Building Extraction from LiDAR Point Clouds Using Fuzzy Inference System

Building extraction from different data resources, which can be applied in 3D city modeling, is one of the most growing research topics in Photogrammetry and Remote Sensing. The aim of this study is proposing a new method for extracting buildings from LiDAR point clouds using a fuzzy inference system. In the proposed method, first, a pre-processing step is performed to eliminate low altitude points and also noisy points. In this regard, an optimal noise removal technique is used to remove noise and outliers from LiDAR point clouds. The proposed method can almost eliminate all of the noises and outliers from the point clouds. After pre-processing step, only high altitude points in the form of points groups including buildings and trees will remain. Then, due to the uncertainty in distinguishing building groups from tree groups, a fuzzy inference system is designed and implemented. In the proposed fuzzy system, three geometric descriptors of "SumD", "Area" and "Volume" are considered as input variables, and feature type as output variable is defined. In order to evaluate the proposed method, a test area of Belgium is used, and the obtained results proved the ability of the proposed fuzzy inference system in resolving the uncertainty in detecting building points group. Moreover, the proposed noise removal method increased 10% in accuracy and improving the quality of the fuzzy system.
3D city Modeling using the technology of LiDAR is one of the most growing research topics in Photogrammetry and Remote Sensing.