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Automatic oil-spill detection by marine X-band radars

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... This causes oil to generate darker areas in the radar images. The algorithms (Gangeskar [3]) contain functionality for making these areas appear rather distinctly in back-scatter intensity (BSI) images, which are images providing information about the average back-scattered electromagnetic intensity from the various areas of the sea surface. Further, the algorithms include functionality for automatic oil detection and tracking based on the information in the BSI images. ...
... The algorithms used for oil detection during these trials were developed using data from the Deep Spill trial onboard the vessel Johan Hjort in June 2000. The algorithms are described at various stages during the development by Gangeskar [3][5], [6]]. ...
... After the trials the work www.witpress.com, ISSN 1743-3541 (on-line) on the algorithms was completed (Gangeskar [3]) and the data collected during the trials were reprocessed in order to test the automatic oil detection functionality. Reasonable OS images were obtained for all periods with reasonable BSI images. ...
Conference Paper
Oil spill incidents represent a large cost, economically and environmentally. It is of great importance to detect an oil spill as soon as possible after a leakage, in order to reduce the consequences. Wavex is a system for monitoring scaled directional wave spectra and sea surface currents from X-band radar images. The oil spill detection (OSD) system considered in this paper consists of the hardware and software modules of the Wavex system, in addition to software modules specific for oil detection purposes. The principle of measurement is based on the fact that areas covered by oil spill reflect less microwave power, due to the dampening of the sea surface capillary waves. The OSD system was thoroughly tested at a field trial carried out in October 2004 near the Troll field, off the west coast of Norway. The Norwegian Clean Seas Association for Operating Companies (NOFO) especially arranged the trial for this purpose. Six small releases of oil equivalents were discharged, and they could be detected and followed by the system during various wind and wave conditions. Images providing an average of the back-scattered intensity from various parts of the sea surface were shown to be useful for the system operator. Continuous surveillance of local areas is particularly helpful during the hours of darkness. Later, the data were reprocessed with a new and completely automatic oil detection algorithm using methods of image segmentation and object classification, and the oil was automatically detected. This algorithm is now implemented in the real-time system. More effort should be put into testing the system operation during marginal conditions, such as low wind speeds with non-optimal directions of sight and rougher sea states, as well as testing the system during various geometrical conditions. Keywords: oil spill detection, field trials, navigation radar, signal processing, image processing.
... Currently, SAR (Synthetic Aperture Radar) is a common remote sensing tool that can effectively monitor oil spills, as its imaging is not constrained by sunlight, climate, or clouds, and the resolution is not impacted by flight altitude, which makes it capable of obtaining remote sensing data at any time in any weather, but the location of an oil spill needs to be pre-identified [7]. Marine radars are widely installed on ships and can obtain remote sensing data quickly and conveniently at a low cost, which makes them able to fulfill the time and space requirements of oil spill real-time monitoring [8,9]. ...
Article
Full-text available
Oil spills can cause damage to the marine environment. When an oil spill occurs in the sea, it is critical to rapidly detect and respond to it. Because of their convenience and low cost, navigational radar images are commonly employed in oil spill detection. However, they are currently only used to assess whether or not there are oil spills, and the area affected is calculated with less accuracy. The main reason for this is that there have been very few studies on how to retrieve oil spill locations. Given the above problems, this article introduces a model of image segmentation based on the soft attention mechanism. First, the semantic segmentation model was established to fully integrate multi-scale features. It takes the target detection model based on the feature pyramid network as the backbone model, including high-level semantic information and low-level location information. The channel attention method was then used for each of the feature layers of the model to calculate the weight relationship between channels to boost the model’s expressive ability for extracting oil spill features.Simultaneously, a multi-task loss function was used. Finally, the public dataset of oil spills on the sea surface was used for detection. The experimental results show that the proposed method improves the segmentation accuracy of the oil spill region. At the same time, compared with segmentation models, such as PSPNet, DeepLab V3+, and Attention U-net, the segmentation accuracy based on the pixel level improved to 95.77%, and the categorical pixel accuracy increased to 96.45%.
... Recently researchers have carried out work on improving the imaging of slicks from ship-borne radars [141]. Today there are some commercial products to enhance the images from ship-borne radar to enable oil imaging [142]. ...
