On the co‐polarized phase difference for oil spill observation

Migliaccio, M.; Nunziata, Ferdinando; Gambardella, A.

doi:10.1080/01431160802520741

Cited by 160 publications

(131 citation statements)

References 21 publications

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“…It is not necessary for every single Pol-SAR feature to be used in the classification process. The commonly used fully Pol-SAR features for oil spill detection include span [8], ρ [7], H [1], A [25], α [25], µ [15], and P [22]. Each of these features has its own different ability for identifying the ocean oil spills from water.…”

Section: Selection Criteria Of Fully Pol-sar Features Based On J-m DImentioning

confidence: 99%

“…The combined usage of the multiple fully Pol-SAR feature images are necessary to help improve the classification performance of oil spills and water. As a general view, the chosen fully Pol-SAR features are effective for oil spill [8]; ρ: co-polarized complex correlation [7]; H: polarization entropy [1]; A: anisotropy coefficient [25]; α: mean scattering angle [25]; µ: conformity coefficient [15]; P: degree of polarization [22]. …”

Section: Selection Criteria Of Fully Pol-sar Features Based On J-m DImentioning

confidence: 99%

“…They bring damage to coastal environment and marine ecosystems [1][2][3][4]. Satellite remote sensing technology has been widely used in oil spill observations due to its frequent large coverage and relatively low cost [5][6][7][8][9][10][11]. Among remote sensing sensors, synthetic aperture radar (SAR) can provide valuable synoptic information about the position and size of a particular oil spill under moderate wind speed (4-12 m/s) weather conditions, day and night [12].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

Song

Ding

et al. 2017

Remote Sensing

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Oil spill accidents from ship or oil platform cause damage to marine and coastal environment and ecosystems. To monitor such spill events from space, fully polarimetric (Pol-SAR) synthetic aperture radar (SAR) has been greatly used in improving oil spill observation. Aiming to promote ocean oil spill classification accuracy, we developed a new oil spill identification method by combining multiple fully polarimetric SAR features data with an optimized wavelet neural network classifier (WNN). Two sets of RADARSAT-2 fully polarimetric SAR data are applied to test the validity of the developed method. The experimental results show that: (1) the convergence ability of optimized WNN can be enhanced, improving overall classification accuracy of ocean oil spill, in comparison to the classification results based on a common un-optimized WNN classifier; and (2) the joint use of the multiple fully Pol-SAR features as the inputs of the classifier can achieve better classification result than that only with single fully Pol-SAR feature. The developed method can improve classification accuracy by 4.96% and 7.75%, compared with the classification results with un-optimized WNN and only with one single fully polarimetric SAR feature. The classification overall accuracy based on the proposed approach can reach 97.67%. Experimental results have proven that the proposed approach is effective and applicable to classify the ocean oil spill.

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Section: Selection Criteria Of Fully Pol-sar Features Based On J-m DImentioning

confidence: 99%

Section: Selection Criteria Of Fully Pol-sar Features Based On J-m DImentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

Song

Ding

et al. 2017

Remote Sensing

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“…Schuler et al [2004b] mapped an ocean eddy covered by biogenic slick fields using these polarimetric parameters. Migliaccio et al [2009] detected oil spills with the statistical distribution of hhvv . Li et al [2008] analyzed the variation of the polarimetric characteristics ( hhvv , H, A, a) of ocean surface processes with incidence angles and radar wave number, using C-, L-and P-band, fully polarimetric SIR-C/X-SAR data.…”

Section: Introductionmentioning

confidence: 99%

“…However, previous studies of such are limited in that they are based on single polarization data and focus on the variations in the intensities of radar returns. Thus, until now, development of an understanding of the SAR imaging of convective processes was hampered by the existence of the single image patterns resulting from many possible different atmospheric and oceanic phenomena, such as the sea surface signature of rain [Atlas, 1994], upper/surface ocean convection [Romeiser et al, 2004], and surfactants [Migliaccio et al, 2009]. It is necessary to distinguish the various phenomena from one another before we can synthesize the results of observations, theory, and modeling studies, to achieve a comprehensive understanding of their structure and dynamics.…”

Section: Introductionmentioning

confidence: 99%

On polarimetric characteristics in SAR images of mesoscale cellular convection in the marine atmospheric boundary layer

Perrie

Guo

et al. 2011

J. Geophys. Res.

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Convection is an important phenomenon in the marine atmospheric boundary layer (MABL). Previous spaceborne radar studies of such have been limited to single polarization data, and therefore their focus was on the variation in intensity of the radar return, which was constrained by the existence of a single polarization image pattern, representing different atmospheric and oceanic phenomena. In this paper, we study the polarimetric characteristics of mesoscale cellular convection (MCC) in the MABL using high‐resolution data from fully polarimetric (HH, VV, HV, and VH) RADARSAT‐2 (RS‐2) synthetic aperture radar (SAR) images, in conjunction with closely collocated mesoscale atmospheric model simulations, to identify the MCC signatures. To compare the polarimetric characteristics of MCC with those of the ocean surface, our analysis also includes 641 open ocean surface quad‐polarization RS‐2 SAR images collocated with 52 National Data Buoy Center buoys. The open ocean surface SAR images exhibit different polarimetric characteristics from those of MCC. Thus, we differentiate MCC from other open ocean phenomena, based on identifiable polarimetric SAR characteristics.

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Interpreting sea surface slicks on the basis of the normalized radar cross‐section model using RADARSAT‐2 copolarization dual‐channel SAR images

Ivonin

Skrunes

Brekke

et al. 2016

Geophysical Research Letters

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A simple automatic multipolarization technique for discrimination of main types of thin oil films (of thickness less than the radio wave skin depth) from natural ones is proposed. It is based on a new multipolarization parameter related to the ratio between the damping in the slick of specially normalized resonant and nonresonant signals calculated using the normalized radar cross‐section model proposed by Kudryavtsev et al. (2003a). The technique is tested on RADARSAT‐2 copolarization (VV/HH) synthetic aperture radar images of slicks of a priori known provenance (mineral oils, e.g., emulsion and crude oil, and plant oil served to model a natural slick) released during annual oil‐on‐water exercises in the North Sea in 2011 and 2012. It has been shown that the suggested multipolarization parameter gives new capabilities in interpreting slicks visible on synthetic aperture radar images while allowing discrimination between mineral oil and plant oil slicks.

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On the co‐polarized phase difference for oil spill observation

Cited by 160 publications

References 21 publications

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network

On polarimetric characteristics in SAR images of mesoscale cellular convection in the marine atmospheric boundary layer

Interpreting sea surface slicks on the basis of the normalized radar cross‐section model using RADARSAT‐2 copolarization dual‐channel SAR images

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