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Auteur Norbert Pfeifer |
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Affiner la recherche Interroger des sources externesA voxel-based method for the three-dimensional modelling of heathland from lidar point clouds: first results / N. Homainejad in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-3-2022 (2022 edition)
Titre : A voxel-based method for the three-dimensional modelling of heathland from lidar point clouds: first results Type de document : Article/Communication Auteurs : N. Homainejad, Auteur ; Sisi Zlatanova, Auteur ; Norbert Pfeifer, Auteur Année de publication : 2022 Article en page(s) : pp 697 - 704 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] canopée
[Termes IGN] classification par nuées dynamiques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] incendie de forêt
[Termes IGN] lande
[Termes IGN] modélisation 3D
[Termes IGN] Nouvelle-Galles du Sud
[Termes IGN] segmentation en régions
[Termes IGN] semis de points
[Termes IGN] voxelRésumé : (auteur) Bushfires are an intrinsic part of the New South Wales’ (NSW) environment in Australia, especially in the Blue Mountains region (11400km2), that is dominated by fire prone vegetation that includes heathland. Many of the Australian native plants in this region are fire-prone and combustible, and many species even require fire to regenerate. The classification of the lateral and vertical distribution of living vegetation is necessary to manage the complexity of bushfires. Currently, interpretation of aerial and satellite images is the prevalent method for the classification of vegetation in NSW. The result does not represent important vegetation structural attributes, such as vegetation height, subcanopy height, and destiny. This paper presents an automated method for the three-dimensional modelling of heathland and important heathland parameters, such as heath shrub height and continuity, and sparse tree and mallee height and density in support of bushfire behaviour modelling. For this study airborne lidar point clouds with a density of 120 points per square meter are used. For the processing and modelling the study is divided into a point cloud processing phase and a voxel-based modelling phase. The point cloud processing phase consists of the normalisation of the height and extraction of the above ground vegetation, while the voxel phase consists of seeded region growing for segmentation, and K-means clustering for the classification of the vegetation into three different canopy layers: a) heath shrubs, b) sparse trees and mallee, c) tall trees. Numéro de notice : A2022-436 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.5194/isprs-annals-V-3-2022-697-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-3-2022-697-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100783
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-3-2022 (2022 edition) . - pp 697 - 704[article]
Titre : 3D modeling of urban area based on oblique UAS images - An end-to-end pipeline Type de document : Article/Communication Auteurs : Valeria-Ersilia Oniga, Auteur ; Ana-Ioana Breaban, Auteur ; Norbert Pfeifer, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 422 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] Bâti-3D
[Termes IGN] CityGML
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] image aérienne oblique
[Termes IGN] image captée par drone
[Termes IGN] indice de végétation
[Termes IGN] lasergrammétrie
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation 3D
[Termes IGN] point d'appui
[Termes IGN] Roumanie
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] zone urbaineRésumé : (auteur) 3D modelling of urban areas is an attractive and active research topic, as 3D digital models of cities are becoming increasingly common for urban management as a consequence of the constantly growing number of people living in cities. Viewed as a digital representation of the Earth’s surface, an urban area modeled in 3D includes objects such as buildings, trees, vegetation and other anthropogenic structures, highlighting the buildings as the most prominent category. A city’s 3D model can be created based on different data sources, especially LiDAR or photogrammetric point clouds. This paper’s aim is to provide an end-to-end pipeline for 3D building modeling based on oblique UAS images only, the result being a parametrized 3D model with the Open Geospatial Consortium (OGC) CityGML standard, Level of Detail 2 (LOD2). For this purpose, a flight over an urban area of about 20.6 ha has been taken with a low-cost UAS, i.e., a DJI Phantom 4 Pro Professional (P4P), at 100 m height. The resulting UAS point cloud with the best scenario, i.e., 45 Ground Control Points (GCP), has been processed as follows: filtering to extract the ground points using two algorithms, CSF and terrain-mark; classification, using two methods, based on attributes only and a random forest machine learning algorithm; segmentation using local homogeneity implemented into Opals software; plane creation based on a region-growing algorithm; and plane editing and 3D model reconstruction based on piece-wise intersection of planar faces. The classification performed with ~35% training data and 31 attributes showed that the Visible-band difference vegetation index (VDVI) is a key attribute and 77% of the data was classified using only five attributes. The global accuracy for each modeled building through the workflow proposed in this study was around 0.15 m, so it can be concluded that the proposed pipeline is reliable. Numéro de notice : A2022-101 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article DOI : 10.3390/rs14020422 Date de publication en ligne : 17/01/2022 En ligne : https://doi.org/10.3390/rs14020422 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99566
