Automatic Oil Spill Detection Research Articles

A self-evolving deep learning algorithm for automatic oil spill detection in Sentinel-1 SAR images

Oil spill accidents are one of the major problems causing marine pollution, and thus such accidents require rapid detection for early response. In recent years, deep learning algorithms for oil spill detection have been developed for analyzing SAR images. Nevertheless, to generation of deep learning training data using visual inspection is not only a time-consuming and labor-intensive, but also cause bias and lack of diversity in oil slick training data. This has easily led to false positives in challenging SAR images, such as large scale look-alikes, biologic surface films or oil slicks surrounded by look-alikes. In order to accommodate a broader range of SAR oil spill scenes, including those under challenging conditions, it is essential to continuously enhance the performance of oil spill detection algorithms. In this study, a novel self-evolving algorithm for automatic oil spill detection is proposed, which consists of three inter-connected modules: 1) oil spill detection, 2) generation of new training data, and 3) enhancement of deep learning models. The algorithm detected oil slicks automatically while the new SAR image was added, and then the additional high-quality training data was generated by an adaptive thresholding method to increase the performance of deep learning models. In order to detect oil slicks from whole SAR image with oil look-alikes, the variation of the backscattering coefficients near the oil boundary was considered as a key parameter to distinguish oil spills from look-alikes. As a result, after 21 self-evolving training cycles, 27 new training data were generated with an improved F1-score rising from 0.8423 to 0.8896. Despite the existence of look-alikes in various magnitudes, the algorithm successfully identified oil slicks, and the parameters of deep learning models were automatically updated. It was demonstrated that the performance of the algorithm was gradually improved by self-evolving without any human intervention. Additionally, the fundamental limitations of oil spill detection algorithms were mitigated by the proposed approaches, providing more possibilities for subsequent studies.

Preliminary Investigation on Marine Radar Oil Spill Monitoring Method Using YOLO Model

Due to the recent rapid growth of ocean oil development and transportation, the offshore oil spill risk accident probability has increased unevenly. The marine oil spill poses a great threat to the development of coastal cities. Therefore, effective and reliable technologies must be used to monitor oil spills to minimize disaster losses. Based on YOLO deep learning network, an automatic oil spill detection method was proposed. The experimental data preprocessing operations include noise reduction, gray adjustment, and local contrast enhancement. Then, real and synthetically generated marine radar oil spill images were used to make slice samples for training the model in the YOLOv5 network. The detection model can identify the effective oil spill monitoring region. Finally, an adaptive threshold was applied to extract the oil slicks in the effective oil spill monitoring regions. The YOLOv5 detection model generated had the advantage of high efficiency compared with existing methods. The offshore oil spill detection method proposed can support real-time and effective data for routine patrol inspection and accident emergency response.

A new approach to oil spill detection that combines deep learning with unmanned aerial vehicles

This study presents a novel approach to automatic oil spill detection, using unmanned aerial vehicle (UAV) images to realize intelligent control in oil production. Despite considerable effort, oil spills still cannot be detected automatically and effectively due to the complexity of the real production environment, which forces oil enterprises to manually inspect facilities and detect oil spills. To solve the problem, we propose an approach consisting of UAVs, deep learning and traditional algorithms—an approach which divides the oil spill detection task into three independent sub-tasks. First, we constructed a model based on the deep convolutional neural network, which can quickly detect the suspected oil spill area in images to ensure there are no omissions. Second, to remove other obstacles in the images, we adjusted the Otsu algorithm to filter the detection results, which improves precision while not affecting the recall rate. Third, the Maximally Stable Extremal Regions algorithm was used to obtain the detail polygon region from the detection box, thus automatically evaluating the severity of the oil spill. Experiments showed that our method could solve problems effectively, reducing the cost of oil spill detection by 57.2% when compared with the traditional manual inspection process.

Oil Spill Segmentation via Adversarial $f$ -Divergence Learning

We develop an automatic oil spill segmentation method in terms of $f$ -divergence minimization. We exploit $f$ -divergence for measuring the disagreement between the distributions of ground-truth and generated oil spill segmentations. To render tractable optimization, we minimize the tight lower bound of the $f$ -divergence by adversarial training a regressor and a generator, which are structured in different forms of deep neural networks separately. The generator aims at producing accurate oil spill segmentation, while the regressor characterizes discriminative distributions with respect to true and generated oil spill segmentations. It is the coplay between the generator net and the regressor net against each other that achieves a minimal of the maximum lower bound for the $f$ -divergence. The adversarial strategy enhances the representational powers of both the generator and the regressor and avoids requesting large amounts of labeled data for training the deep network parameters. In addition, the trained generator net enables automatic oil spill detection that does not require manual initialization. Benefiting from the comprehensiveness of $f$ -divergence for characterizing diversified distributions, our framework can accurately segment variously shaped oil spills in noisy synthetic aperture radar images. Experimental results validate the effectiveness of the proposed oil spill segmentation framework.

