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Sediment characterization in the ‘IJzermonding’ using empirical orthogonal functions: application to CASI
Adam, S.; Salama, S.; Monbaliu, J. (2005). Sediment characterization in the ‘IJzermonding’ using empirical orthogonal functions: application to CASI, in: Herrier, J.-L. et al. (Ed.) Proceedings 'Dunes and Estuaries 2005': International Conference on nature restoration practices in European coastal habitats, Koksijde, Belgium 19-23 September 2005. VLIZ Special Publication, 19: pp. 583-584
In: Herrier, J.-L. et al. (2005). Proceedings 'Dunes and Estuaries 2005': International Conference on nature restoration practices in European coastal habitats, Koksijde, Belgium 19-23 September 2005. VLIZ Special Publication, 19. Vlaams Instituut voor de Zee (VLIZ): Oostende. XIV, 685 pp., more
In: VLIZ Special Publication. Vlaams Instituut voor de Zee (VLIZ): Oostende. ISSN 1377-0950, more
Available in  Authors 
Document type: Conference paper
Keywords
    Physics > Biophysics
    Sedimentary structures > Mud flats
    Marine/Coastal
Authors  Top 
  • Adam, S., more
  • Salama, S.
  • Monbaliu, J., more
Abstract
    The erodability of mudflats is strongly determined by biophysical characteristics of sediments, such as silt, sand, benthic microalgae and water content. Mudflats are often large and inaccessible areas, leading to dangerous and time-consuming in situ measurement campaigns. Furthermore the collected point samples are unrepresentative for the spatial variability of these coastal systems. Airborne hyperspectral remote sensing is identified to be effective for the collection of a synoptic overview of biophysical characteristics of sediments in mudflats. An automated method for the classification of hyperspectral images acquired by the Compact Airborne Spectrographic Imager (CASI) is proposed. The method is based on a linear transformation of each spectrum in the hyperspectral cube. Comparable classification results are obtained using a standard classification method employed in hyperspectral image processing. The superiority of the proposed method lies in its robustness, computational requirements, repeatability, interpretability and objectiveness.
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