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Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests / Sruthi M. Krishna Moorthy in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
[article]
Titre : Improved supervised learning-based approach for leaf and wood classification from LiDAR point clouds of forests Type de document : Article/Communication Auteurs : Sruthi M. Krishna Moorthy, Auteur ; Kim Calders, Auteur ; Matheus B. Vicari, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 3057 - 3070 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] atmosphère terrestre
[Termes IGN] canopée
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] faisceau laser
[Termes IGN] feuille (végétation)
[Termes IGN] foresterie
[Termes IGN] forêt de feuillus
[Termes IGN] forêt tropicale
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] précision de la classification
[Termes IGN] Python (langage de programmation)
[Termes IGN] semis de points
[Termes IGN] transfert radiatifRésumé : (auteur) Accurately classifying 3-D point clouds into woody and leafy components has been an interest for applications in forestry and ecology including the better understanding of radiation transfer between canopy and atmosphere. The past decade has seen an increase in the methods attempting to classify leaves and wood in point clouds based on radiometric or geometric features. However, classification purely based on radiometric features is sensor-specific, and the method by which the local neighborhood of a point is defined affects the accuracy of classification based on geometric features. Here, we present a leaf-wood classification method combining geometrical features defined by radially bounded nearest neighbors at multiple spatial scales in a machine learning model. We compared the performance of three different machine learning models generated by the random forest (RF), XGBoost, and lightGBM algorithms. Using multiple spatial scales eliminates the need for an optimal neighborhood size selection and defining the local neighborhood by radially bounded nearest neighbors makes the method broadly applicable for point clouds of varying quality. We assessed the model performance at the individual tree- and plot-level on field data from tropical and deciduous forests, as well as on simulated point clouds. The method has an overall average accuracy of 94.2% on our data sets. For other data sets, the presented method outperformed the methods in literature in most cases without the need for additional postprocessing steps that are needed in most of the existing methods. We provide the entire framework as an open-source python package. Numéro de notice : A2020-232 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947198 Date de publication en ligne : 31/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947198 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94970
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 3057 - 3070[article]Method for extraction of airborne LiDAR point cloud buildings based on segmentation / Maohua Liu in Plos one, vol 15 n° 5 (May 2020)
[article]
Titre : Method for extraction of airborne LiDAR point cloud buildings based on segmentation Type de document : Article/Communication Auteurs : Maohua Liu, Auteur ; Yue Shao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 0232778 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bati
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de points
[Termes IGN] segmentationRésumé : (auteur) The LiDAR technology is a means of urban 3D modeling in recent years, and the extraction of buildings is a key step in urban 3D modeling. In view of the complexity of most airborne LiDAR building point cloud extraction algorithms that need to combine multiple feature parameters, this study proposes a building point cloud extraction method based on the combination of the Point Cloud Library (PCL) region growth segmentation and the histogram. The filtered LiDAR point cloud is segmented by using the PCL region growth method, and then the local normal vector and direction cosine are calculated for each cluster after segmentation. Finally, the histogram is generated to effectively separate the building point cloud from the non-building.Two sets of airborne LiDAR data in the south and west parts of Tokushima, Japan, are used to test the feasibility of the proposed method. The results are compared with those of the commercial software TerraSolid and the K-means algorithm. Results show that the proposed extraction algorithm has lower type I and II errors and better extraction effect than that of the TerraSolid and the K-means algorithm. Numéro de notice : A2020-832 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1371/journal.pone.0232778 Date de publication en ligne : 29/05/2020 En ligne : https://doi.org/10.1371/journal.pone.0232778 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97666
in Plos one > vol 15 n° 5 (May 2020) . - n° 0232778[article]Outlier detection and robust plane fitting for building roof extraction from LiDAR data / Emon Kumar Dey in International Journal of Remote Sensing IJRS, vol 41 n° 16 (01-10 May 2020)
[article]
Titre : Outlier detection and robust plane fitting for building roof extraction from LiDAR data Type de document : Article/Communication Auteurs : Emon Kumar Dey, Auteur ; Mohammad Awrangjeb, Auteur ; Bela Stantic, Auteur Année de publication : 2020 Article en page(s) : pp 6325 - 6354 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] semis de points
[Termes IGN] toit
