Detection of Planar Points for Building Extraction from Lidar Data Based on Differential Morphological and Attribute Profiles

This paper considers a new method for building-extraction from LiDAR data. This method uses multi-scale levelling schema or MSLS-segmentation based on differential morphological profiles for removing non-building points from LiDAR data during the data denoising step. A new morphological algorithm is proposed for the detection of flat regions and obtaining a set of building-candidates. This binarisation step is made by using differential attribute profiles based on the sum of the second-order morphological gradients. Any distinction between flat and rough surfaces is achieved by area-opening, as applied within each attribute-zone. Thus, the detection of the flat regions is essentially based on the average gradient contained within a region, whilst avoiding subtractive filtering rule. Finally, the shapes of the flat-regions are considered during the building-recognition step. A binary shape-compactness attribute opening is used for this purpose. The efficiency of the proposed method was demonstrated on three test LiDAR datasets containing buildings of different sizes, shapes, and structures. As shown by the experiments, the average quality of the buildings-extraction was more than 95%, with 96% correctness, and 98% completeness. In terms of quality, this method is comparable with TerraScan R ©, but both methods significantly differ when comparing correctness and completeness of the results.