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A feature-preserving point cloud denoising algorithm for LiDAR-derived DEM construction / Chuanfa Chen in Survey review, Vol 53 n° 377 (February 2021)
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Titre : A feature-preserving point cloud denoising algorithm for LiDAR-derived DEM construction Type de document : Article/Communication Auteurs : Chuanfa Chen, Auteur ; Yuan Gao, Auteur ; Yanyan Li, Auteur Année de publication : 2021 Article en page(s) : pp146 - 157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] algorithme de filtrage
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 3D
[Termes descripteurs IGN] filtrage de points
[Termes descripteurs IGN] filtrage du bruit
[Termes descripteurs IGN] interpolation
[Termes descripteurs IGN] modèle numérique de surface
[Termes descripteurs IGN] semis de pointsRésumé : (auteur) To attenuate positional errors of LiDAR-derived datasets for constructing digital elevation models (DEMs), a feature-preserving point denoising algorithm (F-PDA) is developed in this paper. F-PDA includes three main steps: surface normal estimation, normal filtering and point position update. Numerical tests with two simulated surfaces indicate that F-PDA is always more accurate than kriging and natural neighbour. Furthermore, F-PDA has a high effectiveness of preserving feature lines. Real-world examples of interpolating LiDAR samples demonstrate that F-PDA can best retain both prominent and subtle terrain features, while faithfully removing errors in mountainous and flat regions. Moreover, it outperforms some well-known interpolation methods. Numéro de notice : A2021-235 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2019.1704562u date de publication en ligne : 23/12/2019 En ligne : https://doi.org/10.1080/00396265.2019.1704562u Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97242
in Survey review > Vol 53 n° 377 (February 2021) . - pp146 - 157[article]Improving trajectory estimation using 3D city models and kinematic point clouds / Lucas Lucks in Transactions in GIS, Vol 25 n° 1 (February 2021)
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Titre : Improving trajectory estimation using 3D city models and kinematic point clouds Type de document : Article/Communication Auteurs : Lucas Lucks, Auteur ; Lasse Klingbeil, Auteur ; Lutz Plümer, Auteur ; Youness Dehbi, Auteur Année de publication : 2021 Article en page(s) : pp 238 - 260 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes descripteurs IGN] algorithme ICP
[Termes descripteurs IGN] bruit (théorie du signal)
[Termes descripteurs IGN] centrale inertielle
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 3D
[Termes descripteurs IGN] interpolation
[Termes descripteurs IGN] milieu urbain
[Termes descripteurs IGN] modèle 3D de l'espace urbain
[Termes descripteurs IGN] modèle sémantique de données
[Termes descripteurs IGN] navigation autonome
[Termes descripteurs IGN] semis de pointsRésumé : (Auteur) Accurate and robust positioning of vehicles in urban environments is of high importance for autonomous driving or mobile mapping. In mobile mapping systems, a simultaneous mapping of the environment using laser scanning and an accurate positioning using global navigation satellite systems are targeted. This requirement is often not guaranteed in shadowed cities where global navigation satellite system signals are usually disturbed, weak or even unavailable. We propose a novel approach which incorporates prior knowledge (i.e., a 3D city model of the environment) and improves the trajectory. The recorded point cloud is matched with the semantic city model using a point‐to‐plane iterative closest point method. A pre‐classification step enables an informed sampling of appropriate matching points. Random forest is used as classifier to discriminate between facade and remaining points. Local inconsistencies are tackled by a segmentwise partitioning of the point cloud where an interpolation guarantees a seamless transition between the segments. The general applicability of the method implemented is demonstrated on an inner‐city data set recorded with a mobile mapping system. Numéro de notice : A2021-188 Affiliation des auteurs : non IGN Thématique : IMAGERIE/POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12719 date de publication en ligne : 02/01/2021 En ligne : https://doi.org/10.1111/tgis.12719 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97157
in Transactions in GIS > Vol 25 n° 1 (February 2021) . - pp 238 - 260[article]Monitoring the coastal changes of the Po river delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: implications for coastline changes in 2100 A.D. / Massimo Fabris in Remote sensing, Vol 13 n° 3 (February 2021)
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Titre : Monitoring the coastal changes of the Po river delta (Northern Italy) since 1911 using archival cartography, multi-temporal aerial photogrammetry and LiDAR data: implications for coastline changes in 2100 A.D. Type de document : Article/Communication Auteurs : Massimo Fabris, Auteur Année de publication : 2021 Article en page(s) : n° 529 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes descripteurs IGN] analyse spatio-temporelle
