Anisotropic Surface Detection Over Coastal Environment Using Near-IR LiDAR Intensity Maps


Close to-IR LiDAR intensity maps have been investigated to detect surfaces characterised by spatially anisotropic properties. The developed estimators gift an important sensitivity to the surface anisotropic properties (degree of anisotropy and relative anisotropy) and will be corrected for signal-to-noise ratio, which will be highly variable over coastal environments thanks to heterogeneous moisture distribution in house and time. 2 methodologies are proposed to research the data in order to unambiguously characterize the surface anisotropic properties by separating the pure textural information from the amplitude weighted one, that provides a additional relevant data in geomorphology than the pure texture. As an example, knowledge acquired over sandy beaches are considered to illustrate the potential of surface characterization using both degree of anisotropy and associated texture amplitude. Sea surfaces are investigated to show how will the pure texture approach allow to discriminate totally different sea states.

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