scholarly journals DARK SPOT DETECTION USING INTENSITY AND THE DEGREE OF POLARIZATION IN FULLY POLARIMETRIC SAR IMAGES FOR OIL POLUTION MONITORING

2015 ◽  
Vol XL-1-W5 ◽  
pp. 749-753
Author(s):  
F. Zakeri ◽  
J. Amini
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Degree Of Polarization ◽  
Dark Spot ◽  
Sar Images ◽  
Simple Method ◽  
The Past ◽  
Spot Detection ◽  
Otsu Method ◽  
Image Pixels

Oil spill surveillance is of great environmental and economical interest, directly contributing to improve environmental protection. Monitoring of oil spills using synthetic aperture radar (SAR) has received a considerable attention over the past few years, notably because of SAR data abilities like all-weather and day-and-night capturing. The degree of polarization (DoP) is a less computationally complex quantity characterizing a partially polarized electromagnetic field. The key to the proposed approach is making use of DoP as polarimetric information besides intensity ones to improve dark patches detection as the first step of oil spill monitoring. In the proposed approach first simple intensity threshold segmentation like Otsu method is applied to the image. Pixels with intensities below the threshold are regarded as potential dark spot pixels while the others are potential background pixels. Second, the DoP of potential dark spot pixels is estimated. Pixels with DoP below a certain threshold are the real dark-spot pixels. Choosing the threshold is a critical and challenging step. In order to solve choosing the appropriate threshold, we introduce a novel but simple method based on DoP of potential dark spot pixels. Finally, an area threshold is used to eliminate any remaining false targets. The proposed approach is tested on L band NASA/JPL UAVSAR data, covering the Deepwater Horizon oil spill in the Gulf of Mexico. Comparing the obtained results from the new method with conventional approaches like Otsu, K-means and GrowCut shows better achievement of the proposed algorithm. For instance, mean square error (MSE) 65%, Overall Accuracy 20% and correlation 40% are improved.

scholarly journals OILSPILL AND LOOK-ALIKE SPOTS FROM SAR IMAGERY USING OTSU METHOD AND ARTIFICIAL NEURAL NETWORK

2020 ◽  
Vol 4 (11) ◽  
pp. 1-10
Author(s):  
M. Sornam
Keyword(s):  
Image Segmentation ◽  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Oil Spill Detection ◽  
Otsu Method ◽  
Artificial Neural ◽  
Propagation Network

Oil spill pollution plays a significant role in damaging marine ecosystem. Discharge of oil due to tanker accidents has the most dangerous effects on marine environment. The main waste source is the ship based operational discharges. Synthetic Aperture Radar (SAR) can be effectively used for the detection and classification of oil spills. Oil spills appear as dark spots in SAR images. One major advantage of SAR is that it can generate imagery under all weather conditions. However, similar dark spots may arise from a range of unrelated meteorological and oceanographic phenomena, resulting in misidentification. A major focus of research in this area is the development of algorithms to distinguish ‘oil spills’ from ‘look-alikes’. The features of detected dark spot are then extracted and classified to discriminate oil spills from look-alikes. This paper describes the development of a new approach to SAR oil spill detection using Segmentation method and Artificial Neural Networks (ANN). A SAR-based oil-spill detection process consists of three stages: image segmentation, feature extraction and object recognition (classification) of the segmented objects as oil spills or look-alikes. The image segmentation was performed with Otsu method. Classification has been done using Back Propagation Network and this network classifies objects into oil spills or look-alikes according to their feature parameters. Improved results have been achieved for the discrimination of oil spills and look-alikes.

scholarly journals Dark Spot Detection in SAR Images of Oil Spill Using Segnet

2018 ◽  
Vol 8 (12) ◽  
pp. 2670 ◽  
Author(s):  
Hao Guo ◽  
Guo Wei ◽  
Jubai An
Keyword(s):  
Oil Spill ◽  
Semantic Segmentation ◽  
Dark Spot ◽  
Sar Images ◽  
Data Set ◽  
High Noise ◽  
Oil Spill Detection ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Spot Detection