in Remote sensing > vol 14 n° 2 (January-2 2022) . - n° 422[article]
Titre : Improving LiDAR classification accuracy by contextual label smoothing in post-processing Type de document : Article/Communication Auteurs : Nan Li, Auteur ; Chun Liu, Auteur ; Norbert Pfeifer, Auteur Année de publication : 2019 Article en page(s) : pp 13 - 31 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] graphe
[Termes IGN] lissage de valeur
[Termes IGN] post-traitement
[Termes IGN] précision de la classification
[Termes IGN] régularisation
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (Auteur) We propose a contextual label-smoothing method to improve the LiDAR classification accuracy in a post-processing step. Under the framework of global graph-structured regularization, we enhance the effectiveness of label smoothing from two aspects. First, each point can collect sufficient label-relevant neighborhood information to verify its label based on an optimal graph. Second, the input label probability set is improved by probabilistic label relaxation to be more consistent with the spatial context. With this optimal graph and reliable label probability set, the final labels are computed by graph-structured regularization. We demonstrate the contextual label-smoothing approach on two separate urban airborne LiDAR datasets with complex urban scenes. Significant improvements in the classification accuracies are achieved without losing small objects (such as façades and cars). The overall accuracy is increased by 7.01% on the Vienna dataset and 6.88% on the Vaihingen dataset. Moreover, most large, wrongly labeled regions are corrected by long-range interactions that are derived from the optimal graph, and misclassified regions that lack neighborhood communications in terms of correct labels are also corrected with the probabilistic label relaxation. Numéro de notice : A2019-069 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.11.022 Date de publication en ligne : 13/12/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.11.022 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92156
in ISPRS Journal of photogrammetry and remote sensing > vol 148 (February 2019) . - pp 13 - 31[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2019021 RAB Revue Centre de documentation En réserve L003 Disponible 081-2019023 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2019022 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt
Titre : International benchmarking of terrestrial laser scanning approaches for forest inventories Type de document : Article/Communication Auteurs : Xinlian Liang, Auteur ; Juha Hyyppä, Auteur ; Harri Kaartinen, Auteur ; Matti Lehtomäki, Auteur ; Jiri Pyorala, Auteur ; Norbert Pfeifer, Auteur ; Markus Holopainen, Auteur ; Gabor Brolly, Auteur ; Francesco Pirotti, Auteur ; Jan Hackenberg Année de publication : 2018 Projets : DIABOLO / Packalen, Tuula Article en page(s) : pp 137 - 179 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] algorithmique
[Termes IGN] benchmark spatial
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] état de l'art
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] lasergrammétrie
[Termes IGN] semis de points
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) The last two decades have witnessed increasing awareness of the potential of terrestrial laser scanning (TLS) in forest applications in both public and commercial sectors, along with tremendous research efforts and progress. It is time to inspect the achievements of and the remaining barriers to TLS-based forest investigations, so further research and application are clearly orientated in operational uses of TLS. In such context, the international TLS benchmarking project was launched in 2014 by the European Spatial Data Research Organization and coordinated by the Finnish Geospatial Research Institute. The main objectives of this benchmarking study are to evaluate the potential of applying TLS in characterizing forests, to clarify the strengths and the weaknesses of TLS as a measure of forest digitization, and to reveal the capability of recent algorithms for tree-attribute extraction. The project is designed to benchmark the TLS algorithms by processing identical TLS datasets for a standardized set of forest attribute criteria and by evaluating the results through a common procedure respecting reliable references. Benchmarking results reflect large variances in estimating accuracies, which were unveiled through the 18 compared algorithms and through the evaluation framework, i.e., forest complexity categories, TLS data acquisition approaches, tree attributes and evaluation procedures. The evaluation framework includes three new criteria proposed in this benchmarking and the algorithm performances are investigated through combining two or more criteria (e.g., the accuracy of the individual tree attributes