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Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds / Longjie Ye in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 9 (September 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 9 (September 2021) . - pp 615 - 630 Titre : Gaussian mixture model of ground filtering based on hierarchical curvature constraints for airborne Lidar point clouds Type de document : Article/Communication Auteurs : Longjie Ye, Auteur ; Ka Zhang, Auteur ; Wen Xiao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 615 - 630 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] algorithme de filtrage [Termes IGN] classification barycentrique [Termes IGN] courbure [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] fonction spline d'interpolation [Termes IGN] Kappa de Cohen [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de terrain [Termes IGN] processus gaussien [Termes IGN] semis de points Résumé : (Auteur) This paper proposes a Gaussian mixture model of a ground filtering method based on hierarchical curvature constraints. Firstly, the thin plate spline function is iteratively applied to interpolate the reference surface. Secondly, gradually changing grid size and curvature threshold are used to construct hierarchical constraints. Finally, an adaptive height difference classifier based on the Gaussian mixture model is proposed. Using the latent variables obtained by the expectation-maximization algorithm, the posterior probability of each point is computed. As a result, ground and objects can be marked separately according to the calculated possibility. 15 data samples provided by the International Society for Photogrammetry and Remote Sensing are used to verify the proposed method, which is also compared with eight classical filtering algorithms. Experimental results demonstrate that the average total errors and average Cohen's kappa coefficient of the proposed method are 6.91% and 80.9%, respectively. In general, it has better performance in areas with terrain discontinuities and bridges. Numéro de notice : A2021-671 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.20-00080 Date de publication en ligne : 01/09/2021 En ligne : https://doi.org/10.14358/PERS.87.20-00080 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98820 [article]

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