Calibration of Light Detection And Ranging（LiDAR） intensity data can improve classification reliability. The normalized algorithm based on laser transmission distance and scanning angle is the most common algorithm for LiDAR intensity data calibration.However, this study determined that the influence of the tilt angles of several round objects, such as triangular and arched roofs, on echo intensity can be hardly calibrated by the normalized algorithm.Therefore, an optimized algorithm that calculates the tilt angle and calibrates the corresponding influence on LiDAR intensity data was developed. Eight typical ground objects in the study area were selected and sampled. The laser reflectivity of the samples was measured with Lamada 950 and regarded as a reference to evaluate the intensity calibration results. The steps of the optimized algorithm are as follows:（1）calibrate the original intensity value based on laser transmission distance;（2） determine whether adjacent laser points belong to the same tilted object by comparing their plane distance, elevation, and intensity with cut-off values;（3） calculate the tilt angle when the adjacent laser points belong to the same tilted object;（4） propose a rule by analyzing the LiDAR data collection mode and features of tilted ground objects and use this rule to determine whether the tilt angle is negative or positive;（5） calculate the corresponding reflection angle by summing the tilt angle and absolution of the scanning angle; and（6） recalibrate the intensity based on the reflection angle and laser transmission distance to eliminate the influence of reflection angle. The differences in the mean intensity of ground objects calculated with the existing normalized algorithm are consistent with the laser reflection measurement results, except for the blue-and-white-painted iron from different roofs. By contrast, the differences in the mean intensity of all eight ground objects are in agreement with the laser reflection measurement results obtained by using the optimized algorithm.For instance, the range and mean-square deviation of the intensity of triangular and arched roofs with blue-painted iron are both reduced. A few unreliable high intensity values correspond to the laser points, such as the wall of buildings and street lamp. Although these points meet the cut-offs, they have a small plane distance value. Thus, the calculated tilt angles for these points are large, and the cosine values are small;this condition results in unreliable high intensity values. The optimized algorithm can identify the laser points of homogeneous ground objects with a certain area and the same material and calculate the corresponding tilt angles. The influence of tilt angle can be effectively eliminated after calibration based on the reflection angle obtained by summing the tilt and scanning angles. The optimized algorithm can therefore improve classification feasibility based on the intensity value. With regard to the few unreliable high intensity values, two possible solutions are proposed for further study. Given the reliable empirical cut-off values presented by numerous experiments, LiDAR data can be preliminarily classified prior to separate the laser points. The optimized algorithm can then be utilized to calibrate intensity.

airborne LiDAR;intensity;tilt angle;reflection angle;laser reflectivity;normalization calibration