Oil Spill Detection in Radarsat and Envisat SAR Images

Solberg, Anne; Brekke, Camilla; Husoy, P.O.

doi:10.1109/tgrs.2006.887019

Cited by 261 publications

(135 citation statements)

References 12 publications

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“…This phenomenon gives rise to look-alikes which may include biogenic films, areas of low wind (<3 m/s), windshadow near the coastal regions, rain cells, upwelling, internal waves, and oceanic or atmospheric fronts. The semiautomatic and automatic detection algorithms based on neural networks, multi-scale image segmentation and fuzzy logic have been developed to detect oil slicks with SAR data [3][4][5][6][7]. In this paper, we detected an oil slick in the Bohai Sea using ENVI and Nest4A software.…”

mentioning

confidence: 99%

Characteristics of the Bohai Sea oil spill and its impact on the Bohai Sea ecosystem

2012

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confidence: 99%

Characteristics of the Bohai Sea oil spill and its impact on the Bohai Sea ecosystem

2012

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“…However, SAR images must be processed carefully since the dark areas might occur because of some natural phenomena without oil like smooth water (low wind areas), organic films, wind front areas, areas sheltered by land, rain cells, grease ice, internal waves and shallow bathymetric features (Sabins, 1997;Alpers et al, 1991;Hovland et al, 1994). The procedure steps of oil spill detection in SAR data can be generalized as segmentation (dark object extraction), feature extraction and classification (determination oil) stages (Pavlakis et al, 2001;Brekke and Solberg, 2005;Solberg et al, 2007;Shi et al, 2008;Topouzelis et al, 2009). …”

Section: Methodsmentioning

confidence: 99%

“…Today, remote sensing technology is being used for this purpose over the past decade. Synthetic Aperture Radar (SAR) satellites are often preferred to optical sensors due to the all weather and all day capabilities and being used to detect the oil spills discharged into the sea with sufficient accuracies (Solberg et al, 2007). Oil-Spill detection procedures in SAR data generally comprise segmentation, feature extraction and classification stages (Solberg et al, 2007;Brekke and Solberg, 2005).…”

Section: Introductionmentioning

confidence: 99%

“…The backscatter energy level for oil-spilled areas received in SAR systems is too low since the oil dampens the capillary waves of the sea surface. However, there is a serious problem that the other natural phenomenas also dampen the short waves and create dark areas on the sea surface due to suspension of Bragg scattering mechanism depending to ocean and/or atmospheric conditions, (Solberg et al 2007). Thus, the dark image regions of which the probabilities of being either oil spill or alike are high must be segmented.…”

Section: Introductionmentioning

confidence: 99%

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Testing the Generalization Efficiency of Oil Slick Classification Algorithm Using Multiple Sar Data for Deepwater Horizon Oil Spill

Özkan

Osmanoǧlu

Sunar

et al. 2012

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Marine oil spills due to releases of crude oil from tankers, offshore platforms, drilling rigs and wells, etc. are seriously affecting the fragile marine and coastal ecosystem and cause political and environmental concern. A catastrophic explosion and subsequent fire in the Deepwater Horizon oil platform caused the platform to burn and sink, and oil leaked continuously between April 20th and July 15th of 2010, releasing about 780,000 m3 of crude oil into the Gulf of Mexico. Today, space-borne SAR sensors are extensively used for the detection of oil spills in the marine environment, as they are independent from sun light, not affected by cloudiness, and more cost-effective than air patrolling due to covering large areas. In this study, generalization extent of an object based classification algorithm was tested for oil spill detection using multiple SAR imagery data. Among many geometrical, physical and textural features, some more distinctive ones were selected to distinguish oil and look alike objects from each others. The tested classifier was constructed from a Multilayer Perception Artificial Neural Network trained by ABC, LM and BP optimization algorithms. The training data to train the classifier were constituted from SAR data consisting of oil spill originated from Lebanon in 2007. The classifier was then applied to the Deepwater Horizon oil spill data in the Gulf of Mexico on RADARSAT-2 and ALOS PALSAR images to demonstrate the generalization efficiency of oil slick classification algorithm.

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“…In global approach, a universal threshold is used for the entire image (Nirchio et al 2005) and (Chang et al 2008). In adaptive method, the threshold is selected locally (Solberg et al 2007). In addition, recently other methods same as neural network , wavelet based methods (Kuzmanić and Vujović 2010), classification based methods like support vector machines (Mercier and Girard-Ardhuin 2005) and so on have been introduced.…”

Section: Introductionmentioning

confidence: 99%

Dark Spot Detection Using Intensity and the Degree of Polarization in Fully Polarimetric Sar Images for Oil Polution Monitoring

Zakeri¹,

Amini²

2015

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT: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.

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Oil Spill Detection in Radarsat and Envisat SAR Images

Cited by 261 publications

References 12 publications

Characteristics of the Bohai Sea oil spill and its impact on the Bohai Sea ecosystem

Characteristics of the Bohai Sea oil spill and its impact on the Bohai Sea ecosystem

Testing the Generalization Efficiency of Oil Slick Classification Algorithm Using Multiple Sar Data for Deepwater Horizon Oil Spill

Dark Spot Detection Using Intensity and the Degree of Polarization in Fully Polarimetric Sar Images for Oil Polution Monitoring

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