Damping Bragg scattering from the ocean surface is the basic underlying principle of synthetic aperture radar (SAR) oil slick detection, and they produce dark spots on SAR images. Dark spot detection is the first step in oil spill detection, which affects the accuracy of oil spill detection. However, some natural phenomena (such as waves, ocean currents, and low wind belts, as well as human factors) may change the backscatter intensity on the surface of the sea, resulting in uneven intensity, high noise, and blurred boundaries of oil slicks or lookalikes. In this paper, Segnet is used as a semantic segmentation model to detect dark spots in oil spill areas. The proposed method is applied to a data set of 4200 from five original SAR images of an oil spill. The effectiveness of the method is demonstrated through the comparison with fully convolutional networks (FCN), an initiator of semantic segmentation models, and some other segmentation methods. It is here observed that the proposed method can not only accurately identify the dark spots in SAR images, but also show a higher robustness under high noise and fuzzy boundary conditions.

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

2010 ◽  
Vol 114 (9) ◽  
pp. 2026-2035 ◽  
Author(s):  
Yuanming Shu ◽  
Jonathan Li ◽  
Hamad Yousif ◽  
Gary Gomes
Keyword(s):  
Oil Spill ◽  
Dark Spot ◽  
Spatial Density ◽  
Spot Detection

scholarly journals Dark spot detection for characterization of marine surface slicks using UAVSAR quad-pol data

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vaishali Chaudhary ◽  
Shashi Kumar
Keyword(s):  
Oil Spills ◽  
Marine Ecosystem ◽  
Dark Spot ◽  
Accurate Estimation ◽  
Support Vector ◽  
Potential Hazard ◽  
Significant Drop ◽  
Oil Slick ◽  
Spot Detection ◽  
Marine Surface

AbstractOil spills are a potential hazard, causing the deaths of millions of aquatic animals and this leaves a calamitous effect on the marine ecosystem. This research focuses on evaluating the potential of polarimetric parameters in discriminating the oil slick from water and also possible thicker/thinner zones within the slick. For this purpose, L-band UAVSAR quad-pol data of the Gulf of Mexico region is exploited. A total number of 19 polarimetric parameters are examined to study their behavior and ability in distinguishing oil slick from water and its own less or more oil accumulated zones. The simulation of compact-pol data from UAVSAR quad-pol data is carried out which has shown good performance in detection and discrimination of oil slick from water. To know the extent of separation between oil and water classes, a statistical separability analysis is carried out. The outcomes of each polarimetric parameter from separability analysis are then quantified with the radial basis function (RBF) supervised Support Vector Machine classifier followed with an accurate estimation of the results. Moreover, a comparison of the achieved and estimated accuracy has shown a significant drop in accuracy values. It has been observed that the highest accuracy is given by LHV compact-pol decomposition and coherency matrix with a classification accuracy of ~ 94.09% and ~ 94.60%, respectively. The proposed methodology has performed well in discriminating the oil slick by utilizing UAVSAR dataset for both quad-pol and compact-pol simulation.

scholarly journals SAR Oil Spill Detection System through Random Forest Classifiers

2021 ◽  
Vol 13 (11) ◽  
pp. 2044
Author(s):  
Marcos R. A. Conceição ◽  
Luis F. F. Mendonça ◽  
Carlos A. D. Lentini ◽  
André T. C. Lima ◽  
José M. Lopes ◽  
...  
Keyword(s):  
Random Forest ◽  
Oil Spill ◽  
Oil Spills ◽  
Detection System ◽  
Feature Space ◽  
Dark Spot ◽  
Sar Image ◽  
Extensive Search ◽  
Gradient Based ◽  
Biological Films

