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 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 SEA OIL SPILL DETECTION USING SELF-SIMILARITY PARAMETER OF POLARIMETRIC SAR DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 1661-1666 ◽  
Author(s):  
S. Tong ◽  
Q. Chen ◽  
X. Liu
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Sea Surface ◽  
Synthetic Aperture ◽  
Scattering Mechanism ◽  
Self Similarity ◽  
Similarity Parameter ◽  
Polarimetric Synthetic Aperture Radar ◽  
Oil Spill Detection

The ocean oil spills cause serious damage to the marine ecosystem. Polarimetric Synthetic Aperture Radar (SAR) is an important mean for oil spill detections on sea surface. The major challenge is how to distinguish oil slicks from look-alikes effectively. In this paper, a new parameter called self-similarity parameter, which is sensitive to the scattering mechanism of oil slicks, is introduced to identify oil slicks and reduce false alarm caused by look-alikes. Self-similarity parameter is small in oil slicks region and it is large in sea region or look-alikes region. So, this parameter can be used to detect oil slicks from look-alikes and water. In addition, evaluations and comparisons were conducted with one Radarsat-2 image and two SIR-C images. The experimental results demonstrate the effectiveness of the self-similarity parameter for oil spill detection.

scholarly journals Multi-objective entropy evolutionary algorithm for marine oil spill detection using cosmo-skymed satellite data

2015 ◽  
Vol 12 (3) ◽  
pp. 1263-1289 ◽  
Author(s):  
M. Marghany
Keyword(s):  
Evolutionary Algorithm ◽  
Oil Spill ◽  
Satellite Data ◽  
Marine Mammals ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Design Tool ◽  
Multi Objective ◽  
Oil Spill Detection ◽  
Sar Data

Abstract. Oil spill pollution has a substantial role in damaging the marine ecosystem. Oil spill that floats on top of water, as well as decreasing the fauna populations, affects the food chain in the ecosystem. In fact, oil spill is reducing the sunlight penetrates the water, limiting the photosynthesis of marine plants and phytoplankton. Moreover, marine mammals for instance, disclosed to oil spills their insulating capacities are reduced, and so making them more vulnerable to temperature variations and much less buoyant in the seawater. This study has demonstrated a design tool for oil spill detection in SAR satellite data using optimization of Entropy based Multi-Objective Evolutionary Algorithm (E-MMGA) which based on Pareto optimal solutions. The study also shows that optimization entropy based Multi-Objective Evolutionary Algorithm provides an accurate pattern of oil slick in SAR data. This shown by 85 % for oil spill, 10 % look-alike and 5 % for sea roughness using the receiver-operational characteristics (ROC) curve. The E-MMGA also shows excellent performance in SAR data. In conclusion, E-MMGA can be used as optimization for entropy to perform an automatic detection of oil spill in SAR satellite data.

ECOLOGICAL AND GEOMORPHOLOGICAL ASSESSMENT OF THE VULNERABILITY OF THE COASTS OF THE KARA SEA TO THE OIL SPILL

2017 ◽  
Author(s):  
Alexander Ermolov ◽  
Alexander Ermolov
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
International System ◽  
Kara Sea ◽  
Coastal Protection ◽  
Sensitivity Index ◽  
Oil Spill Response ◽  
Expert Assessments ◽  
International Systems

International experience of oil spill response in the sea defines the priority of coastal protection and the need to identify as most valuable in ecological terms and the most vulnerable areas. Methodological approaches to the assessing the vulnerability of Arctic coasts to oil spills based on international systems of Environmental Sensitivity Index (ESI) and geomorphological zoning are considered in the article. The comprehensive environmental and geomorphological approach allowed us to form the morphodynamic basis for the classification of seacoasts and try to adapt the international system of indexes to the shores of the Kara Sea taking into account the specific natural conditions. This work has improved the expert assessments of the vulnerability and resilience of the seacoasts.

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 Sacrificial amphiphiles: Eco-friendly chemical herders as oil spill mitigation chemicals

2015 ◽  
Vol 1 (5) ◽  
pp. e1400265 ◽  
Author(s):  
Deeksha Gupta ◽  
Bivas Sarker ◽  
Keith Thadikaran ◽  
Vijay John ◽  
Charles Maldarelli ◽  
...  
Keyword(s):  
Crude Oil ◽  
Oil Spill ◽  
Oil Spills ◽  
Cold Water ◽  
Marine Ecosystem ◽  
Hot Water ◽  
Sea Surface ◽  
Marine Biota ◽  
Polar Group ◽  
Branched Chain

