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Bayesian graph-cut optimization for wall surfaces reconstruction in indoor environments / Georgios-Tsampikos Michailidis in The Visual Computer, vol 33 n° 10 (October 2017)
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Titre : Bayesian graph-cut optimization for wall surfaces reconstruction in indoor environments Type de document : Article/Communication Auteurs : Georgios-Tsampikos Michailidis, Auteur ; Renato Pajarola, Auteur Année de publication : 2017 Article en page(s) : pp 1347 - 1355 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme Graph-Cut
[Termes IGN] carte d'intérieur
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] mur
[Termes IGN] reconstruction d'objet
[Termes IGN] scène intérieure
[Termes IGN] semis de pointsRésumé : (Auteur) In this paper, a new method capable to extract the wall openings (windows and doors) of interior scenes from point clouds under cluttered and occluded environments is presented. For each wall surface extracted by the polyhedral model of a room, our method constructs a cell complex representation, which is used for the wall object segmentation using a graph-cut method. We evaluate the results of the proposed approach on real-world 3D scans of indoor environments and demonstrate its validity. Numéro de notice : A2017-710 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-016-1230-3 En ligne : https://doi.org/10.1007/s00371-016-1230-3 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88091
in The Visual Computer > vol 33 n° 10 (October 2017) . - pp 1347 - 1355[article]Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data / Hooman Latifi in Forestry, an international journal of forest research, vol 90 n° 4 (October 2017)
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Titre : Multi-model estimation of understorey shrub, herb and moss cover in temperate forest stands by laser scanner data Type de document : Article/Communication Auteurs : Hooman Latifi, Auteur ; Steven Hill, Auteur ; Bastian Schumann, Auteur ; Marco Heurich, Auteur ; Stefan Dech, Auteur Année de publication : 2017 Article en page(s) : pp 496 - 514 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] forêt tempérée
[Termes IGN] habitat forestier
[Termes IGN] sous-boisRésumé : (Auteur) In temperate forests, the highest plant richness is regularly found in the understorey, i.e. shrub, tree regeneration, herbal and moss covers, which provides important food and shelter for other plant and animal species. Here, Light Detection And Ranging (LiDAR) remote sensing was investigated as a surrogate to laborious field surveys to improve understanding of the causal and predictive attributes of understorey. We designed a study in which we used a high-density LiDAR point cloud and applied a thinning algorithm to simulate two lower density point clouds including first and last returns and half of the remaining points (half-thinned data) and only first and last returns (F/L-thinned data). From each dataset, several over- and understorey-related statistical metrics were derived. Each of the three sets of LiDAR metrics was then combined with the forest habitat information to estimate the recorded proportions of shrub, herb and moss coverages. We used three different model procedures including zero-and-one-inflated beta regression (ZOINBR), ordinary least squares with logit-transformed response variables (logistic model) and a machine learning random forest (RF) method. The logistic and ZOINBR model results showed highly significant relationships between LiDAR metrics and habitat types in explaining understorey coverage. The highest coefficients of determination included r2 = 0.80 for shrub cover (estimated by F/L-thinned data and ZOINBR model), r2 = 0.53 for herb cover (estimated by half-thinned data and logistic model) and r2 = 0.48 for moss cover (estimated by half-thinned data and logistic model). RF models returned the best predictive performances (i.e. the lowest root mean square errors). Despite slight differences, no substantial difference was observed amongst the performances achieved by the original, half-thinned and F/L-thinned point clouds. Moreover, the ZOINBR models did not improve predictive performances compared with the logistic model, which suggests that the latter should be preferred due to its greater simplicity and parsimony. Despite the differences between our simulated data and the real-world LiDAR point clouds of different point densities, the results of this study are thought to mostly reflect how LiDAR and forest habitat data can be combined for deriving ecologically relevant information on temperate forest understorey vegetation layers. This, in turn, increases the applicability of prediction results for overarching aims such as forest and wildlife management. Numéro de notice : A2017-906 Affiliation des auteurs : non IGN Thématique : FORET/MATHEMATIQUE Nature : Article DOI : 10.1093/forestry/cpw066 Date de publication en ligne : 27/01/2017 En ligne : https://doi.org/10.1093/forestry/cpw066 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93195
