Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing . vol 83 n° 2Paru le : 01/02/2017 |
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Ajouter le résultat dans votre panierAerial lidar point cloud voxelization with its 3D ground filtering application / Liying Wang in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)
[article]
Titre : Aerial lidar point cloud voxelization with its 3D ground filtering application Type de document : Article/Communication Auteurs : Liying Wang, Auteur ; Yan Xu, Auteur ; Yu Li, Auteur Année de publication : 2017 Article en page(s) : pp 95 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] adjacence
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] filtrage de points
[Termes IGN] modèle conceptuel de données
[Termes IGN] semis de points
[Termes IGN] visualisation 3D
[Termes IGN] voxelRésumé : (Auteur) Compared to raster grid, Triangulated Irregular Network (TIN), and point cloud, the benefit of voxel representation lies in that the implicit notion of adjacency and the true 3D representation can be presented simultaneously. A binary voxel-based data (BVD) model is proposed to reconstruct aerial lidar point cloud and based on the constructed model 3D ground filtering (V3GF) is developed for separating ground points from unground ones. The proposed V3GF algorithm selects the lowest voxels with a value of 1 as ground seeds and then labels them and their 3D connected set as ground voxels. The ISPRS benchmark dataset are used to compare the performance of V3GF with those of eight other publicized filtering methods. Results indicate that the V3GF improves on Axelsson's performance on five samples in terms of total error. The average Kappa coefficients for sites with relatively flat urban areas, rough slope and discontinuous surfaces are 92.49 percent, 72.23 percent and 61.27 percent, respectively. Numéro de notice : A2017-038 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.2.95 En ligne : https://doi.org/10.14358/PERS.83.2.95 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84139
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 2 (February 2017) . - pp 95 - 107[article]On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings / Madhurima Bandyopadhyay in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 2 (February 2017)
[article]
Titre : On the fusion of lidar and aerial color imagery to detect urban vegetation and buildings Type de document : Article/Communication Auteurs : Madhurima Bandyopadhyay, Auteur ; Jan Van Aardt, Auteur ; Kerry Cawse-Nicholson, Auteur ; Emmett Lentilucci, Auteur Année de publication : 2017 Article en page(s) : pp 123 - 136 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de la végétation
[Termes IGN] fusion de données
[Termes IGN] image aérienne
[Termes IGN] image en couleur
[Termes IGN] image RVB
[Termes IGN] zone urbaineRésumé : (Auteur) Three-dimensional (3D) data from light detection and ranging (lidar) sensor have proven advantageous in the remote sensing domain for characterization of object structure and dimensions. Fusion-based approaches of lidar and aerial imagery also becoming popular. In this study, aerial color (RGB) imagery, along with co-registered airborne discrete lidar data were used to separate vegetation and buildings from other urban classes/cover-types, as a precursory step towards the assessment of urban forest biomass. Both spectral and structural features such as object height, distribution of surface normals from the lidar, and a novel vegetation metric derived from combined lidar and RGB imagery, referred to as the lidar-infused vegetation index (LDVI) were used in this classification method. The proposed algorithm was tested on different cityscape regions to verify its robustness. Results showed a good separation of buildings and vegetation from other urban classes with on average an overall classification accuracy of 92 percent, with a kappa statistic of 0.85. These results bode well for the operational fusion of lidar and RGB imagery, often flown on the same platform, towards improved characterization of the urban forest and built environments. Numéro de notice : A2017-039 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.2.123 En ligne : https://doi.org/10.14358/PERS.83.2.123 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84140
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 2 (February 2017) . - pp 123 - 136[article]