Segmentation to the point clouds of LIDAR data based on change of Kurtosis

Abstract

Airborne laser scanning, also known by the acronym LIDAR (Light Detection And Ranging), is an operationally mature remote sensing technology and it can provide rapid and highly-accurate measurements of both object and ground surface over large areas. Presently, there are mostly two class of methods are used to process the LIDAR data. One method is a method that processing the lidar image like two dimensions ordinary image; the other method is a way that directly processing the point clouds of airborne LIDAR data, that is the non-ground points are filtered from all point clouds of LIDAR data. Among the second class method, some algorithms have been also developed to process the point clouds of LIDAR data. In this paper, a statistical algorithm-change of Kurtosis is presented to separate non-ground points and ground points. From the curve of kurtosis's change, its inflexion is easily found to separate the object points and ground points. The algorithm will be test on three study areas of LIDAR data provided by ISPRS Commission III Working Group 3: City site 3, City site 4 and Forest site 5. The algorithm efficiently separates ground and object points. Furthermore, lower objects, such as bridge, can be distinguished from other higher vegetation by the change of Kurtosis.