@InProceedings{8903047,
  author = {M. -C. Benito-Ortiz and D. Mata-Moya and M. -P. Jarabo-Amores and N. d. Rey-Maestre and P. -J. Gomez-del-Hoyo},
  booktitle = {2019 27th European Signal Processing Conference (EUSIPCO)},
  title = {Generalized Gamma Distribution SAR Sea Clutter Modelling for Oil Spill Candidates Detection},
  year = {2019},
  pages = {1-5},
  abstract = {This paper tackles the oil spills offshore monitoring using satellite Earth Observation tools based on Synthetic Aperture Radar (SAR) sensors. The proposed processing scheme is based on modelling SAR sea backscattering assuming a Generalized Gamma Distribution clutter. The signal processing scheme includes a first stage to define the non-homogeneous area due to the presence of dark spots in function of the multi-scale estimations of a textural parameter defined as the inverse of the product between shape and scale sea clutter parameters. After an statistical study of this parameter, a robust value can be defined for comparison purposes. In the resulted search area, an adaptive thresholding is performed to obtain a segmented image with the oil slicks candidates contouring at pixel level. Results obtained with SAR images acquired by Sentinel-1 over Corsica, confirm the suitability of the proposed methodology.},
  keywords = {filtering theory;gamma distribution;geophysical signal processing;image segmentation;marine pollution;oceanographic techniques;radar clutter;radar imaging;remote sensing by radar;synthetic aperture radar;signal processing scheme;nonhomogeneous area;multiscale estimations;textural parameter;scale sea clutter parameters;oil slicks candidates;SAR images;Generalized gamma distribution SAR sea clutter;oil spill candidates detection;offshore monitoring;Synthetic Aperture Radar sensors;generalized gamma distribution clutter;SAR sea backscattering;satellite Earth Observation tools;Oils;Clutter;Radar polarimetry;Estimation;Feature extraction;Synthetic aperture radar;Shape;SAR;Oil Spill;Generalized Gamma Distribution;Radar Detection},
  doi = {10.23919/EUSIPCO.2019.8903047},
  issn = {2076-1465},
  month = {Sep.},
  url = {https://www.eurasip.org/proceedings/eusipco/eusipco2019/proceedings/papers/1570533581.pdf},
}

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Rey-Maestre, N.","Gomez-del-Hoyo, P. -."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","author":[{"firstnames":["M.","-C."],"propositions":[],"lastnames":["Benito-Ortiz"],"suffixes":[]},{"firstnames":["D."],"propositions":[],"lastnames":["Mata-Moya"],"suffixes":[]},{"firstnames":["M.","-P."],"propositions":[],"lastnames":["Jarabo-Amores"],"suffixes":[]},{"firstnames":["N."],"propositions":["d."],"lastnames":["Rey-Maestre"],"suffixes":[]},{"firstnames":["P.","-J."],"propositions":[],"lastnames":["Gomez-del-Hoyo"],"suffixes":[]}],"booktitle":"2019 27th European Signal Processing Conference (EUSIPCO)","title":"Generalized Gamma Distribution SAR Sea Clutter Modelling for Oil Spill Candidates Detection","year":"2019","pages":"1-5","abstract":"This paper tackles the oil spills offshore monitoring using satellite Earth Observation tools based on Synthetic Aperture Radar (SAR) sensors. The proposed processing scheme is based on modelling SAR sea backscattering assuming a Generalized Gamma Distribution clutter. The signal processing scheme includes a first stage to define the non-homogeneous area due to the presence of dark spots in function of the multi-scale estimations of a textural parameter defined as the inverse of the product between shape and scale sea clutter parameters. After an statistical study of this parameter, a robust value can be defined for comparison purposes. In the resulted search area, an adaptive thresholding is performed to obtain a segmented image with the oil slicks candidates contouring at pixel level. 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