[Termes descripteurs IGN] archives
[Termes descripteurs IGN] cartographie ancienne
[Termes descripteurs IGN] détection de changement
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données multitemporelles
[Termes descripteurs IGN] modèle numérique de terrain
[Termes descripteurs IGN] montée du niveau de la mer
[Termes descripteurs IGN] photogrammétrie aérienne
[Termes descripteurs IGN] Pô (delta)
[Termes descripteurs IGN] semis de points
[Termes descripteurs IGN] surveillance du littoral
[Termes descripteurs IGN] trait de côteRésumé : (auteur) Interaction between land subsidence and sea level rise (SLR) increases the hazard in coastal areas, mainly for deltas, characterized by flat topography and with great social, ecological, and economic value. Coastal areas need continuous monitoring as a support for human intervention to reduce the hazard. Po River Delta (PRD, northern Italy) in the past was affected by high values of artificial land subsidence: even if at low rates, anthropogenic settlements are currently still in progress and produce an increase of hydraulic risk due to the loss of surface elevation both of ground and levees. Many authors have provided scenarios for the next decades with increased flooding in densely populated areas. In this work, a contribution to the understanding future scenarios based on the morphological changes that occurred in the last century on the PRD coastal area is provided: planimetric variations are reconstructed using two archival cartographies (1911 and 1924), 12 multi-temporal high-resolution aerial photogrammetric surveys (1933, 1944, 1949, 1955, 1962, 1969, 1977, 1983, 1990, 1999, 2008, and 2014), and four LiDAR (light detection and ranging) datasets (acquired in 2006, 2009, 2012, and 2018): obtained results, in terms of emerged surfaces variations, are linked to the available land subsidence rates (provided by leveling, GPS—global positioning system, and SAR—synthetic aperture radar data) and to the expected SLR values, to perform scenarios of the area by 2100: results of this work will be useful to mitigate the hazard by increasing defense systems and preventing the risk of widespread flooding. Numéro de notice : A2021-199 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13030529 date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/rs13030529 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97151
in Remote sensing > Vol 13 n° 3 (February 2021) . - n° 529[article]Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning / Maryam Pourshamsi in ISPRS Journal of photogrammetry and remote sensing, vol 172 (February 2021)
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Titre : Tropical forest canopy height estimation from combined polarimetric SAR and LiDAR using machine-learning Type de document : Article/Communication Auteurs : Maryam Pourshamsi, Auteur ; Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 79 - 94 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes descripteurs IGN] apprentissage automatique
[Termes descripteurs IGN] bande L
[Termes descripteurs IGN] canopée
[Termes descripteurs IGN] classification par forêts aléatoires
[Termes descripteurs IGN] classification par séparateurs à vaste marge
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données polarimétriques
[Termes descripteurs IGN] forêt tropicale
[Termes descripteurs IGN] Gabon
[Termes descripteurs IGN] hauteur des arbres
[Termes descripteurs IGN] image captée par drone
[Termes descripteurs IGN] image radar moirée
[Termes descripteurs IGN] Rotation Forest classification
[Termes descripteurs IGN] semis de pointsRésumé : (auteur) Forest height is an important forest biophysical parameter which is used to derive important information about forest ecosystems, such as forest above ground biomass. In this paper, the potential of combining Polarimetric Synthetic Aperture Radar (PolSAR) variables with LiDAR measurements for forest height estimation is investigated. This will be conducted using different machine learning algorithms including Random Forest (RFs), Rotation Forest (RoFs), Canonical Correlation Forest (CCFs) and Support Vector Machine (SVMs). Various PolSAR parameters are required as input variables to ensure a successful height retrieval across different forest heights ranges. The algorithms are trained with 5000 LiDAR samples (less than 1% of the full scene) and different polarimetric variables. To examine the dependency of the algorithm on input training samples, three different subsets are identified which each includes different features: subset 1 is quiet diverse and includes non-vegetated region, short/sparse vegetation (0–20 m), vegetation with mid-range height (20–40 m) to tall/dense ones (40–60 m); subset 2 covers mostly the dense vegetated area with height ranges 40–60 m; and subset 3 mostly covers the non-vegetated to short/sparse vegetation (0–20 m) .The trained algorithms were used to estimate the height for the areas outside the identified subset. The results were validated with independent samples of LiDAR-derived height showing high accuracy (with the average R2 = 0.70 and RMSE = 10 m between all the algorithms and different training samples). The results confirm that it is possible to estimate forest canopy height using PolSAR parameters together with a small coverage of LiDAR height as training data. Numéro de notice : A2021-086 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.11.008 date de publication en ligne : 19/12/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.11.008 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96846