A set of open-source routines capable of identifying possible oil-like spills based on two random forest classifiers were developed and tested with a Sentinel-1 SAR image dataset. The first random forest model is an ocean SAR image classifier where the labeling inputs were oil spills, biological films, rain cells, low wind regions, clean sea surface, ships, and terrain. The second one was a SAR image oil detector named “Radar Image Oil Spill Seeker (RIOSS)”, which classified oil-like targets. An optimized feature space to serve as input to such classification models, both in terms of variance and computational efficiency, was developed. It involved an extensive search from 42 image attribute definitions based on their correlations and classifier-based importance estimative. This number included statistics, shape, fractal geometry, texture, and gradient-based attributes. Mixed adaptive thresholding was performed to calculate some of the features studied, returning consistent dark spot segmentation results. The selected attributes were also related to the imaged phenomena’s physical aspects. This process helped us apply the attributes to a random forest, increasing our algorithm’s accuracy up to 90% and its ability to generate even more reliable results.

scholarly journals Revisit of a Case Study of Spilled Oil Slicks Caused by the Sanchi Accident (2018) in the East China Sea

2021 ◽  
Vol 9 (3) ◽  
pp. 279
Author(s):  
Zhehao Yang ◽  
Weizeng Shao ◽  
Yuyi Hu ◽  
Qiyan Ji ◽  
Huan Li ◽  
...  
Keyword(s):  
East China Sea ◽  
Oil Spill ◽  
Oil Spills ◽  
Ocean Model ◽  
Sea Surface ◽  
East China ◽  
The East China Sea ◽  
Sar Images ◽  
China Sea ◽  
Spatial Coverage

Marine oil spills occur suddenly and pose a serious threat to ecosystems in coastal waters. Oil spills continuously affect the ocean environment for years. In this study, the oil spill caused by the accident of the Sanchi ship (2018) in the East China Sea was hindcast simulated using the oil particle-tracing method. Sea-surface winds from the European Centre for Medium-Range Weather Forecasts (ECMWF), currents simulated from the Finite-Volume Community Ocean Model (FVCOM), and waves simulated from the Simulating WAves Nearshore (SWAN) were employed as background marine dynamics fields. In particular, the oil spill simulation was compared with the detection from Chinese Gaofen-3 (GF-3) synthetic aperture radar (SAR) images. The validation of the SWAN-simulated significant wave height (SWH) against measurements from the Jason-2 altimeter showed a 0.58 m root mean square error (RMSE) with a 0.93 correlation (COR). Further, the sea-surface current was compared with that from the National Centers for Environmental Prediction (NCEP) Climate Forecast System Version 2 (CFSv2), yielding a 0.08 m/s RMSE and a 0.71 COR. Under these circumstances, we think the model-simulated sea-surface currents and waves are reliable for this work. A hindcast simulation of the tracks of oil slicks spilled from the Sanchi shipwreck was conducted during the period of 14–17 January 2018. It was found that the general track of the simulated oil slicks was consistent with the observations from the collected GF-3 SAR images. However, the details from the GF-3 SAR images were more obvious. The spatial coverage of oil slicks between the SAR-detected and simulated results was about 1 km2. In summary, we conclude that combining numerical simulation and SAR remote sensing is a promising technique for real-time oil spill monitoring and the prediction of oil spreading.

scholarly journals OIL SPILL LIABILITY AND COMPENSATION (“SUPERFUND”)

1977 ◽  
Vol 1977 (1) ◽  
pp. 15-18 ◽  
Author(s):  
James J. Reynolds
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Compensation System ◽  
The Past ◽  
The Status ◽  
Substantial Progress ◽  
Adequate Coverage ◽  
Legislative Changes ◽  
Double Coverage ◽  
Compensation Law

ABSTRACT The subjects under consideration are the liability imposed upon shippers, producers, refiners, and other handlers of oil, and the compensation monies available to persons damaged from oil spills. The liability and compensation system in existence today is one that provides little or no coverage in some instances, adequate coverage in some instances, and double coverage in still other instances. It has been correctly described as a “patchwork.” In the past three years, concerted efforts have been made by industry, government, and environmentalists to legislate improvements to the system. An attempt to enact a comprehensive oil spill liability and compensation law made substantial progress in the last Congress. This paper reviews the system as it now exists, the problems caused by the existing system, the proposed legislative changes, and the status of the legislation today.