Crude oil spills are a major threat to marine biota and the environment. When light crude oil spills on water, it forms a thin layer that is difficult to clean by any methods of oil spill response. Under these circumstances, a special type of amphiphile termed as “chemical herder” is sprayed onto the water surrounding the spilled oil. The amphiphile forms a monomolecular layer on the water surface, reducing the air–sea surface tension and causing the oil slick to retract into a thick mass that can be burnt in situ. The current best-known chemical herders are chemically stable and nonbiodegradable, and hence remain in the marine ecosystem for years. We architect an eco-friendly, sacrificial, and effective green herder derived from the plant-based small-molecule phytol, which is abundant in the marine environment, as an alternative to the current chemical herders. Phytol consists of a regularly branched chain of isoprene units that form the hydrophobe of the amphiphile; the chain is esterified to cationic groups to form the polar group. The ester linkage is proximal to an allyl bond in phytol, which facilitates the hydrolysis of the amphiphile after adsorption to the sea surface into the phytol hydrophobic tail, which along with the unhydrolyzed herder, remains on the surface to maintain herding action, and the cationic group, which dissolves into the water column. Eventual degradation of the phytol tail and dilution of the cation make these sacrificial amphiphiles eco-friendly. The herding behavior of phytol-based amphiphiles is evaluated as a function of time, temperature, and water salinity to examine their versatility under different conditions, ranging from ice-cold water to hot water. The green chemical herder retracted oil slicks by up to ~500, 700, and 2500% at 5°, 20°, and 35°C, respectively, during the first 10 min of the experiment, which is on a par with the current best chemical herders in practice.

ANALISA PENCEMARAN LAUT OLEH PTT EXPLORATION AND PRODUCTION (PTTEP) AUSTRALASIA TERHADAP LAUT TIMOR INDONESIA

2020 ◽  
Vol 4 (2) ◽  
pp. 127
Author(s):  
Paulin Yosephin Marini ◽  
Sherlly Monica Bonsapia ◽  
Johni R.V. Korwa
Keyword(s):  
Oil Spill ◽  
Oil And Gas ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Oil Field ◽  
Australian Government ◽  
Compensation Mechanisms ◽  
Exploration And Production ◽  
Data Collection Technique ◽  
The Impact

<p><em>This study aims to analyze a blowout from an oil and gas leak owned by PTT Exploration and Production (PTTEP) Australasia in the Montara oil field in the Indonesian Timor Sea, and how to resolve disputes between Australia and Indonesia. A qualitative approach was used in this study, whilst the data collection technique was through library research. The theory of state responsibility, the concept of human security, and the concept of international maritime law are used to analyze disputes between Indonesia and Australia. The study found that the Montara oil spill had not only damaged the marine ecosystem but also polluted Indonesian waters. It also found that although the Australian government had formed a special commission to resolve cases and even used dispersant, it had not satisfied all parties. Several points are summarized. First, the Montara oil spill in Australia is a transnational study because the impact has crossed national borders. Secondly, UNCLOS has a weakness in the settlement of the Montara case because the Convention only provides a description related to ‘Responsibility of Each Country’ and does not specifically arrange material compensation mechanisms to countries that cause sea pollution. Third, the Montara oil spill has caused huge losses for Indonesian seaweed farmers, especially 13 districts in NTT. The recommendations are that the Indonesian government along with the Montara Victim Peoples’ Advocacy Team should continue to follow up the case of oil spills from the Montara platform and continue to fight for compensation to the Australian government and the PTTEP as the responsible party.</em></p>

scholarly journals MODELING THE WIND INFLUENCE IN AN OIL SPILL ALONG THE SOUTHERN BRAZILIAN SHELF

2012 ◽  
Vol 11 (1-2) ◽  
pp. 100
Author(s):  
C. E. Stringari ◽  
W. C. Marques ◽  
L. F. Mello ◽  
R. T. Edit
Keyword(s):  
Oil Spill ◽  
Oil Spills ◽  
Marine Ecosystem ◽  
Computational Cost ◽  
Temperature Fields ◽  
Local Wind ◽  
Wind Influence ◽  
Oil Density ◽  
Environmental Agencies ◽  
Spilled Oil

Oil spills can generate different effects in different time scales on the marine ecosystem. The numerical modeling of this process is an important tool with low computational cost which provides a powerful appliance to environmental agencies regarding the risk management. In this way, the objective of this work is evaluate the local wind influence in a hypothetical oil spill along the Southern Brazilian shelf. The numerical simulation was carried using the ECOS model (Easy Coupling Oil System), an oil spill model developed at the Universidade Federal do Rio Grande – FURG, coupled with the tridimensional hydrodynamical model TELEMAC3D (EDF, France). The hydrodynamic model provides the velocities, salinity and temperature fields used by the oil spill model to evaluate the behavior and fate of the oil. The results suggest that the local wind influence are the main forcing driven the fate of the spilled oil. The direction and intensity of the currents are important controlling the behavior and the tridimensional transportation of the oil, on the other hand, the turbulent diffusion is important for the horizontal drift of the oil. The weathering results indicate 40% of evaporation and 80% of emulsification, and the combination of these processes leads an increasing of the oil density around 53.4 kg/m³ after 5 days of simulation.

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