Automatic Mexico Gulf Oil Spill Detection from Radarsat-2 SAR Satellite Data Using Genetic Algorithm

In this work, a genetic algorithm is exploited for automatic detection of oil spills of small and large size. The route is achieved using arrays of RADARSAT-2 SAR ScanSAR Narrow single beam data obtained in the Gulf of Mexico. The study shows that genetic algorithm has automatically segmented the dark spot patches related to small and large oil spill pixels. This conclusion is confirmed by the receiveroperating characteristic (ROC) curve and ground data which have been documented. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the curve between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. The small oil spill sizes represented 30% of the discriminated oil spill pixels in ROC curve. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills of either small or large size and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill patterns detection and surveying in the Gulf of Mexico.

Automatic Detection of Oil Spill Disasters Along Gulf of Mexico Using RADARSAT-2 SAR Data

In this work, the genetic algorithm is utilised for automatic detection of oil spills under wind speed conditions larger than 20 ms−1. The procedure is implemented using sequences of RADARSAT-2 SAR ScanSAR Narrow single beam data acquired in the Gulf of Mexico. The study demonstrates that implementing crossover allows for generation of accurate oil spills pattern. This conclusion is confirmed by the receiver–operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified with the area under the ROC curve and the no-discrimination line of 85 %, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills and the ScanSAR Narrow single beam mode serves as an excellent sensor for oil spill detection and surveying under wind speed larger than 20 ms−1.

Reference spectral signature selection using density-based cluster for automatic oil spill detection in hyperspectral images.

Reference spectral signature selection is a fundamental work for automatic oil spill detection. To address this issue, a new approach is proposed here, which employs the density-based cluster to select a specific spectral signature from a hyperspectral image. This paper first introduces the framework of oil spill detection from hyperspectral images, indicating that detecting oil spill requires a reference spectral signature of oil spill, parameters of background, and a target detection algorithm. Based on the framework, we give the new reference spectral signature selection approach in details. Then, we demonstrate the estimation of background parameters according to the reflectance of seawater in the infrared bands. Next, the conventional adaptive cosine estimator (ACE) algorithm is employed to achieve oil spill detection. Finally, the proposed approach is tested via several practical hyperspectral images that are collected during the Horizon Deep water oil spill. The experimental results show that this new approach can automatically select the reference spectral signature of oil spills from hyperspectral images and has high detection performance.

Automatic detection of oil spills in the Gulf of Mexico from RADARSAT-2 SAR satellite data

This work aimed to design optimization procedures for oil spill footprint automatic detection from synthetic aperture radar (SAR) satellite data. The main motivation of this work is to utilize a genetic algorithm (GA) without involving post-classification image processing tools for oil spill footprint boundary shape optimizations that involve local and global optimizations. The procedures are operated using sequences of RADARSAT-2 SAR ScanSAR Narrow single beam data acquired in the Gulf of Mexico. The study shows that the GA has high performance for oil spill boundary shape automatic optimization and detection. This provides evidence with standard error of 0.12 and non-significant differences with different acquisition dates. The ScanSAR Narrow mode data shows the extremely existing of 90 % of the oil spill footprint compared to the sea surface roughness and look-alikes. It can be said that Scan SAR Narrow mode can monitor oil spill disasters. In conclusion, the GA can be used as an automatic tool for oil spill without involving other post-image processing classification.

Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery

We present a methodology to detect oil spills using MODIS near-infrared sun glittered radiance imagery. The methodology was developed by using a set of seven MODIS images (training dataset) and validated using four other images (validation dataset). The method is based on the ratio image R = L'GN/LGN, where L'GN is the MODIS-retrieved normalized sun glint radiance image and LGN the same quantity, but obtained from the Cox and Munk isotropic (independent of wind direction) sun glint model. We show that in the R image, while clean water pixel values tend to one, oil spills stand out as anomalies. Moreover, we provide a criterion to distinguish between positive and negative oil-water contrast. A pixel in an R image is classified as a potential oil spill or water via a variable threshold Rs as a function of L'GN, where the threshold values are obtained from the slicks of our training dataset. Two different fitting curves are provided for Rs, according to the contrast sign. The selection of the correct fitting curve is based on the contrast type, resulting from the criterion above. Results indicate that the thresholding is able to isolate the spills and that the spills of the validation dataset are successfully detected. Spurious look-alike features, such as clouds, and other non-spill features, e.g., large areas at the glint region border, are also detected as oil spills and must be eliminated. We believe that our methodology represents a novel and promising, though preliminary, approach towards automatic oil spill detection in optical satellite images.

