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

Abstract

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,more » 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.« less

Authors:
 [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.
Publication Date:
Research Org.:
Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States). National Energy Research Scientific Computing Center
Sponsoring Org.:
USDOE
OSTI Identifier:
1478758
Resource Type:
Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
[ Journal Volume: 201; Journal Issue: C]; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
47 OTHER INSTRUMENTATION; Oil spills; Biogenic slicks; SAR; Polarimetry; Co-polarized phase difference

Citation Formats

Alpers, Werner, Holt, Benjamin, and Zeng, Kan. Oil spill detection by imaging radars: Challenges and pitfalls. United States: N. p., 2017. Web. doi:10.1016/j.rse.2017.09.002.
Alpers, Werner, Holt, Benjamin, & Zeng, Kan. Oil spill detection by imaging radars: Challenges and pitfalls. United States. doi:10.1016/j.rse.2017.09.002.
Alpers, Werner, Holt, Benjamin, and Zeng, Kan. Mon . "Oil spill detection by imaging radars: Challenges and pitfalls". United States. doi:10.1016/j.rse.2017.09.002. https://www.osti.gov/servlets/purl/1478758.
@article{osti_1478758,
title = {Oil spill detection by imaging radars: Challenges and pitfalls},
author = {Alpers, Werner and Holt, Benjamin and Zeng, Kan},
abstractNote = {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.},
doi = {10.1016/j.rse.2017.09.002},
journal = {Remote Sensing of Environment},
number = [C],
volume = [201],
place = {United States},
year = {2017},
month = {9}
}

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