Marine Oil Spill Detection Research Articles

Marine Oil-Spill Detection in Multi-Polarization Image-Based SAR on Improved FCN

Marine Oil-Spill Detection in Multi-Polarization Image-Based SAR on Improved FCN

Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data

Marine oil spill detection is vital for strengthening the emergency commands of oil spill accidents and repairing the marine environment after a disaster. Polarimetric Synthetic Aperture Radar (Pol-SAR) can obtain abundant information of the targets by measuring their complex scattering matrices, which is conducive to analyze and interpret the scattering mechanism of oil slicks, look-alikes, and seawater and realize the extraction and detection of oil slicks. The polarimetric features of quad-pol SAR have now been extended to oil spill detection. Inspired by this advancement, we proposed a set of improved polarimetric feature combination based on polarimetric scattering entropy H and the improved anisotropy A12–H_A12. The objective of this study was to improve the distinguishability between oil slicks, look-alikes, and background seawater. First, the oil spill detection capability of the H_A12 combination was observed to be superior than that obtained using the traditional H_A combination; therefore, it can be adopted as an alternate oil spill detection strategy to the latter. Second, H(1 − A12) combination can enhance the scattering randomness of the oil spill target, which outperformed the remaining types of polarimetric feature parameters in different oil spill scenarios, including in respect to the relative thickness information of oil slicks, oil slicks and look-alikes, and different types of oil slicks. The evaluations and comparisons showed that the proposed polarimetric features can indicate the oil slick information and effectively suppress the sea clutter and look-alike information.

Marine oil spill detection using Synthetic Aperture Radar over Indian Ocean

Four oil spill events over the Indian Ocean including Chennai, Sharjah, Al Khiran and Mubarak Village are analyzed using Sentinel-1 satellite data. General National Oceanic and Atmospheric Administration (NOAA) Operational Modeling Environment (GNOME) model is utilized for oil spills trajectory production, whereas oil spills weathering processes are modeled using Automated Data Inquiry for Oil Spill (ADIOS). Synthetic Aperture Radar (SAR) based oil spill detection technique provided reliable results at the wind speed between 3 to 9 m/s for all events. Maximum oil spill movement (33 km) from the source point is observed in the Al Khiran, whereas evaporation rate of crude (degraded) oil is observed as high (low). The Near Real Time (NRT) detection of oil spill using SAR imagery needs high computational power, however, provides better results. This study concludes that SAR based oil spill detection is a cost-effective technique and can be utilized for mapping of oil spills.

Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment

Although advancements in remote sensing technology have facilitated quick capture and identification of the source and location of oil spills in water bodies, the presence of other biogenic elements (lookalikes) with similar visual attributes hinder rapid detection and prompt decision making for emergency response. To date, different methods have been applied to distinguish oil spills from lookalikes with limited success. In addition, accurately modeling the trajectory of oil spills remains a challenge. Thus, we aim to provide further insights on the multi-faceted problem by undertaking a holistic review of past and current approaches to marine oil spill disaster reduction as well as explore the potentials of emerging digital trends in minimizing oil spill hazards. The scope of previous reviews is extended by covering the inter-related dimensions of detection, discrimination, and trajectory prediction of oil spills for vulnerability assessment. Findings show that both optical and microwave airborne and satellite remote sensors are used for oil spill monitoring with microwave sensors being more widely used due to their ability to operate under any weather condition. However, the accuracy of both sensors is affected by the presence of biogenic elements, leading to false positive depiction of oil spills. Statistical image segmentation has been widely used to discriminate lookalikes from oil spills with varying levels of accuracy but the emergence of digitalization technologies in the fourth industrial revolution (IR 4.0) is enabling the use of Machine learning (ML) and deep learning (DL) models, which are more promising than the statistical methods. The Support Vector Machine (SVM) and Artificial Neural Network (ANN) are the most used machine learning algorithms for oil spill detection, although the restriction of ML models to feed forward image classification without support for the end-to-end trainable framework limits its accuracy. On the other hand, deep learning models’ strong feature extraction and autonomous learning capability enhance their detection accuracy. Also, mathematical models based on lagrangian method have improved oil spill trajectory prediction with higher real time accuracy than the conventional worst case, average and survey-based approaches. However, these newer models are unable to quantify oil droplets and uncertainty in vulnerability prediction. Considering that there is yet no single best remote sensing technique for unambiguous detection and discrimination of oil spills and lookalikes, it is imperative to advance research in the field in order to improve existing technology and develop specialized sensors for accurate oil spill detection and enhanced classification, leveraging emerging geospatial computer vision initiatives.