in Forestry, an international journal of forest research > vol 90 n° 4 (October 2017) . - pp 496 - 514[article]Registration of images to Lidar and GIS data without establishing explicit correspondences / Gabor Barsai in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 10 (October 2017)
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Titre : Registration of images to Lidar and GIS data without establishing explicit correspondences Type de document : Article/Communication Auteurs : Gabor Barsai, Auteur ; Alper Yilmaz, Auteur ; Sudhagar Nagarajan, Auteur ; Panu Srestasathiern, Auteur Année de publication : 2017 Article en page(s) : pp 705 - 716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] contour
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] fusion d'images
[Termes IGN] image aérienne oblique
[Termes IGN] méthode de réduction d'énergie
[Termes IGN] superposition d'images
[Termes IGN] variable aléatoireRésumé : (auteur) Recovering the camera orientation is a fundamental problem in photogrammetry for precision 3D recovery, orthophoto generation, and image registration. In this paper, we achieve this goal by fusing the image information with information extracted from different modalities, including lidar and GIS. In contrast to other approaches, which require feature correspondences, our approach exploits edges across the modalities without the necessity to explicitly establish correspondences. In the proposed approach, extracted edges from different modalities are not required to have analytical forms. This flexibility is achieved by minimizing a new cost function using a Bayesian approach, which takes the Euclidean distances between the projected edges extracted from the other data source and the edges extracted from the reference image as its random variable. The proposed formulation minimizes the overall distances between the sets of edges iteratively, such that the end product results in the correct camera parameters for the reference image as well as matching features across the modalities. The initial solution can be obtained from GPS/IMU data. The formulation is shown to successfully handle noise and missing observations in edges. Point matching methods may fail for oblique images, especially high oblique images. We eliminate the requirement for exact point-to-point matching. The feasibility of the method is experimented with nadir and oblique images. Numéro de notice : A2017-691 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.10.705 En ligne : https://doi.org/10.14358/PERS.83.10.705 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=87858
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 10 (October 2017) . - pp 705 - 716[article]Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests / Jing Liu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)
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Titre : Significant effect of topographic normalization of airborne LiDAR data on the retrieval of plant area index profile in mountainous forests Type de document : Article/Communication Auteurs : Jing Liu, Auteur ; Andrew K. Skidmore, Auteur ; Marco Heurich, Auteur ; Tiejun Wang, Auteur Année de publication : 2017 Article en page(s) : pp 77 - 87 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Allemagne
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt alpestre
[Termes IGN] hauteur des arbres
[Termes IGN] lever topographique
[Termes IGN] normalisation
[Termes IGN] reliefRésumé : (Auteur) As an important metric for describing vertical forest structure, the plant area index (PAI) profile is used for many applications including biomass estimation and wildlife habitat assessment. PAI profiles can be estimated with the vertically resolved gap fraction from airborne LiDAR data. Most research utilizes a height normalization algorithm to retrieve local or relative height by assuming the terrain to be flat. However, for many forests this assumption is not valid. In this research, the effect of topographic normalization of airborne LiDAR data on the retrieval of PAI profile was studied in a mountainous forest area in Germany. Results show that, although individual tree height may be retained after topographic normalization, the spatial arrangement of trees is changed. Specifically, topographic normalization vertically condenses and distorts the PAI profile, which consequently alters the distribution pattern of plant area density in space. This effect becomes more evident as the slope increases. Furthermore, topographic