Abstract
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.
Highlights
The technological development in the fields of computer vision and digital photogrammetry provides new tools and automated solutions for applications in urban studies, cadastre, etc, associated with urban development, identification of illegal constructions, 3D modelling, change detection, etc
This study aims to automatically detect building points: (a) from LIDAR data using simple techniques of filtering that enhance the geometric properties of each point, and (b) from a CIR dense image point cloud that extracted using the semi-global matching (SGM) technique
The proposed approaches are considered to be satisfactory; especially those that implement the proposed scan line smooth filtering and the bilateral filtering at the LIDAR point cloud as success rates of completeness, correctness and quality larger than 95% are achieved without the use of training data or any additional information, such as intensity or multiple returns
Summary
The technological development in the fields of computer vision and digital photogrammetry provides new tools and automated solutions for applications in urban studies, cadastre, etc, associated with urban development, identification of illegal constructions, 3D modelling, change detection, etc. Numerous algorithms have been developed over the years for the automatic building detection using LIDAR data and point clouds from dense image matching. Han et al (2007) proposed a fast and memory-efficient segmentation algorithm similar to the region-growing and unsupervised-classification methods for airborne laser point clouds utilizing scan line characteristics. Zhou and Neumann, (2008) proposed an automatic algorithm which reconstructs building models from LIDAR data. After the removal of vegetation applying a SVM method based on local geometry property analysis, the planar roofs and the boundaries of buildings were extracted. Their classification algorithm achieved a success rate of 95%
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