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.

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