• DocumentCode
    5065
  • Title

    Sea Ice Concentration Retrieval Using Composite ScanSAR Features in a SAR Data Assimilation Process

  • Author

    Kasapoglu, Necip Gokhan

  • Author_Institution
    Dept. of Phys. & Technol., Univ. of Tromso (UiT)-The Arctic Univ. of Norway, Tromso, Norway
  • Volume
    11
  • Issue
    12
  • fYear
    2014
  • fDate
    Dec. 2014
  • Firstpage
    2085
  • Lastpage
    2089
  • Abstract
    Sensor and target originated variations in the scanning synthetic aperture radar (ScanSAR) images cause increase in variances of observations that affect the performance of data assimilation systems for sea ice analysis. Incidence angle (IA) dependence of radar backscatter along the range direction is one of the largest contributors to this variation. This important aspect can be taken into account both in a forward model and in a feature selection process. In this letter, an incidence-angle-dependent forward model is employed to reduce this variation. Moreover, instead of assimilation of ScanSAR features for the entire swath, individual features for specific IA intervals are used in order to decrease analysis bias and to achieve more consistent results.
  • Keywords
    backscatter; data assimilation; feature extraction; geophysical image processing; oceanographic techniques; radar imaging; sea ice; synthetic aperture radar; SAR data assimilation process; ScanSAR feature assimilation; ScanSAR images; analysis bias; composite ScanSAR features; data assimilation system; feature selection process; incidence-angle-dependent forward model; observation variances; radar backscatter; range direction; scanning synthetic aperture radar images; sea ice analysis; sea ice concentration retrieval; sensor originated variations; target originated variations; Backscatter; Feature extraction; Sea ice; Standards; Synthetic aperture radar; Data assimilation (DA); geophysical signal processing; gray-level co-occurrence matrix (GLCM); sea ice; synthetic aperture radar (SAR); texture;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1545-598X
  • Type

    jour

  • DOI
    10.1109/LGRS.2014.2319212
  • Filename
    6815673