• Title of article

    Combination of overlap-driven adjustment and Phong model for LiDAR intensity correction

  • Author/Authors

    Ding، نويسنده , , Qiong and Chen، نويسنده , , Wu and King، نويسنده , , Bruce and Liu، نويسنده , , Yanxiong and Liu، نويسنده , , Guoxiang، نويسنده ,

  • Issue Information
    روزنامه با شماره پیاپی سال 2013
  • Pages
    8
  • From page
    40
  • To page
    47
  • Abstract
    Airborne laser scanning LiDAR systems deliver not only geometric (X, Y, Z) information of the scanned surfaces but also the returned intensity of the laser pulse. Recent studies have shown the potential of using intensity data for many applications. However, there are limitations in using the raw intensity data because of radiometric system bias, reflectance noise and variations between adjacent strips. To overcome these limitations, a three-step LiDAR intensity correction algorithm is proposed. Following corrections for environmental and surface effects, an overlap-driven least-squares adjustment model that does not rely on the selection of homologous points minimizes intensity differences in the overlap area of strips. Finally, the Phong reflection model, which describes both diffuse and specular reflectance, is used to attenuate the effects of strong reflections that typically occur over wet or water dominated areas. The algorithm was applied to a multi-strip LiDAR dataset that covers wetlands in the estuary of the Yellow River, People’s Republic of China. Results demonstrated a significant reduction in radiometric differences in the overlap areas, and strong specular reflections in the nadir regions were reduced. Objects which were obscured by the specular reflection in the original intensity data were clearly identifiable after the adjustment.
  • Keywords
    least squares , Overlapping strips , reflection , Airborne laser scanning , intensity
  • Journal title
    ISPRS Journal of Photogrammetry and Remote Sensing
  • Serial Year
    2013
  • Journal title
    ISPRS Journal of Photogrammetry and Remote Sensing
  • Record number

    2229113