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Segmentation and reconstruction of polyhedral building roofs from aerial lidar points clouds / A. Sampath in IEEE Transactions on geoscience and remote sensing, vol 48 n° 3 Tome 2 (March 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 3 Tome 2 (March 2010) . - pp 1554 - 1567 Titre : Segmentation and reconstruction of polyhedral building roofs from aerial lidar points clouds Type de document : Article/Communication Auteurs : A. Sampath, Auteur ; J. Shan, Auteur Année de publication : 2010 Article en page(s) : pp 1554 - 1567 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse de groupement [Termes IGN] cohérence géométrique [Termes IGN] détection du bâti [Termes IGN] diagramme de Voronoï [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] intégrité topologique [Termes IGN] reconstruction 3D du bâti [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] similitude [Termes IGN] toit Résumé : (Auteur) This paper presents a solution framework for the segmentation and reconstruction of polyhedral building roofs from aerial LIght Detection And Ranging (lidar) point clouds. The eigenanalysis is first carried out for each roof point of a building within its Voronoi neighborhood. Such analysis not only yields the surface normal for each lidar point but also separates the lidar points into planar and nonplanar ones. In the second step, the surface normals of all planar points are clustered with the fuzzy k-means method. To optimize this clustering process, a potential-based approach is used to estimate the number of clusters, while considering both geometry and topology for the cluster similarity. The final step of segmentation separates the parallel and coplanar segments based on their distances and connectivity, respectively. Building reconstruction starts with forming an adjacency matrix that represents the connectivity of the segmented planar segments. A roof interior vertex is determined by intersecting all planar segments that meet at one point, whereas constraints in the form of vertical walls or boundary are applied to determine the vertices on the building outline. Finally, an extended boundary regularization approach is developed based on multiple parallel and perpendicular line pairs to achieve topologically consistent and geometrically correct building models. This paper describes the detail principles and implementation steps for the aforementioned solution framework. Results of a number of buildings with diverse roof complexities are presented and evaluated. Numéro de notice : A2010-272 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2009.2030180 Date de publication en ligne : 03/11/2009 En ligne : https://doi.org/10.1109/TGRS.2009.2030180 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30466 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2010031B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Terrain modeling from Lidar range data in natural landscapes: a predictive and Bayesian framework / Frédéric Bretar in IEEE Transactions on geoscience and remote sensing, vol 48 n° 3 Tome 2 (March 2010)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 48 n° 3 Tome 2 (March 2010) . - pp 1568 - 1578 Titre : Terrain modeling from Lidar range data in natural landscapes: a predictive and Bayesian framework Type de document : Article/Communication Auteurs : Frédéric Bretar, Auteur ; Nesrine Chehata , Auteur Année de publication : 2010 Article en page(s) : pp 1568 - 1578 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] altimètre laser [Termes IGN] classification bayesienne [Termes IGN] données lidar [Termes IGN] filtre de Kalman [Termes IGN] modèle numérique de terrain [Termes IGN] montagne [Termes IGN] semis de points Résumé : (Auteur) The Earth's topography, including vegetation and human-made features, reduced to a virtual 3-D representation is a key geographic layer for any extended development or risk management project. Processed from multiple aerial images or from airborne lidar systems, the 3-D topography is first represented as a point cloud. This paper deals with the generation of digital terrain models (DTMs) in natural landscapes. We present a global methodology for estimating the terrain height by deriving a predictive filter paradigm. Under the assumption that the terrain topography (elevation and slope) is regular in a neighboring system, a predictive filter combines linearly the predicted topographic values and the effective measured values. In this paper, such a filter is applied to 3-D lidar data which are known to be of high elevation accuracy. The algorithm generates an adaptive local geometry wherein the elevation distribution of the point cloud is analyzed. Since local terrain elevations depend on the local slope, a predictive filter is first applied on the slopes and then on the terrain elevations. The algorithm propagates through the point cloud following specific rules in order to optimize the probability of computing areas containing terrain points. Considered as an initial surface, the previous DTM is finally regularized in a Bayesian framework. Our approach is based on the definition of an energy function that manages the evolution of a terrain surface. The energy is designed as a compromise between a data attraction term and a regularization term. The minimum of this energy corresponds to the final terrain surface. The methodology is discussed, and some conclusive results are presented on vegetated mountainous areas. Numéro de notice : A2010-273 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2009.2032653 Date de publication en ligne : 04/12/2009 En ligne : https://doi.org/10.1109/TGRS.2009.2032653 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30467 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2010031B	RAB	Revue	Centre de documentation	En réserve L003	Disponible