Segmentation of LiDAR Data for Extracting Building's Shapes, Using Fuzzy Logic Concepts

Building extraction becomes one of important fields of research in photogrammetry and machine vision. Involving airborne laser scanner (LiDAR) in geomatics, as an active sensor of 3D data acquisition, great evolution happened in data preparing to 3D outdoor object modeling. In consequence a great trend in developers and geomatics science researcher’s communities leaded them to develop techniques in extracting interesting objects from point cloud such as buildings. The LiDAR data don’t explicitly contain any geometric information and feature of objects. The feature such as planes, lines and corners can be only indirectly extracted by segmentation algorithms. In this research, a method for segmentation of LiDAR data for extracting buildings is proposed. In the proposed method, input data is an irregular LiDAR data and an adaptive clustering method for roof plane extraction from LiDAR data is implemented. So, to extract the roof structure, an assumption of planarity has been made. It is assumed that the roof can be modeled by a set of planar segments. In the proposed method the clustering is done without having any prior information about the number of the clusters. The clustering is done using FCM algorithm with considering maximum cluster number and then the extracted cluster will be modified using an iteratively split and merge technique. In this technique, small parallel planes are merged to create a new plane and a big plane created from different planes is divided to generate correct planes. This approach was evaluated using some buildings of used data set and the results prove high efficiency and reliability of this method for extraction of buildings.