Chapter
Full-text available
Remote-sensing for oil spills is reviewed. The technical aspects of sensors are summarized and the benefits and limitations of each sensor are given. The use of visible techniques is ubiquitous; however, it gives only the same results as visible monitoring. Oil has no particular spectral features that would allow for identification among the many possible background interferences. Identification of specific oil types is not possible. Cameras are useful to provide documentation. Infrared (IR) offers some potential as an oil spill sensor. In daytime oil absorbs light and remits a portion of this as thermal energy at temperatures 3 to 4 K above ambient. IR cameras are economical, however they suffer from problems such as the inability to discriminate oil from interferences on beaches, among weeds, debris or sediment, and under certain lighting conditions. Furthermore, water-in-oil emulsions may not be detected in the infrared, depending on light and sea conditions. The laser fluorosensor is a useful instrument because of its unique capability to identify oil on backgrounds that include water, soil, weeds, ice and snow. It is the only sensor that can positively discriminate oil on most backgrounds. The laser fluorosensor also allows for positive identification and discrimination between oil types. Radar detects oil on water because oil dampens water-surface capillary waves under low to moderate wave/wind conditions. Radar offers the only potential for large area searches, day/night and foul weather remote sensing. False targets can be as high as 95%. Satellite-borne radar sensors are now extensively used for mapping large spills or assisting in ship and platform discharge monitoring. The only commercial equipment that measures slick thickness is passive microwave.
... Recently researchers have carried out work on improving the imaging of slicks from ship-borne radars [141]. Today there are some commercial products to enhance the images from ship-borne radar to enable oil imaging [142]. ...
Article
Full-text available
Remote-sensing for oil spills is reviewed. The technical aspects of sensors are reviewed and the benefits and limitations of each sensor are given. Oil spill response often requires that remote sensing is used to detect and map the spill of interest. A wide variety of technologies had been tried.
... Ship-borne radar has similar limitations to airborne and satellite-borne radar and the additional handicap of low altitude, which restricts its range to between 8 and 30 km, depending on the height of the antenna (Gangeskar, 2004). Ordinary ship radars can be adjusted to reduce the effect of sea clutter de-enhancement, however specialized units perform much better for oil slick detection (Nøst and Egset, 2006;Suo et al., 2012). ...
Chapter
Full-text available
Even though the design and electronics of sensors are becoming increasingly sophisticated and sensors are becoming much less expensive, the operational use of remote sensing equipment lags behind the development of the technology. The most common forms of oil spill surveillance and mapping is done with simple still or video photography, which provide little, if any, forensic data. Remote sensing from aircraft is still the most common form of oil spill tracking. Attempts to use satellite remote sensing for oil spills, although successful, are not necessarily as claimed and are generally limited to identifying features at sites of known oil spills. The laser fluorosensor is a most useful instrument to forensics because of its unique capability to positively identify oil against most backgrounds, including water, soil, weeds, ice, and snow. Radar offers the only potential for searching in large areas and carrying out remote sensing during foul weather conditions, but offers very poor positive detection characteristics and thus low forensic capability. The usefulness of the visible spectrum for oil detection is limited. It is, however, an economical way to document oil spills and provide baseline data on shorelines or relative positions.
... The measuring range of the X-band radar employed in this study is 11.112 km, and the accurate recognition range when collecting data are within 5.556 km. 11 The Spx radar data capture card program divides the measuring range into 512 pieces. The scanning circumference is divided into 2048 pieces because of the 360 deg radar scanning and is restricted by its revolving speed and pulse width. ...
Article
Full-text available
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. © 2015 Society of Photo-Optical Instrumentation Engineers (SPIE).
... Gangeskar has proposed an automatic system that could be mounted on oil drilling platforms [107]. This system would use standard X-band ship navigation units and would provide an alert if an oil spill is present. ...
... This contrast can be exploited to tease apart oil spills [11]. Some commercial systems using marine radars have been developed, including the oil spill detection (OSD) system of Miros (Asker, Norway) [12,13] and the SeaDarQ radar system of Nortek B.V. (Badhoevedorp, Netherlands) [14,15]. However, due to commercial competition, the identification methods are seldom publicized. ...