Utilization of a genetic algorithm for the automatic detection of oil spill from RADARSAT-2 SAR satellite data

In this work, a genetic algorithm is applied for the automatic detection of oil spills. The procedure is implemented using sequences from RADARSAT-2 SAR ScanSAR Narrow single-beam data acquired in the Gulf of Mexico. The study demonstrates that the implementation of crossover allows for the generation of an accurate oil spill pattern. This conclusion is confirmed by the receiver-operating characteristic (ROC) curve. The ROC curve indicates that the existence of oil slick footprints can be identified using the area between the ROC curve and the no-discrimination line of 90%, which is greater than that of other surrounding environmental features. In conclusion, the genetic algorithm can be used as a tool for the automatic detection of oil spills, and the ScanSAR Narrow single-beam mode serves as an excellent sensor for oil spill detection and survey.

Automatic decision support system based on SAR data for oil spill detection

Global trade is mainly supported by maritime transport, which generates important pollution problems. Thus, effective surveillance and intervention means are necessary to ensure proper response to environmental emergencies. Synthetic Aperture Radar (SAR) has been established as a useful tool for detecting hydrocarbon spillages on the oceans surface. Several decision support systems have been based on this technology. This paper presents an automatic oil spill detection system based on SAR data which was developed on the basis of confirmed spillages and it was adapted to an important international shipping route off the Galician coast (northwest Iberian Peninsula). The system was supported by an adaptive segmentation process based on wind data as well as a shape oriented characterization algorithm. Moreover, two classifiers were developed and compared. Thus, image testing revealed up to 95.1% candidate labeling accuracy. Shared-memory parallel programming techniques were used to develop algorithms in order to improve above 25% of the system processing time.

A Model Based Approach to Radar Oil Spill Detection

ABSTRACT A typical oil spill recovery vessel has been historically outfitted with an oil spill detection (OSD) radar. During an oil spill recovery operation, there is a dedicated operator who is responsible for interpreting information from the radar image. Industry developments over the last several years now require that an OSD radar automatically detect and track an oil spill. There are two primary needs driving this development. The first is that OSD systems and operations are becoming more sophisticated; automatic OSD aids for a more efficient oil spill operation where an operator's attention may be directed to a potential spill. The automatic OSD also aids a multi-sensor system; one such example is where an OSD radar is used to steer an IR camera to a candidate spill for more detailed evaluation or validation. The other primary driver for automatic OSD is for monitoring systems, which serve for early warning. Monitoring systems may be found along coastal installations or oil platforms. The automatic spill detection functionality of an OSD system may be implemented in different levels of sophistication. Perhaps the simplest configuration is one that uses fixed thresholds relative to the image for alarming whether a region in a radar image is a spill or not. The benefit of simple threshold detector is that it is easy to implement in software. The weakness is that it is prone to both lower overall detection rate and high false alarm rate. A more robust automatic spill detection method is one that treats it as an image-processing problem. The paper here presents a model based OSD. Generation of confidence maps is central to the method and provides an indication of the likelihood of oil. Inputs to the confidence maps come from multiple sources, several of which are based on uniquely constructed models. Among these is a histogram comparator, which scans a radar image and compares the data to reference models from real oil spills. A discussion of the methods used focuses on (a) the necessary steps prior to the confidence map construction, (b) how the confidence maps are layered with inputs, (c) how the information in the confidence maps is transitioned into the detection of oil, (d) and finally alarming.

Entropy algorithm for automatic detection of oil spill from radarsat-2 SAR data

Synthetic aperture radar (SAR) is a precious foundation of oil spill detection, surveying and monitoring that improves oil spill detection by various approaches. The main objective of this work is to design automatic detection procedures for oil spill in synthetic aperture radar (SAR) satellite data. In doing so the Entropy algorithm tool was designed to investigate the occurrence of oil spill in Gulf of Mexico using RADARSAT-2 SAR satellite data. The study shows that entropy algorithm provides accurate pattern of oil slick in SAR data. This shown by 90% for oil spill, 3% look-alike and 7% for sea roughness using the receiver -operational characteristics (ROC) curve. It can therefore be concluded Entropy algorithm can be used as automatic tool for oil spill detection in RADARSAT-2 SAR data.

