Scan Angle Analysis of Airborne Lidar Data for Missing Return Approximation in Urban Areas

The density and uniformity of lidar data play crucial roles in the cor-responding data processing steps. One factor influencing point density and spacing in lidar data is the presence of empty pulses, where no return is detected. Missing returns can occur due to atmospheric absorption, specular and diffusive reflection, etc. To address this issue and enhance point density, this paper introduces a novel method for approximating missing returns in airborne lidar data collected over urban areas. This technique focuses on approximating returns for empty pulses that hit spots near abrupt slope changes on building and ground surfaces. The proposed methodology is validated through experiments using a lidar data set from downtown Dublin, Ireland. The collected data contained numerous gaps associated with wet surfaces, as well as missing returns on vertical and oblique surfaces.