dc.contributor.author | Dey, Emon Kumar | |
dc.contributor.author | Awrangjeb, Mohammad | |
dc.contributor.author | Stantic, Bela | |
dc.date.accessioned | 2020-09-14T02:44:57Z | |
dc.date.available | 2020-09-14T02:44:57Z | |
dc.date.issued | 2020 | |
dc.identifier.issn | 0143-1161 | |
dc.identifier.doi | 10.1080/01431161.2020.1737339 | |
dc.identifier.uri | http://hdl.handle.net/10072/397432 | |
dc.description.abstract | Individual roof plane extraction from Light Detection and Ranging (LiDAR) point-cloud data is a complex and difficult task because of unknown semantic characteristics and inharmonious behaviour of input data. Most of the existing state-of-the-art methods fail to detect small true roof planes with exact boundaries due to outliers, occlusions, complex building structures, and other inconsistent nature of LiDAR data. In this paper, we have presented an improved building detection and roof plane extraction method, which is less sensitive to the outliers and unlikely to generate spurious planes. For this, a robust outlier detection algorithm has been proposed in this paper along with a robust plane-fitting algorithm based on M-estimator SAmple Consensus (MSAC) for detecting individual roof planes. Using two benchmark datasets (Australian and International Society for Photogrammetry and Remote Sensing benchmark) with different numbers of buildings and sizes, trees and point densities, we have evaluated the proposed method. Experimental results show that the method removes outliers and vegetation almost accurately and offers a high success rate in terms of completeness and correctness (between 80% and 100% per-object) for both roof plane extraction and building detection. In most of the cases, the proposed method shows above 90% correctness. | |
dc.description.peerreviewed | Yes | |
dc.language | English | |
dc.language.iso | eng | |
dc.publisher | Taylor & Francis Group | |
dc.relation.ispartofpagefrom | 6325 | |
dc.relation.ispartofpageto | 6354 | |
dc.relation.ispartofissue | 16 | |
dc.relation.ispartofjournal | International Journal of Remote Sensing | |
dc.relation.ispartofvolume | 41 | |
dc.subject.fieldofresearch | Physical geography and environmental geoscience | |
dc.subject.fieldofresearch | Geomatic engineering | |
dc.subject.fieldofresearch | Geophysics | |
dc.subject.fieldofresearchcode | 3709 | |
dc.subject.fieldofresearchcode | 4013 | |
dc.subject.fieldofresearchcode | 3706 | |
dc.subject.keywords | Science & Technology | |
dc.subject.keywords | Remote Sensing | |
dc.subject.keywords | Imaging Science & Photographic Technology | |
dc.subject.keywords | 3-D BUILDINGS | |
dc.title | Outlier detection and robust plane fitting for building roof extraction from LiDAR data | |
dc.type | Journal article | |
dc.type.description | C1 - Articles | |
dcterms.bibliographicCitation | Dey, EK; Awrangjeb, M; Stantic, B, Outlier detection and robust plane fitting for building roof extraction from LiDAR data, International Journal of Remote Sensing, 2020, 41 (16), pp. 6325-6354 | |
dc.date.updated | 2020-09-14T01:38:21Z | |
dc.description.version | Accepted Manuscript (AM) | |
gro.rights.copyright | This is an Author's Accepted Manuscript of an article published in the International Journal of Remote Sensing,41 (16), pp. 6325-6354, 09 Jun 2020, copyright Taylor & Francis, available online at: https://doi.org/10.1080/01431161.2020.1737339 | |
gro.hasfulltext | Full Text | |
gro.griffith.author | Awrangjeb, Mohammad | |
gro.griffith.author | Stantic, Bela | |