Automatic detection of oil spills from SAR images

Nirchio, F.; Sorgente, M.; Giancaspro, A.; Biamino, W.; Parisato, E.; Ravera, R.; Trivero, P.

doi:10.1080/01431160512331326558

Cited by 89 publications

(45 citation statements)

References 16 publications

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“…Besides, manual detection is constrained by the knowledge and experience of the human operator, whose results are subjective. A trend to develop fast, reliable, and automated oil spill detection systems, has therefore arisen recently (Nirchio et al, 2005;Karathanassi et al, 2006;Keramitsoglou et al, 2006;Solberg et al, 2007;Topouzelis et al, 2007).…”

Section: Automated Detection Of Oil Spills From Sar Intensity Imagerymentioning

confidence: 99%

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

Shu

Yousif

et al. 2010

Remote Sensing of Environment

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Since the 1980s, satellite-borne synthetic aperture radar (SAR) has been investigated for early warning and monitoring of marine oil spills to permit effective satellite surveillance in the marine environment.Automated detection of oil spills from satellite SAR intensity imagery consists of three steps: 1) Detection of dark spots; 2) Extraction of features from the detected dark spots; and 3) Classification of the dark spots into oil spills and look-alikes. However, marine oil spill detection is a very difficult and challenging task. Open questions exist in each of the three stages.In this thesis, the focus is on the first stage-dark spot detection. An efficient and effective dark spot detection method is critical and fundamental for developing an automated oil spill detection system. A novel method for this task is presented. The key to the method is utilizing the spatial density feature to enhance the separability of dark spots and the background. After an adaptive intensity thresholding, a spatial density thresholding is further

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Section: Automated Detection Of Oil Spills From Sar Intensity Imagerymentioning

confidence: 99%

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

Shu

Yousif

et al. 2010

Remote Sensing of Environment

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“…Here we describe a procedure, named "Oil Spill Automatic Detector" (OSAD), able to distinguish oil spills from other similar sea surface features (look-alike) in SAR images using a statistical approach; during last years, the procedure has been updated (Fiscella et al 2000, Nirchio et al 2002, Nirchio et. al 2005a, Nirchio et al 2005b.…”

Section: Statistical Techniquesmentioning

confidence: 99%

“…al 2005a, Nirchio et al 2005b. It considers both the radiometric and the geometric characteristics of the areas being tested.…”

Section: Statistical Techniquesmentioning

confidence: 99%

Observing Marine Pollution with Synthetic Aperture Radar

Trivero¹,

Biamino²

2010

Geoscience and Remote Sensing New Achievements

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“…In addition, the accuracy and precision of detection depend on the skills of the operator (Shu et al 2010). Recently, many researchers have been interested in improving and developing new automated monitoring oil spill methods (Solberg et al 1999), (Nirchio et al 2005), (Solberg et al 2003) and (Marghany 2014).…”

Section: Introductionmentioning

confidence: 99%

“…Theresholding method can be divided into two general ones: 1) global thresholding and 2) adaptive thresholding. 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).…”

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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Automatic detection of oil spills from SAR images

Cited by 89 publications

References 16 publications

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

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

Observing Marine Pollution with Synthetic Aperture Radar

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

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