• DocumentCode
    576515
  • Title

    Model based oil spill detection using polarimetric SAR

  • Author

    Salberg, Arnt-Børre ; Rudjord, Øystein ; Solberg, Anne H S

  • Author_Institution
    Dept. SAMBA, Norwegian Comput. Center, Oslo, Norway
  • fYear
    2012
  • fDate
    22-27 July 2012
  • Firstpage
    5884
  • Lastpage
    5887
  • Abstract
    In this paper we propose a model-based oil spill detection approach using polarimetic SAR data. The underlying hypothesis is that the co-polarized phase difference is zero when assuming Bragg scattering mechanisms. From this hypothesis we may construct a (linear) model and derive features that discriminates oil slicks from sea water using dual-polarized (VV and HH) SAR data. We also investigate the model when assuming X-Bragg scattering, and derive a feature based on the Pauli decomposition that are invariant to the tilt angle reflection plane. The oil spill detection methodology is evaluated on a Radarsat-2 quad-pol image that covers various types of oil released in an oil-in-sea exercise in Norway. The results show that the oil spills are clearly visible in all of the derived feature-based images. Furthermore, the feature images have a more homogeneous background than the VV-polarized SAR image, in particular the Pauli-based feature image that suppresses a non-oil slick present in the VV-image.
  • Keywords
    marine pollution; remote sensing by radar; synthetic aperture radar; water pollution measurement; Bragg scattering mechanisms; Norway; Pauli decomposition; Radarsat-2 quadpol image; X-Bragg scattering; dual-polarized SAR data; feature-based images; model-based oil spill detection approach; oil slicks; oil spill detection methodology; oil-in-sea exercise; polarimetic SAR data; sea water; tilt angle reflection plane; Backscatter; Data models; Remote sensing; Scattering; Sea surface; Surface waves; Synthetic aperture radar; Oil spill detection; SAR; dual-polarization;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International
  • Conference_Location
    Munich
  • ISSN
    2153-6996
  • Print_ISBN
    978-1-4673-1160-1
  • Electronic_ISBN
    2153-6996
  • Type

    conf

  • DOI
    10.1109/IGARSS.2012.6352270
  • Filename
    6352270