Laser Intensity Used in Classification of Lidar Point Cloud Data

Abstract

LIDAR (LIght Detection And Ranging) is a powerful remote sensing technology for the acquisition of terrain surface. The LIDAR system not only generates the 3D points cloud with irregular spacing, but also detects the laser impulse reflection data. The algorithms used for the LIDAR data are mostly used to deal with the 3D points cloud, produce the digital terrain model (DTM) and detect the objects, such as buildings. Usually, these objects must be classified as part of the extraction. In order to classify, other information besides the height information of points cloud is required, such as laser intensity information. However, few classification algorithms using intensity data have been deeply investigated. The laser intensity is different from material to material. The intensity of reflection on the same material is similar, while pulsed on different material is differ. Based on this theory, this paper provides a classification algorithm of airborne laser scanning altimetry data combined with the intensity of laser in detail. This paper proposes a classification algorithm for LiDAR points data by fusing height data and intensity data. Initially, the height data is used to generate the original terrain data after filtering. As a result, the terrain data and objects data are separated. Second, a filter algorithm is applied to the raw laser intensity of object points. Third, a histogram of filtered intensity is obtained. Fourth, the statistic result combined with the height information according to the pulse feature of laser intensity, is analyzed.