• DocumentCode
    1207348
  • Title

    Fusing AMSR-E and QuikSCAT Imagery for Improved Sea Ice Recognition

  • Author

    Yu, Peter ; Clausi, David A. ; Howell, Stephen E L

  • Author_Institution
    Dept. of Syst. Design Eng., Univ. of Waterloo, Waterloo, ON
  • Volume
    47
  • Issue
    7
  • fYear
    2009
  • fDate
    7/1/2009 12:00:00 AM
  • Firstpage
    1980
  • Lastpage
    1989
  • Abstract
    The benefits of augmenting Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) image data with Quick Scatterometer (QuikSCAT) image data for supervised sea ice classification in the Western Arctic region are investigated. Experiments compared the performance of a maximum likelihood classifier when used with the AMSR-E-only data set against using the combined data. The preferred number of bands to use for classification was examined, as well as whether principal component analysis (PCA) can be used to reduce the dimensionality of the data. The reliability of training data over time was also investigated. Adding QuikSCAT often improves classifier accuracy in a statistically significant manner and never decreases it significantly when a sufficient number of bands are used. Combining these data sets is beneficial for sea ice mapping. Using all available bands is recommended, data fusion with PCA does not offer any benefit for these data, and training data from a specific date remains reliable within 30 days.
  • Keywords
    geophysics computing; image classification; image fusion; oceanographic regions; principal component analysis; remote sensing; sea ice; AMSR-E-QuikSCAT imagery fusion; Advanced Microwave Scanning Radiometer for the Earth Observing System; PCA; Quick Scatterometer; Western Arctic region; data dimensionality reduction; maximum likelihood classifier; principal component analysis; sea ice mapping; sea ice recognition; supervised sea ice classification; training data reliability; Beaufort Sea; classification; data fusion; ice mapping; multisensor; passive microwave; principal component (PC) analysis (PCA); scatterometer;
  • fLanguage
    English
  • Journal_Title
    Geoscience and Remote Sensing, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0196-2892
  • Type

    jour

  • DOI
    10.1109/TGRS.2009.2013632
  • Filename
    4806106