Analysis on Adaptability of Monitoring Methods for Oil Spill by Using SAR Imagery

2014 ◽  
Vol 1030-1032 ◽  
pp. 1653-1656
Author(s):  
Qi Yao ◽  
Zhan Qiang Chang ◽  
Xiao Meng Liu ◽  
Li Yan Zhang ◽  
Chao Zhao
Keyword(s):  
Markov Chain ◽  
Oil Spill ◽  
Basic Principle ◽  
Ocean Wave ◽  
Ecological Environment ◽  
Monitoring Methods ◽  
Sar Images ◽  
Markov Chain Method ◽  
Otsu Method ◽  
Sar Imagery

As is well known, the ocean plays a key role in global ecological environment. In this paper, we introduced the basic principle of monitoring oil spill by using SAR images. On the basis of that, we systematically analyzed the applicability of various methods for monitoring oil spill by using SAR images. The conclusion shows that the ANN method and the OTSU method have the advantages of timeliness and efficiency in oil spill monitoring, while the Markov Chain method cost more time due to its capability in reducing the effect of internal ocean wave.

Task Force on Oil Spill Preparedness: A Canadian On-Shore Evaluation

1991 ◽  
Vol 1991 (1) ◽  
pp. 15-17 ◽  
Author(s):  
Wayne O. Wiebe ◽  
Paul Wotherspoon
Keyword(s):  
Oil Spill ◽  
Public Interest ◽  
Oil Spills ◽  
Task Force ◽  
Oil Industry ◽  
Regulatory Agencies ◽  
The Past ◽  
Oil Pipelines ◽  
Overall Evaluation ◽  
Offshore Activities

ABSTRACT The oil industry's ability to effectively contain and clean up oil spills has been questioned over the years, and recent events have heightened this concern. Growing public interest and efforts by the upstream oil industry in Canada to assess its operations resulted in formation of the Task Force on Oil Spill Preparedness. The study was sponsored by the Canadian Petroleum Association and the Independent Petroleum Association of Canada, which represent most companies in the upstream industry. The overall evaluation concentrates on both onshore and offshore activities, but this paper discusses only the onshore segment. In the past 40 years the industry has made substantial efforts to prevent oil spills. As a result, Canada has experienced no catastrophic oil spills in operating about 40,000 producing wells and 37,000 km of oil pipelines. In spite of these efforts, the industry believes there is room for improvement. The study recommends allocating more resources to improving equipment, training on-site personnel, establishing better communications within companies and between companies and regulatory agencies, and continuing research in oil spill countermeasures. These recommendations are being incorporated in the existing framework to improve the response capability of the upstream oil industry.

25 years of collaboration: the Pacific States/British Columbia Oil Spill Task Force 1989–2014

2014 ◽  
Vol 2014 (1) ◽  
pp. 50-62
Author(s):  
Sarah Brace
Keyword(s):  
British Columbia ◽  
Oil Spill ◽  
West Coast ◽  
Oil Spills ◽  
Task Force ◽  
Mutual Aid ◽  
Regulatory Oversight ◽  
Task Force Member ◽  
The Past ◽  
The Pacific

ABSTRACT Two significant west coast spill incidents, the barge Nestucca spill in B.C. in 1988 and the tanker Exxon Valdez spill of 1989 catalyzed the formal creation of the Pacific States/British Columbia Oil Spill Task Force, a union of Alaska, California, Oregon, Washington and British Columbia. Hawaii joined 12 years later and for the past 25 years the Task Force member organizations have collaborated on numerous projects and policy initiatives that have significantly influenced how the west coast prevents, prepares for and responds to oil spills. This paper will: 1) Provide an overview of how the Task Force functions and how it fosters collaboration between industry, agencies, and other stakeholders in the region; 2) Highlight key projects and accomplishments from the past two decades, including Transboundary coordination, vessel traffic risk studies, mutual aid agreements, and federal regulatory oversight; and how these projects were initiated and carried out; 3) Offer examples of how the Task Force is looking at challenges ahead, such as the shifting landscape of energy transportation and emerging fuels in the region, and what this means for spill prevention and response.

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