ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume V-2-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 351–358, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-351-2022
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2022, 351–358, 2022
https://doi.org/10.5194/isprs-annals-V-2-2022-351-2022
 
17 May 2022
17 May 2022

EXTRACTION OF ORTHOGONAL BUILDING BOUNDARY FROM AIRBORNE LIDAR DATA BASED ON FEATURE DIMENSION REDUCTION

Y. Chen1 and W. Yao1,2 Y. Chen and W. Yao
  • 1Department of Land Surveying and Geo-Informatics, The Hong Kong Polytechnic University, Hung Hom, Hong Kong
  • 2Otto Poon Charitable Foundation Smart Cities Research Institute, The Hong Kong Polytechnic University, Hung Hom, Hong Kong

Keywords: LiDAR, Building boundary, Feature dimension reduction, Recursive Gaussian filtering

Abstract. Building boundary extraction is an active research topic in the field of feature extraction from airborne LiDAR point cloud data. Owing to the high complexity of most building extraction algorithms based on point clouds, multiple feature parameters must often be combined with iterative operations, particularly in the process of mitigating the sawtooth phenomenon using the sleeve algorithm and its improved versions. To improve the degree of automation and ensure accuracy, this study proposes a fast corner point detection method based on a dimensionality reduction technique, which utilizes reduced data mapping from 3D to 2D. We converted the boundaries extracted by the alpha shape algorithm to a 2D image and applied recursive Gaussian filtering with a relatively high level of automation to smoothen the image edges and mitigate the sawtooth phenomenon, thereby improving upon the sleeve algorithm, which requires a large number of iterations. Subsequently, the Douglas Peucker algorithm is used to retrieve the contour key points after extracting the contour lines and obtaining the regularized building contours using the grouped orthogonal regularization method. To verify the accuracy of the algorithm, it was compared with a cluster and adjustment (CAA)method based on the sleeve algorithm using three major evaluation metrics with respect to four representative building instances in two experimental datasets of urban areas. The value of the RMSE was reduced by an average of 43.79%. In addition, the time complexity decreased from O(n2) to O(n). These results demonstrate that the proposed method improves not only the accuracy of boundary extraction, but also the efficiency of data processing.