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Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data / Abdul Nurunnabi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 4 (April 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 4 (April 2016) . - pp 2181 - 2193 Titre : Robust locally weighted regression techniques for ground surface points filtering in mobile laser scanning three dimensional point cloud data Type de document : Article/Communication Auteurs : Abdul Nurunnabi, Auteur ; Geoff West, Auteur ; David Belton, Auteur Année de publication : 2016 Article en page(s) : pp 2181 - 2193 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] ajustement de paramètres [Termes IGN] algorithme de filtrage [Termes IGN] analyse comparative [Termes IGN] analyse de données [Termes IGN] extraction de points [Termes IGN] régression [Termes IGN] segmentation d'image [Termes IGN] semis de points [Termes IGN] télémétrie laser mobile [Termes IGN] zone urbaine Résumé : (Auteur) This paper introduces robust algorithms for extracting the ground points in laser scanning 3-D point cloud data. Global polynomial functions have been used for filtering algorithms for point cloud data; however, it is not suitable as it may lead to bias for the filtering algorithms and can cause misclassification errors when many different objects are present. In this paper, robust statistical approaches are coupled with locally weighted 2-D regression that fits without any predefined global function for the variables of interest. Algorithms are performed iteratively on 2-D profiles: x - z and y - z. The z (elevation) values are robustly down weighted based on the residuals for the fitted points. The new set of down-weighted z values, along with the corresponding x (or y) values, is used to get a new fit for the lower surface level. The process of fitting and down weighting continues until the difference between two consecutive fits is insignificant. The final fit is the required ground level, and the ground surface points are those that fall within the ground level and the level after adding some threshold value with the ground level for z values. Experimental results are compared with the recently proposed segmentation method through simulated and real mobile laser scanning point clouds from urban areas that include many objects that appear in road scenes such as short walls, large buildings, electric poles, signposts, and cars. Results show that the proposed robust methods efficiently extract ground surface points with better than 97% accuracy. Numéro de notice : A2016-840 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2496972 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2496972 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82884 [article]