A novel deep learning instance segmentation model for automated marine oil spill detection

The visual similarity of oil slick and other elements, known as look-alike, affects the reliability of synthetic aperture radar (SAR) images for marine oil spill detection. So far, detection and discrimination of oil spill and look-alike are still limited to the use of traditional machine learning algorithms and semantic segmentation deep learning models with limited accuracy. Thus, this study developed a novel deep learning oil spill detection model using computer vision instance segmentation Mask-Region-based Convolutional Neural Network (Mask R-CNN) model. The model training was conducted using transfer learning on the ResNet 101 on COCO as backbone in combination with Feature Pyramid Network (FPN) architecture for feature extraction at 30 epochs with 0.001 learning rate. Testing of the model was conducted using the least training and validation loss value on the withheld testing images. The model’s performance was evaluated using precision, recall, specificity, IoU, F1-measure and overall accuracy values. Ship detection and segmentation had the highest performance with overall accuracy of 98.3%. The model equally showed a higher accuracy for oil spill and look-alike detection and segmentation although oil spill detection outperformed look-alike with overall accuracy values of 96.6% and 91.0% respectively. The study concluded that the deep learning instance segmentation model performs better than conventional machine learning models and deep learning semantic segmentation models in detection and segmentation.

Buoy Sensor Cyberattack Detection in Offshore Petroleum Cyber-Physical Systems

Frequently occurred oil leaking accidents can induce significant damage to the ocean ecosystem and environment. Integrated buoy sensing, which functions as a tool for periodically monitoring oil existence, plays an essential role in oil leakage detection in an offshore petroleum Internet of Things (IoT) and Cyber Physical System (CPS). Buoy sensor cyberattack can severely affect the ability to detect the petroleum leakage and hence delay the pollution recovery process. Despite these, existing techniques seldom deal with attacks on buoy sensors and their impacts on marine oil spill detection. In this article, a Partially observable Markov decision process based Buoy Sensor Cyberattack detection (PBSC) technique is proposed. Proposed PBSC technique utilizing Partially Observable Markov Decision Process (POMDP) method, which is a stochastic process based on Markov decision process, to evaluate the cyberattack probability for each buoy sensors. Cyberattack probability is evaluated by cross entropy based oil simulation method. This technique can efficiently identify attacked sensors and locate the oil leaking sources, which facilitates future pollution recovery. Experimental results from a marine area in Shenzhen, China demonstrate that the proposed technique can improve the detection accuracy by up to 50 percent while ruining $\times 6$ × 6 faster than the state of art cyberattack techniques.

A Novel Marine Oil Spillage Identification Scheme Based on Convolution Neural Network Feature Extraction From Fully Polarimetric SAR Imagery

Marine oil spill pollution has caused serious impacts on marine ecological environments, ecological resources and marine economy. Synthetic Aperture Radar (SAR), especially polarmetric SAR (PolSAR), has been proven to be a powerful and efficient tool for marine oil spill detection. In general, traditional oil spill detection methods mainly rely on artificially-extracted polarization characteristics, and the detection accuracy is limited by the quality of feature extraction. Recently proposed Convolutional neural network (CNN) is capable of mining spatial feature from large data set automatically. Inspired by these, in this paper we proposed a novel oil spill identification method based on multi-layer deep feature extraction by CNN. Firstly, PolSAR data are converted into a 9-channel data block to feed the CNN. Then, a 5-layer CNN architecture is built to extract two high-level features from the original data automatically. The features are fused after dimension reduction via principal component analysis (PCA). Finally, support vector machine method with radial basis function kernel (RBF-SVM) is utilized for classification. Three sets of RADARSAT-2 fully polarimetric SAR data were used in this study to validate the proposed method. The obtained results reveal that the proposed method provides competitive results in overall classification accuracy and kappa coefficient. Moreover, this method can improve the accuracy of oil spill detection, reduce the false alarm rate, and effectively distinguish an oil spill from a biogenic slick.

Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

As a major marine pollution source, oil spills largely threaten the sustainability of the coastal environment. Polarimetric synthetic aperture radar remote sensing has become a promising approach for marine oil spill detection since it could effectively separate crude oil and biogenic look-alikes. However, on the sea surface, the signal to noise ratio of Synthetic Aperture Radar (SAR) backscatter is usually low, especially for cross-polarized channels. In practice, it is necessary to combine the refined detail of slick-sea boundary derived from the co-polarized channel and the highly accurate crude slick/look-alike classification result obtained based on the polarimetric information. In this paper, the architecture for oil spill detection based on polarimetric SAR is proposed and analyzed. The performance of different polarimetric SAR filters for oil spill classification are compared. Polarimetric SAR features are extracted and taken as the input of Staked Auto Encoder (SAE) to achieve high accurate classification between crude oil, biogenic slicks, and clean sea surface. A post-processing method is proposed to combine the classification result derived from SAE and the refined boundary derived from VV channel power image based on special density thresholding (SDT). Experiments were conducted on spaceborne fully polarimetric SAR images where both crude oil and biogenic slicks were presented on the sea surface.