are inspected in conjunction with plot-level completeness) in order to reveal algorithms’ overall performance. The results also reveal some best available forest attribute estimates at this time, which clarify the status quo of TLS-based forest investigations. Some results are well expected, while some are new, e.g., the variances of estimating accuracies between single-/multi-scan, the principle of the algorithm designs and the possibility of a computer outperforming human operation. With single-scan data, i.e., one hemispherical scan per plot, most of the recent algorithms are capable of achieving stem detection with approximately 75% completeness and 90% correctness in the easy forest stands (easy plots: 600 stems/ha, 20 cm mean DBH). The detection rate decreases when the stem density increases and the average DBH decreases, i.e., 60% completeness with 90% correctness (medium plots: 1000 stem/ha, 15 cm mean DBH) and 30% completeness with 90% correctness (difficult plots: 2000 stems/ha, 10 cm mean DBH). The application of the multi-scan approach, i.e., five scans per plot at the center and four quadrant angles, is more effective in complex stands, increasing the completeness to approximately 90% for medium plots and to approximately 70% for difficult plots, with almost 100% correctness. The results of this benchmarking also show that the TLS-based approaches can provide the estimates of the DBH and the stem curve at a 1–2 cm accuracy that are close to what is required in practical applications, e.g., national forest inventories (NFIs). In terms of algorithm development, a high level of automation is a commonly shared standard, but a bottleneck occurs at stem detection and tree height estimation, especially in multilayer and dense forest stands. The greatest challenge is that even with the multi-scan approach, it is still hard to completely and accurately record stems of all trees in a plot due to the occlusion effects of the trees and bushes in forests. Future development must address the redundant yet incomplete point clouds of forest sample plots and recognize trees more accurately and efficiently. It is worth noting that TLS currently provides the best quality terrestrial point clouds in comparison with all other technologies, meaning that all the benchmarks labeled in this paper can also serve as a reference for other terrestrial point clouds sources. Numéro de notice : A2018-400 Affiliation des auteurs : LIF+Ext (2012-2019) Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.021 Date de publication en ligne : 24/07/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90829
in ISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 137 - 179[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2018101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018103 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt
Titre : Rigorous strip adjustment of UAV-based laserscanning data including time-dependent correction of trajectory errors Type de document : Article/Communication Auteurs : Philipp Glira, Auteur ; Norbert Pfeifer, Auteur ; Gottfried Mandlburger, Auteur Année de publication : 2016 Article en page(s) : pp 945 - 954 Note générale : biblographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] compensation
[Termes IGN] drone
[Termes IGN] erreur systématique
[Termes IGN] espace-temps
[Termes IGN] étalonnage de capteur (imagerie)
[Termes IGN] orientation du capteur
[Termes IGN] paramètre de temps
[Termes IGN] semis de pointsRésumé : (auteur) A new generation of laser scanners mounted on Unmanned Aerial Vehicles (UAVs) have the potential to provide high-quality point clouds of comparatively small areas (a few hectares). The high maneuverability of the UAVs, a typically large field of view of the laser scanners, and a comparatively small measurement range lead to point clouds with very high point density, less occlusions, and low measurement noise. However, due to the limited payload of UAVs, lightweight navigation sensors with a moderate level of accuracy are used to estimate the platform's trajectory. As a consequence, the georeferencing quality of the point clouds is usually sub-optimal; for this, strip adjustment can be performed. The main goal of strip adjustment is to simultaneously optimize the relative and absolute orientation of the strip-wise collected point clouds. This is done by fully re-calibrating the laser scanning system and by correcting systematic measurement errors of the trajectory. In this paper, we extend our previous work on the topic of strip adjustment by the estimation of time-dependent trajectory errors. The errors are thereby modelled by natural cubic splines with constant segment length in time domain. First results confirm the suitability of this flexible correction model by reducing the relative and absolute strip discrepancies to 1.38 cm and 1.65 cm, respectively. Numéro de notice : A2016-983 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.12.945 En ligne : https://doi.org/10.14358/PERS.82.12.945 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83699
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 12 (December 2016) . - pp 945 - 954[article]vol 63 n° 1 - January - February 2008 - Terrestrial laser scanning (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Derek D. Lichti
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