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Using a binary space partitioning tree for reconstructing polyhedral building models from airborne Lidar data / Gunho Sohn in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 11 (November 2008)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 11 (November 2008) . - pp 1425 - 1438 Titre : Using a binary space partitioning tree for reconstructing polyhedral building models from airborne Lidar data Type de document : Article/Communication Auteurs : Gunho Sohn, Auteur ; X. Huang, Auteur ; V. Tao, Auteur Année de publication : 2008 Article en page(s) : pp 1425 - 1438 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre-B [Termes IGN] données lidar [Termes IGN] extraction de traits caractéristiques [Termes IGN] modélisation 3D [Termes IGN] polyèdre [Termes IGN] reconstruction 3D du bâti [Termes IGN] toit Résumé : (Auteur) During the past several years, point density covering topographic objects with airborne lidar (Light Detection And Ranging) technology has been greatly improved. This achievement provides an improved ability for reconstructing more complicated building roof structures; more specifically, those comprising various model primitives horizontally and/or vertically. However, the technology for automatically reconstructing such a complicated structure is thus far poorly understood and is currently based on employing a limited number of pre-specified building primitives. This paper addresses this limitation by introducing a new technique of modeling 3D building objects using a data-driven approach whereby densely collecting low-level modeling cues from lidar data are used in the modeling process. The core of the proposed method is to globally reconstruct geometric topology between adjacent linear features by adopting a BSP (Binary Space Partitioning) tree. The proposed algorithm consists of four steps: (a) detecting individual buildings from lidar data, (b) clustering laser points by height and planar similarity, (c) extracting rectilinear lines, and (d) planar partitioning and merging for the generation of polyhedral models. This paper demonstrates the efficacy of the algorithm for creating complex models of building rooftops in 3D space from airborne lidar data. Copyright ASPRS Numéro de notice : A2008-410 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.11.1425 En ligne : https://doi.org/10.14358/PERS.74.11.1425 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29402 [article]

Automatic extraction of main road centrelines from high resolution satellite imagery using hierarchical grouping / Xiangyun Hu in Photogrammetric Engineering & Remote Sensing, PERS, vol 73 n° 9 (September 2007)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 73 n° 9 (September 2007) . - pp 1049 - 1056 Titre : Automatic extraction of main road centrelines from high resolution satellite imagery using hierarchical grouping Type de document : Article/Communication Auteurs : Xiangyun Hu, Auteur ; V. Tao, Auteur Année de publication : 2007 Article en page(s) : pp 1049 - 1056 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] approche hiérarchique [Termes IGN] axe médian [Termes IGN] extraction du réseau routier [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image Quickbird [Termes IGN] zone urbaine Résumé : (Auteur) Automatic road centerline extraction from high-resolution satellite imagery has gained considerable interest recently due to the increasing availability of commercial high-resolution satellite images. In this paper, a hierarchical grouping strategy is proposed to automatically extract main road centerlines from high-resolution satellite imagery. Here hierarchical grouping means that, instead of grouping all segments at once, the selective segments are grouped gradually, and multiple clues are closely integrated into the procedure. By this means, the computational cost can be reduced significantly. Through the stepwise grouping, the detected fragmented line segments eventually form the long main road lines. The proposed method has been tested and validated using several Ikonos and QuickBird images both in open areas and build-up urban environments. The results demonstrate its robustness and viability on extracting salient main road centerlines. Copyright ASPRS Numéro de notice : A2007-413 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : sans En ligne : https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=4c44af1158a711e37 [...] Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28776 [article]

Hierarchical recovery of digital terrain models from single and multiple return lidar data / Y. Hu in Photogrammetric Engineering & Remote Sensing, PERS, vol 71 n° 4 (April 2005)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 71 n° 4 (April 2005) . - pp 425 - 433 Titre : Hierarchical recovery of digital terrain models from single and multiple return lidar data Type de document : Article/Communication Auteurs : Y. Hu, Auteur ; V. Tao, Auteur Année de publication : 2005 Article en page(s) : pp 425 - 433 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] approche hiérarchique [Termes IGN] classification par seuillage sur la limite la plus proche [Termes IGN] convolution (signal) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] interpolation [Termes IGN] lasergrammétrie [Termes IGN] modèle numérique de terrain Résumé : (Auteur) A hierarchical terrain recovery approach for generating digital terrain models (DTM) from single and multiple returns lidar data is presented in this paper. The algorithm can intelligently discriminate between terrain and non-terrain points by using adaptive and robust filtering and interpolation techniques. It processes the image pyramid, bottom-up and top-down, to estimate high-quality terrain surfaces from lidar data with varying point densities and scene complexities. Using road and vegetation information, the algorithm is able to adaptively adjust thresholds to be suited to process changing contents in a large scene. The algorithms have been tested extensively using multiple medium - and high - resolution lidar datasets. The worst-case error is below 25 cm Linear Error (LE) 90 comparing the derived DTMs and the raw range images on bare surfaces when testing several lidar datasets. Copyright ASPRS Numéro de notice : A2005-589 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : https://doi.org/10.14358/PERS.71.4.425 En ligne : https://doi.org/10.14358/PERS.71.4.425 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27724 [article]