- Title
- Robust building roof segmentation using airborne point cloud data
- Creator
- Gilani, Syed; Awrangjeb, Mohammad; Lu, Guojun
- Date
- 2016
- Type
- Text; Conference proceedings; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/154271
- Identifier
- vital:11092
- Identifier
-
https://doi.org/10.1109/ICIP.2016.7532479
- Identifier
- ISBN:15224880 (ISSN); 9781467399616 (ISBN)
- Abstract
- Approximation of the geometric features is an essential step in point cloud segmentation and surface reconstruction. Often, the planar surfaces are estimated using principal component analysis (PCA), which is sensitive to noise and smooths the sharp features. Hence, the segmentation results into unreliable reconstructed surfaces. This article presents a point cloud segmentation method for building detection and roof plane extraction. It uses PCA for saliency feature estimation including surface curvature and point normal. However, the point normals around the anisotropic surfaces are approximated using a consistent isotropic sub-neighbourhood by Low-Rank Subspace with prior Knowledge (LRSCPK). The developed segmentation technique is tested using two real-world samples and two benchmark datasets. Per-object and per-area completeness and correctness results indicate the robustness of the approach and the quality of the reconstructed surfaces and extracted buildings. © 2016 IEEE.; Proceedings - International Conference on Image Processing, ICIP
- Publisher
- IEEE Computer Society
- Relation
- 23rd IEEE International Conference on Image Processing, ICIP 2016; Phoenix, United States; 25th-28th September 2016; published in Proceedings - International Conferenec on Image Processing, ICIP Vol. 2016-August, p. 859-863
- Rights
- Copyright © 2016 IEEE.
- Rights
- This metadata is freely available under a CCO license
- Subject
- Detection; Reconstruction; Segmentation; Error detection; Image reconstruction; Image segmentation; Principal component analysis; Roofs; Surface reconstruction; Anisotropic surfaces; Benchmark datasets; Building detection; Point cloud segmentation; Reconstructed surfaces; Segmentation results; Segmentation techniques; Surface curvatures; Image processing
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