Shore-based GNSS-R method for marine oil spill detection

Shore-based GNSS-R method for marine oil spill detection

Detection and Monitoring of Oil Spills Using Moderate/High-Resolution Remote Sensing Images.

Current marine oil spill detection and monitoring methods using high-resolution remote sensing imagery are quite limited. This study presented a new bottom-up and top-down visual saliency model. We used Landsat 8, GF-1, MAMS, HJ-1 oil spill imagery as dataset. A simplified, graph-based visual saliency model was used to extract bottom-up saliency. It could identify the regions with high visual saliency object in the ocean. A spectral similarity match model was used to obtain top-down saliency. It could distinguish oil regions and exclude the other salient interference by spectrums. The regions of interest containing oil spills were integrated using these complementary saliency detection steps. Then, the genetic neural network was used to complete the image classification. These steps increased the speed of analysis. For the test dataset, the average running time of the entire process to detect regions of interest was 204.56s. During image segmentation, the oil spill was extracted using a genetic neural network. The classification results showed that the method had a low false-alarm rate (high accuracy of 91.42%) and was able to increase the speed of the detection process (fast runtime of 19.88s). The test image dataset was composed of different types of features over large areas in complicated imaging conditions. The proposed model was proved to be robust in complex sea conditions.

Fully Connected Continuous Conditional Random Field With Stochastic Cliques for Dark-Spot Detection In SAR Imagery

Dark-spot detection from synthetic aperture radar (SAR) imagery is a fundamental step in marine oil-spill detection and monitoring. However, achieving robust and accurate detection is difficult due to SAR sensor limitations and the complex marine environment. To address this problem, the large-scale spatial contextual information in SAR imagery has to be utilized to increase the class separability between the dark spot and the background. A stochastic fully connected continuous conditional random field (SFCCRF) approach to model SAR imagery and perform soft-label inference has been designed and built, leading to an efficient detection algorithm. Instead of treating all pixels in the imagery as being connected, SFCCRF determines the connectivity of two pixels in a stochastic manner based on their proximity in both feature space and image space. Since SFCCRF provides an efficient and effective way for modeling the large-scale spatial correlation effect, the resulting soft labels can resist the influence of speckle noise and highlight the difference between dark spot and the background. Dark-spot detection is achieved by binarizing the soft labels estimated by SFCCRF. The proposed algorithm is tested on both simulated and real SAR imageries. The results show that SFCCRF can delineate the dark spot with low commission and omission error rates.

THE EUROPEAN CONTROLLING SYSTEM TO REDUCE OIL DISCHARGES IN THE SEA

Ship-origin operational discharges of oil mainly include the discharge of bilge water from motor rooms, fuel oil sludge and oily ballast water from fuel tanks. Also, various tankers can illegally discharge of tank-washing residues. Accidental discharges can appear when ship collide or come in distress at sea (e.g. engine breakdown, fire, explosion, pipeline breaks). There is necessity of continuous inspection of marine shipping routes, especially in environmentally sensitive areas (e.g. whole Baltic Sea). If protection against oil pollution is considered - the coastal nations of the North Sea are formed in the Bonn Agreement, whereas coastal nations of the Baltic Sea are formed in the HELCOM convention. Both organizations carrying out the international aerial surveillances and manage international oil-combat systems. Unfortunately, air surveillance can operate mainly in good weather, good visibility and in daylight. The surface of the whole world is observed independently on the time of day and weather by antennas of Synthetic Aperture Radars (SAR) of dozen military satellites with resolution of several meters. Some civil satellites equipped with SAR, supply every few days a set of signals which in ground-based centres are transformed to image of a define area of the sea surface. In these images, the shapes of places, which can be interpreted as polluted by an oil film, are shown. The system is introduced after EU directive and managed by European Maritime Safety Agency (EMSA). EMSA has developed the CleanSeaNet service - a satellite based monitoring system for marine oil spill detection. Advantages and limitations of the SAR-methods are discussed in this study. Physical, meteorological and hydrological as well as organizational conditions for effective use of this system are considered.

