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Title: Oil spill detection by imaging radars: Challenges and pitfalls

Journal Article · · Remote Sensing of Environment
 [1];  [2];  [3]
  1. Univ. of Hamburg, Hamburg (Germany). Inst. of Oceanography
  2. California Inst. of Technology (CalTech), Pasadena, CA (United States). Jet Propulsion Lab.
  3. Ocean Univ. of Chna, Qingdao (China). Ocean Remote Sensing Inst.

In this paper, criteria for discriminating between radar signatures of oil films and biogenic slicks visible on synthetic aperture radar (SAR) images of the sea surface as dark patches are critically reviewed. We question the often claimed high success rate of oil spill detection algorithms using single-polarization SARs because the SAR images used to train these algorithms are based usually on subjective interpretation and are not validated by on-site inspections or multi-sensor measurements carried out from oil pollution surveillance planes. Furthermore, we doubt that polarimetric parameters derived from fully-polarimetric SAR data, like entropy, anisotropy, and mean scattering angle, are beneficial for discriminating between mineral oil films and biogenic slicks. We challenge the often-made claim that another scattering mechanism than Bragg scattering applies for radar backscattering from mineral oil films than from biogenic slicks. This view is supported by data acquired by the Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) of NASA/JPL, which operates at L-band and has an extremely low noise floor. We suspect that opposing results obtained from previous analyses of spaceborne polarimetric SAR data are caused by the high noise floors of the spaceborne SARs. However, most of the analyzed spaceborne polarimetric data were not acquired at L-band, but at C-and X-band. On the other hand, differences in the statistical behavior of the radar backscattering could be real due to the fact that, other than biogenic surface films, mineral oil films, can form multi-layers, whose thickness can vary within an oil patch. Radar backscattering from emulsion layers can also fluctuate due to variations of the oil/water mixture ratio. These effects could cause an increase of the standard deviation (STD) of the co-polarized phase difference (CPD) for scattering at mineral oil films and emulsions. In the special case of thick oil layers or oil/water emulsion layers, where the radar is sensitive to the dielectric constant of the oil, discrimination becomes possible due the fact that Bragg scattering depends on the dielectric constant of the scattering medium.

Research Organization:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center (NERSC)
Sponsoring Organization:
USDOE
OSTI ID:
1478758
Journal Information:
Remote Sensing of Environment, Vol. 201, Issue C; ISSN 0034-4257
Publisher:
ElsevierCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 145 works
Citation information provided by
Web of Science

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Cited By (14)

The ASI Integrated Sounder-SAR System Operating in the UHF-VHF Bands: First Results of the 2018 Helicopter-Borne Morocco Desert Campaign journal August 2019
COSMO–SkyMed Synthetic Aperture Radar Data to Observe the Deepwater Horizon Oil Spill journal October 2018
Synthetic Aperture Radar Remote Sensing of Operational Platform Produced Water Releases journal December 2019
Tracking an Oil Tanker Collision and Spilled Oils in the East China Sea Using Multisensor Day and Night Satellite Imagery journal April 2018
Protecting oceans from illicit oil spills: environment control and remote sensing using spaceborne imaging radars journal November 2019
On the analysis of a time series of X–band TerraSAR–X SAR imagery over oil seepages journal November 2018
Integration of Automatic Identification System (AIS) Data and Single-Channel Synthetic Aperture Radar (SAR) Images by SAR-Based Ship Velocity Estimation for Maritime Situational Awareness journal September 2019
On-site visualized classification of transparent hazards and noxious substances on a water surface by multispectral techniques journal January 2019
Spaceborne radar remote sensing of ocean surfaces: electromagnetic modelling and applications journal December 2019
Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter journal February 2019
Marine Oil Slicks Quantification From L-band Dual-Polarization SAR Imagery journal April 2019
Dependence of the noise level estimates supplied with TerraSAR-X data on the used preprocessor version journal January 2019
Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning journal July 2020
Bilge dump Automatic Alert System in Southern Africa Oceans journal August 2020

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