[Termes IGN] valeur aberranteRésumé : (auteur) Individual roof plane extraction from Light Detection and Ranging (LiDAR) point-cloud data is a complex and difficult task because of unknown semantic characteristics and inharmonious behaviour of input data. Most of the existing state-of-the-art methods fail to detect small true roof planes with exact boundaries due to outliers, occlusions, complex building structures, and other inconsistent nature of LiDAR data. In this paper, we have presented an improved building detection and roof plane extraction method, which is less sensitive to the outliers and unlikely to generate spurious planes. For this, a robust outlier detection algorithm has been proposed in this paper along with a robust plane-fitting algorithm based on M-estimator SAmple Consensus (MSAC) for detecting individual roof planes. Using two benchmark datasets (Australian and International Society for Photogrammetry and Remote Sensing benchmark) with different numbers of buildings and sizes, trees and point densities, we have evaluated the proposed method. Experimental results show that the method removes outliers and vegetation almost accurately and offers a high success rate in terms of completeness and correctness (between 80% and 100% per-object) for both roof plane extraction and building detection. In most of the cases, the proposed method shows above 90% correctness. Numéro de notice : A2020-454 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2020.1737339 Date de publication en ligne : 09/06/2020 En ligne : https://doi.org/10.1080/01431161.2020.1737339 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95543
in International Journal of Remote Sensing IJRS > vol 41 n° 16 (01-10 May 2020) . - pp 6325 - 6354[article]A point cloud feature regularization method by fusing judge criterion of field force / Xijiang Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 5 (May 2020)
[article]
Titre : A point cloud feature regularization method by fusing judge criterion of field force Type de document : Article/Communication Auteurs : Xijiang Chen, Auteur ; Qing Liu, Auteur ; Kegen Yu, Auteur Année de publication : 2020 Article en page(s) : pp 2994 - 3006 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse vectorielle
[Termes IGN] arbre BSP
[Termes IGN] détection de contours
[Termes IGN] échantillonnage
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] matrice de covariance
[Termes IGN] modèle numérique de surface
[Termes IGN] modélisation du bâti
[Termes IGN] niveau de gris (image)
[Termes IGN] plus proche voisin, algorithme du
[Termes IGN] reconstruction d'objet
[Termes IGN] semis de points
[Termes IGN] spline cubique
[Termes IGN] traitement d'image
[Termes IGN] transformation de Hough
[Termes IGN] Wuhan (Chine)Résumé : (auteur) Point cloud boundary is an important part of the surface model. The traditional feature extraction method has slow speed and low efficiency and only achieves the boundary feature points. Hence, the point cloud feature regularization is proposed to obtain the boundary lines based on the fast extraction of feature points in this article. First, an improved $k$ - $d$ tree method is used to search the $k$ neighbors of sampling point. Then, the sampling point and its $k$ neighbors are used as the reference points set to fit a microcut plane and project to the plane. The local coordinate system is established on the microcut plane to convert 3-D into 2-D. The boundary feature points are identified by judging criterion of field force and then are sorted and connected according to the vector deflected angle and distance. Finally, the boundary lines are smoothed by the improved cubic B-spline fitting method. Experiments show that the proposed method can extract the boundary feature points quickly and efficiently, and the mean error of boundary lines is 0.0674 mm and the standard deviation is 0.0346 mm, which has high precision. This proposed method was also successfully applied to feature extraction and boundary fitting of Xinyi teaching building of the Wuhan University of Technology. Numéro de notice : A2020-230 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2946326 Date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2946326 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94968
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 5 (May 2020) . - pp 2994 - 3006[article]La télédétection aéroportée pour la gestion des territoires forestiers de montagne / Jean-Matthieu Monnet in Sciences, eaux & territoires, n° 33 (avril 2020)
[article]
Titre : La télédétection aéroportée pour la gestion des territoires forestiers de montagne Type de document : Article/Communication Auteurs : Jean-Matthieu Monnet, Auteur ; Pierre Paccard, Auteur ; Catherine Riond, Auteur Année de publication : 2020 Article en page(s) : pp 64 - 69 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] abattage (sylviculture)
[Termes IGN] acquisition de données
[Termes IGN] diffusion de données
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt alpestre
[Termes IGN] France (végétation)
[Termes IGN] gestion forestière
[Termes IGN] télémétrie laser aéroportéRésumé : (Auteur) Le Programme national de la forêt et du bois 2016-2026 affiche comme objectif « d’augmenter les prélèvements de bois en France tout en assurant le renouvellement de la forêt ». Les forêts de montagne qui représentent environ un quart de la surface forestière pourraient contribuer de manière significative à cet objectif. Les contraintes d’accès et de topographie rendent cependant difficile la gestion de ces forêts. En s’appuyant sur la technologie Lidar aéroporté, il est désormais possible de cartographier à haute résolution, sur des territoires de la taille d’un parc naturel régional, les caractéristiques forestières (ressource, accessibilité) intéressant les gestionnaires. La généralisation de l’outil pose cependant des questions de coût d’acquisition des données et de droit de leur diffusion auprès des acteurs de la filière. Numéro de notice : A2020-182 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueNat DOI : 10.14758/SET-REVUE.2020.3.12 Date de publication en ligne : 10/04/2020 En ligne : https://doi.org/10.14758/SET-REVUE.2020.3.12 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94956