in ISPRS Journal of photogrammetry and remote sensing > vol 172 (February 2021) . - pp 79 - 94[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2021021 SL Revue Centre de documentation Revues en salle Disponible 081-2021022 DEP-RECF Revue Nancy Bibliothèque Nancy IFN Disponible A density-based algorithm for the detection of individual trees from LiDAR data / Melissa Latella in Remote sensing, Vol 13 n° 2 (January 2021)
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Titre : A density-based algorithm for the detection of individual trees from LiDAR data Type de document : Article/Communication Auteurs : Melissa Latella, Auteur ; Fabio Sola, Auteur ; Carlo Camporeal, Auteur Année de publication : 2021 Article en page(s) : n° 322 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes descripteurs IGN] arbre (flore)
[Termes descripteurs IGN] comptage
[Termes descripteurs IGN] densité de la végétation
[Termes descripteurs IGN] détection d'arbres
[Termes descripteurs IGN] distribution spatiale
[Termes descripteurs IGN] données lidar
[Termes descripteurs IGN] données localisées 3D
[Termes descripteurs IGN] forêt de feuillus
[Termes descripteurs IGN] hauteur des arbres
[Termes descripteurs IGN] inventaire forestier (techniques et méthodes)
[Termes descripteurs IGN] semis de points
[Termes descripteurs IGN] sous-étageRésumé : (auteur) Nowadays, LiDAR is widely used for individual tree detection, usually providing higher accuracy in coniferous stands than in deciduous ones, where the rounded-crown, the presence of understory vegetation, and the random spatial tree distribution may affect the identification algorithms. In this work, we propose a novel algorithm that aims to overcome these difficulties and yield the coordinates and the height of the individual trees on the basis of the point density features of the input point cloud. The algorithm was tested on twelve deciduous areas, assessing its performance on both regular-patterned plantations and stands with randomly distributed trees. For all cases, the algorithm provides high accuracy tree count (F-score > 0.7) and satisfying stem locations (position error around 1.0 m). In comparison to other common tools, the algorithm is weakly sensitive to the parameter setup and can be applied with little knowledge of the study site, thus reducing the effort and cost of field campaigns. Furthermore, it demonstrates to require just 2 points·m−2 as minimum point density, allowing for the analysis of low-density point clouds. Despite its simplicity, it may set the basis for more complex tools, such as those for crown segmentation or biomass computation, with potential applications in forest modeling and management. Numéro de notice : A2021-196 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/rs13020322 date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/rs13020322 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97146
in Remote sensing > Vol 13 n° 2 (January 2021) . - n° 322[article]Building extraction from Lidar data using statistical methods / Haval Abdul-Jabbar Sadeq in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)
PermalinkExtraction of street pole-like objects based on plane filtering from mobile LiDAR data / Jingming Tu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)
PermalinkFusion of ground penetrating radar and laser scanning for infrastructure mapping / Dominik Merkle in Journal of applied geodesy, vol 15 n° 1 (January 2021)
PermalinkGeoreferencing with self-calibration for airborne full-waveform Lidar data using digital elevation model / Qinghua Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)
PermalinkRelation-constrained 3D reconstruction of buildings in metropolitan areas from photogrammetric point clouds / Yuan Li in Remote sensing, vol 13 n° 1 (January 2021)
PermalinkStructure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring / Edoardo Grottoli in Remote sensing, vol 13 n° 1 (January 2021)
PermalinkThe potential of LiDAR and UAV-photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England / Israa Kadhim in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)
PermalinkAdjusting the regular network of squares resolution to the digital terrain model surface shape / Dariusz Gościewski in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)
PermalinkDu drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)
PermalinkMS-RRFSegNetMultiscale regional relation feature segmentation network for semantic segmentation of urban scene point clouds / Haifeng Luo in IEEE Transactions on geoscience and remote sensing, Vol 58 n° 12 (December 2020)
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