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Open AccessArticle

Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform

1
Department of Geoscience and Remote Sensing, Delft University of Technology, 2628CN Delft, The Netherlands
2
GRID–UNSW Built Environment, Sydney, NSW 2052, Australia
3
Geospatial Information Agency, Cibinong, Bogor 16911, Indonesia
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1727; https://doi.org/10.3390/rs11141727
Received: 6 June 2019 / Revised: 16 July 2019 / Accepted: 19 July 2019 / Published: 21 July 2019
(This article belongs to the Section Urban Remote Sensing)
Many urban applications require building polygons as input. However, manual extraction from point cloud data is time- and labor-intensive. Hough transform is a well-known procedure to extract line features. Unfortunately, current Hough-based approaches lack flexibility to effectively extract outlines from arbitrary buildings. We found that available point order information is actually never used. Using ordered building edge points allows us to present a novel ordered points–aided Hough Transform (OHT) for extracting high quality building outlines from an airborne LiDAR point cloud. First, a Hough accumulator matrix is constructed based on a voting scheme in parametric line space (θ, r). The variance of angles in each column is used to determine dominant building directions. We propose a hierarchical filtering and clustering approach to obtain accurate line based on detected hotspots and ordered points. An Ordered Point List matrix consisting of ordered building edge points enables the detection of line segments of arbitrary direction, resulting in high-quality building roof polygons. We tested our method on three different datasets of different characteristics: one new dataset in Makassar, Indonesia, and two benchmark datasets in Vaihingen, Germany. To the best of our knowledge, our algorithm is the first Hough method that is highly adaptable since it works for buildings with edges of different lengths and arbitrary relative orientations. The results prove that our method delivers high completeness (between 90.1% and 96.4%) and correctness percentages (all over 96%). The positional accuracy of the building corners is between 0.2–0.57 m RMSE. The quality rate (89.6%) for the Vaihingen-B benchmark outperforms all existing state of the art methods. Other solutions for the challenging Vaihingen-A dataset are not yet available, while we achieve a quality score of 93.2%. Results with arbitrary directions are demonstrated on the complex buildings around the EYE museum in Amsterdam. View Full-Text
Keywords: building outline; Hough transform; point cloud; ordered points; regularization building outline; Hough transform; point cloud; ordered points; regularization
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MDPI and ACS Style

Widyaningrum, E.; Gorte, B.; Lindenbergh, R. Automatic Building Outline Extraction from ALS Point Clouds by Ordered Points Aided Hough Transform. Remote Sens. 2019, 11, 1727.

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