A comparative study of different classification techniques for marine oil spill identification using RADARSAT-1 imagery

The discrimination of oil spills and look-alike phenomena (e.g., low wind area, wind front area and natural slicks) on Synthetic Aperture Radar (SAR) images is a crucial task in marine oil spill detection. Many classification techniques can be employed for this purpose. In order to make the best use of the large variety of statistical and machine learning classification methods, it is necessary to assess their performance differences and make recommendations for classifier selection and improvement. The objective of this paper is to compare different classification techniques for oil-spill detection in RADARSAT-1 imagery. The data of this study consists of 15 features of 192 oil spills and look-alikes identified by Canadian Ice Service between 2004 and 2008 off Canada's east and west coastal areas. The studied classifiers include the Support Vector Machine (SVM), Artificial Neural Network (ANN), tree-based ensemble classifiers (bagging, bundling and boosting), Generalized Additive Model (GAM) and Penalized Linear Discriminant Analysis (PLDA). Two performance measures, the specificity at fixed sensitivity (80%) and the area under the Receiver Operating Characteristic (ROC) curve (AUC), were estimated using cross-validation to evaluate the performance of classifiers at a high sensitivity. Overall, the bundling technique which achieved a median specificity of 90.7%, at sensitivity of 80%, significantly outperformed the second best (i.e. bagging) by 1.5 percentage points, and the worst (i.e. ANN) by 15 percentage points. The median values of AUC measure indicated consistent results. Bundling and bagging achieved comparable median AUC values of about 92%, followed by GAM and PLDA, with ANN yielding the smallest. Most classifiers (SVM, bundling and especially PLDA and ANN) performed significantly better on datasets pre-processed by log-transformation and standardization than on the original dataset. These results demonstrate the importance and benefit of selecting the optimal classifiers for oil spill classification, and configuring the classifiers by proper feature construction techniques.

Optimal Detection and Estimation of Marine Oil Spills Through Coherent Pluralism

An important area of environmental science involves the combination of information from diverse sources relating to a similar endpoint. Majority of optical remote sensing techniques used for marine oil spills detection have been reported lately of having high number of false alarms (oil slick look-a-likes) phenomena which give rise to signals which appear to be oil but are not. Suggestions for radar image as an operational tool has also been made. However, due to the inherent risk in these tools, this paper presents the possible research directions of combining statistical techniques with remote sensing in marine oil spill detection and estimation.

Marine Oil Spill Detection in SAR Image Based on Mathematical Morphology

Oil spills are almost constantly present in recent years, which cause ecological disaster of marine environment. It is important to detect the area of oil spill to take rescue measurement in time. The paper took the EnviSat Asar image as example to detect the boundary of oil spills. Firstly, the paper removed speckle noise by Histogram Equalization. Then, image segmentation was carried out by open-close mixed operation in mathematical morphology. At last, the region of oil spill was exacted independently.

Dark-spot detection from SAR intensity imagery with spatial density thresholding for oil-spill monitoring

Dark-spot detection is a critical and fundamental step in marine oil-spill detection and monitoring. In this paper, a novel approach for automated dark-spot detection using synthetic aperture radar (SAR) intensity imagery is presented. The key to the approach is making use of a spatial density feature to differentiate between dark spots and the background. A detection window is passed through the entire SAR image. First, intensity threshold segmentation is applied to each window. Pixels with intensities below the threshold are regarded as potential dark-spot pixels while the others are potential background pixels. Second, the density of potential background pixels is estimated using kernel density estimation within each window. Pixels with densities below a certain threshold are the real dark-spot pixels. Third, an area threshold and a contrast threshold are used to eliminate any remaining false targets. In the last step, the individual detection results are mosaicked to produce the final result. The proposed approach was tested on 60 RADARSAT-1 ScanSAR intensity images which contain verified oil-spill anomalies. The same parameters were used in all tests. For the overall dataset, the average of commission error, omission error, and average difference were 7.0%, 6.1%, and 0.4 pixels, respectively. The average number of false alarms was 0.5 per unit image and the average computational time for a detection window was 1.2 s using a PC-based MATLAB platform. Our experimental results demonstrate that the proposed approach is fast, robust and effective.

Marine oil spill detection, statistics and mapping with ERS SAR imagery in south-east Asia

With a large coverage, short repeativity, day and night, and all- weather capability, ERS SAR imagery has been proven to be a useful tool for ocean oil spill monitoring. The paper discusses ocean oil pollution detection, statistics and mapping in south-east Asian waters by using ERS-1/2 SAR data. The work is roughly divided into four main steps. Possible oil slicks are first identified from each image. Next, the scene in which most slicks have been detected within a frame is selected for re-examination and all oil slicks detected on this image are further classified into either high, medium or low probability categories. The overall statistical results are then complied and values of normalized oil slick occurrence intensity for various scenarios computed for each ERS frame. Finally, spatial oil spill distribution maps in the south-east Asian waters are derived. The results show very good agreement with shipping routes as well as in situ data in the region.