Article
Full-text available
Oil spills generate a large cost in environmental and economic terms. Their identification plays an important role in oil-spill response. We propose an oil spill detection method with improved adaptive enhancement on X-band marine radar systems. The radar images used in this paper were acquired on 21 July 2010, from the teaching-training ship “YUKUN” of the Dalian Maritime University. According to the shape characteristic of co-channel interference, two convolutional filters are used to detect the location of the interference, followed by a mean filter to erase the interference. Small objects, such as bright speckles, are taken as a mask in the radar image and improved by the Fields-of-Experts model. The region marked by strong reflected signals from the sea’s surface is selected to identify oil spills. The selected region is subject to improved adaptive enhancement designed based on features of radar images. With the proposed adaptive enhancement technique, calculated oil spill detection is comparable to visual interpretation in accuracy.
... 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. Some scholarly studies have been published on how to use marine radar to detect oil spills in the 1980s [12,13], but the detailed oil spill segmentation method was not yet introduced. ...
Article
Full-text available
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.
... Using ship-borne radar images to detect oil spills in real-time is still in its infancy, but the ability to detect oil spills from their backscatter-intensity images has been demonstrated, under appropriate sea conditions [18,19]. Some related commercial monitoring products have been developed, such as Miros, SeaDarQ, FURUNO, and SHIRA [20][21][22][23]. However, due to corporate confidentiality policies, these products' technologies have not been publicly disclosed. ...
Article
Full-text available
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.
... 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]. However, due to the confidentiality policy of commercial companies, the core technologies have not yet been published. ...
Article
Full-text available
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.
Article
Oil spills cause damage in ocean environment. It is important to identify the oil spills for further treatment. We propose an oil spill detection method based on X-band marine radar image using texture analysis. In this method, first, received radar image was denoised by erasing co-channel interference and small speckles. Then, texture analysis was used to indicate the location of oil spills. In the texture analysis, we proposed a texture index calculated by four texture features of grey level co-occurrence matrix (GLCM). The texture index in oil spill area is higher than it in other place, which can be used for extracting oil spill area roughly. In the end, precise extraction of an oil spill was carried out by an adaptive thresholding algorithm on the area selected by the proposed texture index. According to the X-band marine radar images sampled in the oil spill accident on Xingang Port of Dalian, China, an oil spill detected by the proposed texture analysis method is comparable to visual interpretation.
Article
Oil spills cause great environmental damage and the ability to identify them accurately is important to prompt oil spill response. A power fitting method of radar echo is proposed in this paper to detect oil spills on sampled X-band marine radar images. In this method, first, the marine radar image is pre-processed, including coordinate transform and noise reduction. Then, the difference between the pre-processed radar image and power fitting result is obtained. Finally, oil spills are extracted by using a mean filter, the Otsu method, and connected component analysis. The proposed oil spill detection method was used on radar images sampled after an oil spill accident in the coastal region of Dalian, China, on 21 July 2010. The proposed method can detect oil spills without manual participation, and the extracted oil spills are comparable to visual interpretation.
Article
A verified Miros oil spill detection (OSD) system based on Marine X-band Radar has been developed for automatic detection and real-time presentation of position, extent, and drift of oil spills. The system ensures continuous oil spill detection in sea states Beaufort 2 to 6 independent of visibility and light conditions. The Miros OSD system is an add-on to the Miros Wavex system, which derives scaled directional wave spectra and sea surface currents from X-band radar images. The system collects environmental data including wind velocity and direction and uses it for quality control. The objects and noise present in the radar image are removed using advanced object and noise removal techniques. Back Scatter Intensity (BSI) images, obtained from post-processing of the images, also give binary oil spill images (OS images) using statistical methods, which on successive detections reveal trace images to represent tracking of oil spill.
Article
Marine oil spills affect the environment, economy, and quality of life for coastal inhabitants. This article presents a method of X-band marine radar oil-spill identification by considering the marine radar images of the 2010 Dalian 7-16 accident. The Prewitt operator was improved and a linear interpolation was proposed to suppress co-channel interferences. In addition, a model of a gray-intensity-correcting matrix is proposed to smooth a whole image, thus displaying the oil film more intuitively. Furthermore, a contrast-limited adaptive histogram equalization method was used to increase the contrast inside and outside the oil film. Moreover, the local adaptive thresholding method was improved to segment the oil spills. The results show that the proposed method is an improvement on similar previous approaches for this task when employing X-band marine radar images. The proposed method can provide technical and theoretical bases for emergency response, damage assessment, and liability identification of oil spills.