normalization may also undermine the complexity (i.e., canopy layer number and entropy) of the PAI profile. The decrease in PAI profile complexity is not solely determined by local topography, but is determined by the interaction between local topography and the spatial distribution of each tree. This research demonstrates that when calculating the PAI profile from airborne LiDAR data, local topography needs to be taken into account. We therefore suggest that for ecological applications, such as vertical forest structure analysis and modeling of biodiversity, topographic normalization should not be applied in non-flat areas when using LiDAR data. Numéro de notice : A2017-639 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.005 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86992
in ISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 77 - 87[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2017101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 081-2017103 DEP-EXM Revue Saint-Mandé Dépôt en unité Exclu du prêt Occupancy modelling for moving object detection from Lidar point clouds: A comparative study / Wen Xiao in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-2/W4 (September 2017)
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Titre : Occupancy modelling for moving object detection from Lidar point clouds: A comparative study Type de document : Article/Communication Auteurs : Wen Xiao, Auteur ; Bruno Vallet , Auteur ; Y. Xiao, Auteur ; Jon Mills, Auteur ; Nicolas Paparoditis
, Auteur
Année de publication : 2017 Article en page(s) : pp 171 - 178 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection d'objet
[Termes IGN] détection de changement
[Termes IGN] données lidar
[Termes IGN] grille
[Termes IGN] objet mobile
[Termes IGN] semis de points
[Termes IGN] théorie de Dempster-ShaferRésumé : (auteur) Lidar technology has been widely used in both robotics and geomatics for environment perception and mapping. Moving object detection is important in both fields as it is a fundamental step for collision avoidance, static background extraction, moving pattern analysis, etc. A simple method involves checking directly the distance between nearest points from the compared datasets. However, large distances may be obtained when two datasets have different coverages. The use of occupancy grids is a popular approach to overcome this problem. There are two common theories employed to model occupancy and to interpret the measurements, DempsterShafer theory and probability. This paper presents a comparative study of these two theories for occupancy modelling with the aim of moving object detection from lidar point clouds. Occupancy is modelled using both approaches and their implementations are explained and compared in details. Two lidar datasets are tested to illustrate the moving object detection results Numéro de notice : A2017-913 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-2-W4-171-2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-2-W4-171-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102874
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-2/W4 (September 2017) . - pp 171 - 178[article]Building on firm foundations / Dominik Wesołowski in GEO: Geoconnexion international, vol 16 n° 9 (September 2017)
PermalinkDrones et photogrammétrie : Un outil dans l’ADN de la profession / Benoît Greuzat in Géomètre, n° 2150 (septembre 2017)
PermalinkEffects of using different sources of remote sensing and geographic information system data on urban stormwater 2D–1D modeling / Yi Hong in Applied sciences, vol 7 n° 9 (September 2017)
PermalinkForest canopy height estimation using satellite laser altimetry : a case study in the Western Ghats, India / S.M. Ghosh in Applied geomatics, vol 9 n° 3 (September 2017)
PermalinkImpact of spatial correlations on the surface estimation based on terrestrial laser scanning / Tobias Jurek in Journal of applied geodesy, vol 11 n° 3 (September 2017)
PermalinkPoint cloud refinement with self-calibration of a mobile multibeam lidar sensor / Houssem Nouira in Photogrammetric record, vol 32 n° 159 (September 2017)
PermalinkPrecision estimation of the angular resolution of terrestrial laser scanners / Xijiang Chen in Photogrammetric record, vol 32 n° 159 (September 2017)
PermalinkTerrain model reconstruction from terrestrial LiDAR data using radial basis functions / Jules Morel in IEEE Computer graphics and applications, vol 37 n° 5 ([01/09/2017])
PermalinkUrban building reconstruction from raw LiDAR point data / Cheng Yi in Computer-Aided Design, vol 9x (2017)
PermalinkVisual inspection of fire-damaged concrete based on terrestrial laser scanner data / Wallace Mukupa in Applied geomatics, vol 9 n° 3 (September 2017)
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