechanism was proposed. Based on the improved Sobel operator, Otsu and linear interpolation, the co-frequency interference noises were detected and suppressed. Gray intensity correction model (GICM) and dual-threshold method were used to eliminate highlighted continuous pixels. Oil films were extracted by using an active contour model (ACM). Finally, a multi-time-domain discrimination mechanism based on variation range tolerance of identified oil films centroids was designed to reduce the false alarm rate. It can provide technical support for decision-making and emergency response.

show abstract

“…The marine radar images used in this study are convenient and expedient to obtain. In marine radar images, the intensity of the backscattered signal in an oil spill area is weaker than in the neighboring waters, a phenomenon that can be exploited to detect oil spills [7]. Although several commercial systems have been developed, such as the oil spill detection (OSD) system of Miros (Norwegian company) [8,9], the SeaDarQ radar system of Nortek Netherlands [10], and the sigma S6 OSD system of Rutter (Canada company) [11], oil spill extraction methods are seldom publicized due to commercial competition.…”

Section: Introductionmentioning

confidence: 99%

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Liu

et al. 2019

Remote Sensing

View full text Add to dashboard Cite

Oil spills bring great damage to the environment and, in particular, to coastal ecosystems. The ability of identifying them accurately is important to prompt oil spill response. We propose a semi-automatic oil spill detection method, where texture analysis, machine learning, and adaptive thresholding are used to process X-band marine radar images. Coordinate transformation and noise reduction are first applied to the sampled radar images, coarse measurements of oil spills are then subjected to texture analysis and machine learning. To identify the loci of oil spills, a texture index calculated by four textural features of a grey level co-occurrence matrix is proposed. Machine learning methods, namely support vector machine, k-nearest neighbor, linear discriminant analysis, and ensemble learning are adopted to extract the coarse oil spill areas indicated by the texture index. Finally, fine measurements can be obtained by using adaptive thresholding on coarsely extracted oil spill areas. Fine measurements are insensitive to the results of coarse measurement. The proposed oil spill detection method was used on radar images that were sampled after an oil spill accident that occurred in the coastal region of Dalian, China on 21 July 2010. Using our processing method, thresholds do not have to be set manually and oil spills can be extracted semi-automatically. The extracted oil spills are accurate and consistent with visual interpretation.

show abstract

Observation of Oil Slicks on the Sea Surface by Using Marine Navigation Radar

Cited by 13 publications

References 3 publications

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

Oil Spill Detection Using LBP Feature and K-Means Clustering in Shipborne Radar Image

Oil spill discrimination of multi-time-domain shipborne radar images using active contour model

Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding

Contact Info

Product

Resources

About