Conference Paper
Full-text available
A discussion on remote sensors for application to oil spills covers an IR camera or an IR/UV system, which can detect oil under a variety of conditions, discriminate oil from some backgrounds, and has the lowest cost of any sensor; laser fluorosensor, a most-useful instrument because of its unique capability to identify oil on backgrounds that include water, soil, weeds, ice, and snow; radar, which offers the only potential for large area searches and foul weather remote sensing; passive microwave radiometers, which have potential as all-weather oil sensors; optical sensors, the most common means of remote sensing; slick thickness determination; acoustic systems; satellite remote sensing; and oil under ice detection. This is an abstract of a paper presented at the 28th Arctic and Marine Oil Spill Program Technical Seminar (Calgary, Alberta 6/7-9/2005).
Chapter
Even though the design and electronics of sensors are becoming increasingly sophisticated and sensors are becoming much less expensive, the operational use of remote sensing equipment lags behind the development of the technology. The most common forms of oil spill surveillance and mapping is done with simple still or video photography, which provide little, if any, forensic data. Remote sensing from aircraft is still the most common form of oil spill tracking. Attempts to use satellite remote sensing for oil spills, although successful, are not necessarily as claimed and are generally limited to identifying features at sites of known oil spills. The laser fluorosensor is a most useful instrument to forensics because of its unique capability to positively identify oil against most backgrounds, including water, soil, weeds, ice, and snow. Radar offers the only potential for searching in large areas and carrying out remote sensing during foul weather conditions, but offers very poor positive detection characteristics and thus low forensic capability. The usefulness of the visible spectrum for oil detection is limited. It is, however, an economical way to document oil spills and provide baseline data on shorelines or relative positions.
Chapter
Full-text available
This chapter outlines several general reviews of oil spill remote sensing. Massive spills of oil and related petroleum products can have serious biological and economic impacts. Following a spill, there is a scrutiny both from public and media with demands that the location and extent of the oil spill be determined. This chapter discusses various sensors. Remote sensing is playing a major role in determining the extent of oil spill. With the help of this instrument, oil can be monitored on the open ocean around the clock. The demerit of this instrument is that it lags behind in technology. Another instrument known as 'optical sensor' helps in determining the extent of oil spill. Optical techniques, the most common means of remote sensing uses the same range of the visible spectrum detection. Of late, a new technology which is known as Global Positioning Systems (GPS) is directly used to map remote-sensing data onto base maps. This chapter reviews the future trends of remote sensing such as cameras and thermal IR cameras, laser technology. Also rapidly improving computer capabilities allows real-time processing. Combinations of airborne and satellite-borne sensor systems are recommended in order to respond effectively to major marine oil spills.
Conference Paper
The OSD system was thoroughly tested at a field trial carried out in October 2004 off the West Coast of Norway nearby the Troll field. The Norwegian Clean Seas Association for Operating Companies (NOFO) especially arranged the trial for this purpose. 6 small releases of oil equivalents were discharged, and they could be detected and followed by the system during various wind and wave conditions. Images providing an average of the back-scattered intensity from various parts of the sea surface were shown to be useful for the system operator. Continuous surveillance of local areas is particularly helpful during the hours of darkness. Later, the data were reprocessed with a new and completely automatic oil detection algorithm using methods of image segmentation and object classification and the oil was automatically detected. This algorithm is implemented in the real-time system and tested out in field trials carried out in June 2005. After the successful trials in June 2005 NOFO has bought five OSD systems. During the field trials arranged in May 2006, the Oil Spill Detection System was mounted on three different oil recovery vessels and the trials were located off the West Coast of Norway near the Frigg field. The object of this trial was for the oil detection radar, which is only one element in an oil recovery operation, to gain further operative experience in various conditions. The preliminary results of the field trials are presented in this article. Future trials should preferably include testing during weather conditions with higher wind speeds and waves
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