Comparison between mahalanobis classification and neural network for oil spill detection using RADARSAT-1 SAR data

Oil spill or leakage into waterways and ocean spreads very rapidly due to the action of wind and currents. The study of the behavior and movement of these oil spills in sea had become imperative in describing a suitable management plan for mitigating the adverse impacts arising from such accidents. But the inherent difficulty of discriminating between oil spills and look-alikes is a main challenge with Synthetic Aperture Radar (SAR) satellite data and this is a drawback, which makes it difficult to develop a fully automated algorithm for detection of oil spill. As such, an automatic algorithm with a reliable confidence estimator of oil spill would be highly desirable. The main objective of this work is to develop comparative automatic detection procedures for oil spill pixels in multimode (Standard beam S2, Wide beam W1 and fine beam F1) RADARSAT-1 SAR satellite data that were acquired in the Malacca Straits using two algorithms namely, post supervised classification, and neural network (NN) for oil spill detection. The results show that NN is the best indicator for oil spill detection as it can discriminate oil spill from its surrounding such as look-alikes, sea surface and land. The receiver operator characteristic (ROC) is used to determine the accuracy of oil spill detection from RADARSAT-1 SAR data. The results show that oil spills, lookalikes, and sea surface roughness are perfectly discriminated with an area difference of 20% for oil spill, 35% look–alikes, 15% land and 30% for the sea roughness. The NN shows higher performance in automatic detection of oil spill in RADARSAT-1 SAR data as compared to Mahalanobis classification with standard deviation of 0.12. It can therefore be concluded that NN algorithm is an appropriate algorithm for oil spill automatic detection and W1 beam mode is appropriate for oil spill and look-alikes discrimination and detection. Key words: Oil Spill, RADARSAT-1 SAR data, Mahalanobis classification, neural network (NN).

Classifiers and Confidence Estimation for Oil Spill Detection in ENVISAT ASAR Images

An improved classification approach is proposed for automatic oil spill detection in synthetic aperture radar images. The performance of statistical classifiers and support vector machines is compared. Regularized statistical classifiers prove to perform the best on this problem. To allow the user to tune the system with respect to the tradeoff between the number of true positive alarms and the number of false positives, an automatic confidence estimator has been developed. Combining the regularized classifier with confidence estimation leads to acceptable performance.

Oil Spill Detection in Radarsat and Envisat SAR Images

We present algorithms for automatic detection of oil spills in synthetic aperture radar (SAR) images. The algorithms consist of three main parts, namely: 1) detection of dark spots; 2) feature extraction from the dark spot candidates; and 3) classification of dark spots as oil spills or look-alikes. The algorithms have been trained on a large number of Radarsat and Envisat Advanced Synthetic Aperture Radar (ASAR) images. The performance of the algorithm is compared to manual and semiautomatic approaches in a benchmark study using 59 Radarsat and Envisat images. The algorithms can be considered to be a good alternative to manual inspection when large ocean areas are to be inspected

Automatic Oil Spill Detection by Marine X-Band Radars -New System Based on Processing of Digitized Radar Images

The OSD (Oil Spill Detection) system's main objective is to detect oil spills on the sea surface, and to track the oil spill over time. The system provides help both for those supervising oil fields, and those in charge of clean-up operations. The OSD system has been thoroughly tested during several field trials in the North Sea. During these trials the spill size, sea state and wind conditions varied throughout, thus enabling realistic real time testing. The OSD system may operate in complete darkness or fog, and for 24 hours per day. This greatly enabling recovery and skimming operations when visibility is poor.The Miros AS Company has developed the OSD system in close co-operation with the Norwegian Clean Seas Association for operating companies, thus complying with the NOFO practical and operational requirements. To identify an oil spill, the OSD system uses advanced image-processing algorithms on radar images and extracted by the Miros Wavex System. The OSD system is based on the fact that areas covered with oil will reflect less microwave power. This is due to the damping of capillary waves on the sea surface.

RADARSAT automatic algorithms for detecting coastal oil spill pollution

This paper presents work done for oil spill detection along coastal waters. Consumers are primarily interested in automatic detection of oil spills. The main objective of this study is to develop an automatic model for oil spill detection. This model includes texture analysis and two types of algorithms (Lee and Gamma). Texture analysis, such as contrast analysis, was used to discriminate between oil and water. The Lee algorithm was used to determine the linearity of oil movements, while the Gamma algorithm was used to determine oil spill spreading. The results showed that texture analysis and Lee and Gamma algorithms can be good modules for automatic detection of oil spills by using SAR data. Such modules will be useful for rapid detection of oil spills and is illustrative for educational purposes.

Automatic detection of oil spills in ERS SAR images

The authors present algorithms for the automatic detection of oil spills in SAR images. The developed framework consists of first detecting dark spots in the image, then computing a set of features for each dark spot, before the spot is classified as either an oil slick or a "lookalike" (other oceanographic phenomena which resemble oil slicks). The classification rule is constructed by combining statistical modeling with a rule-based approach. Prior knowledge about the higher probability for the presence of oil slicks around ships and oil platforms is incorporated into the model. In addition, knowledge about the external conditions like wind level and slick surroundings are taken into account. The presented algorithms are tested on 84 SAR images. The algorithm can discriminate between oil slicks and lookalikes with high accuracy, 94% of the oil slicks and 99% of the lookalikes were correctly classified.