Automatic detection of building points from lidar and dense image matching point clouds

Abstract

This study aims to detect automatically building points: (a) from LIDAR point cloud using simple techniques of filtering that enhance the geometric properties of each point, and (b) from a point cloud which is extracted applying dense image matching at high resolution colour-infrared (CIR) digital aerial imagery using the stereo method semi-global matching (SGM). At first step, the removal of the vegetation is carried out. At the LIDAR point cloud, two different methods are implemented and evaluated using initially the normals and the roughness values afterwards: (1) the proposed scan line smooth filtering and a thresholding process, and (2) a bilateral filtering and a thresholding process. For the case of the CIR point cloud, a variation of the normalized differential vegetation index (NDVI) is computed for the same purpose. Afterwards, the bare-earth is extracted using a morphological operator and removed from the rest scene so as to maintain the buildings points. The results of the extracted buildings applying each approach at an urban area in northern Greece are evaluated using an existing orthoimage as reference; also, the results are compared with the corresponding classified buildings extracted from two commercial software. Finally, in order to verify the utility and functionality of the extracted buildings points that achieved the best accuracy, the 3D models in terms of Level of Detail 1 (LoD 1) and a 3D building change detection process are indicatively performed on a sub-region of the overall scene.