in Sciences, eaux & territoires > n° 33 (avril 2020) . - pp 64 - 69[article]Directionally constrained fully convolutional neural network for airborne LiDAR point cloud classification / Congcong Wen in ISPRS Journal of photogrammetry and remote sensing, vol 162 (April 2020)PermalinkUsing multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)PermalinkHow far can we trust forestry estimates from low-density LiDAR acquisitions? The Cutfoot Sioux experimental forest (MN, USA) case study / Enrico Borgogno Mondino in International Journal of Remote Sensing IJRS, vol 41 n° 12 (20 - 30 March 2020)PermalinkClassification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network / Yueguan Yan in ISPRS International journal of geo-information, vol 9 n° 3 (March 2020)PermalinkComparison and analysis of results of 3D modelling of complex cultural and historical objects using different types of terrestrial laser scanner / Admir Mulahusic in Survey review, vol 52 n° 371 (March 2020)PermalinkA discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)PermalinkGeneration of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data / Behnaz Bigdeli in Survey review, vol 52 n° 371 (March 2020)PermalinkHierarchical classification of pole‐like objects in mobile laser scanning point clouds / Rufei Liu in Photogrammetric record, vol 35 n° 169 (March 2020)PermalinkLearning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds / Zhipeng Luo in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkObject-based incremental registration of terrestrial point clouds in an urban environment / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 161 (March 2020)PermalinkUnsupervised extraction of urban features from airborne lidar data by using self-organizing maps / Alper Sen in Survey review, vol 52 n° 371 (March 2020)PermalinkAutomated extraction of lane markings from mobile LiDAR point clouds based on fuzzy inference / Heidar Rastiveis in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkA spatio-temporal deformation model for laser scanning point clouds / Corinna Harmening in Journal of geodesy, vol 94 n°2 (February 2020)PermalinkTree annotations in LiDAR data using point densities and convolutional neural networks / Ananya Gupta in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkAutomatic scale estimation of structure from motion based 3D models using laser scalers in underwater scenarios / Klemen Istenič in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkContribution à la segmentation et à la modélisation 3D du milieu urbain à partir de nuages de points / Tania Landes (2020)PermalinkEstimation of soil surface water contents for intertidal mudflats using a near-infrared long-range terrestrial laser scanner / Kai Tan in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)PermalinkMoving objects aware sensor mesh fusion for indoor reconstruction from a couple of 2D lidar scans / Teng Wu (2020)PermalinkPermalinkPoint cloud registration and mitigation of refraction effects for geomonitoring using long-range terrestrial laser scanning / Ephraim Friedli (2020)PermalinkPratique des relevés en zones urbaines denses intégrant les nouvelles technologies / Théo Laporte (2020)PermalinkPredicting carbon accumulation in temperate forests of Ontario, Canada using a LiDAR-initialized growth-and-yield model / Paulina T. Marczak in Remote sensing, vol 12 n° 1 (January 2020)PermalinkPermalinkRelevés par Lidar mobile de cours d’eau et intégration des profils aux relevés bathymétriques réalisés par sondeur mono-faisceau / Guillaume Didier (2020)PermalinkSimplicial complexes reconstruction and generalisation of 3d lidar data in urban scenes / Stéphane Guinard (2020)PermalinkThree-dimensional reconstruction of fluvial surface sedimentology and topography using personal mobile laser scanning / Richard David Williams in Earth surface processes and landforms, vol 45 n° 1 (January 2020)PermalinkValidation and verification procedures for defining legal 3D boundaries using terrestrial laser scanners / Sam Rondeel in Survey review, Vol 52 n°370 (January 2020)PermalinkDeep learning for conifer/deciduous classification of airborne LiDAR 3D point clouds representing individual trees / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkInside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica / Alexandra L. Boghosian in Photogrammetric record, vol 34 n° 168 (December 2019)PermalinkMaquette numérique BIM : modélisation 3D de l'aéroport international de Bâle-Mulhouse-Fribourg / Thibault Bavoux in XYZ, n° 161 (décembre 2019)PermalinkAutomated fusion of forest airborne and terrestrial point clouds through canopy density analysis / Wenxia Dai in ISPRS Journal of photogrammetry and remote sensing, vol 156 (October 2019)PermalinkPostprocessing synchronization of a laser scanning system aboard a UAV / Marcela do Valle Machado in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)PermalinksUAS-based remote rensing of river discharge using thermal particle image velocimetry and bathymetric lidar / Paul J. Kinzel in Remote sensing, vol 11 n° 19 (October-1 2019)PermalinkAddressing overfitting on point cloud classification using Atrous XCRF / Hasan Asy’ari Arief in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)PermalinkComparison of filtering algorithms used for DTM production from airborne lidar data: a case study in Bergama, Turkey / Baris Suleymanoglu in Geodetski vestnik, vol 63 n° 3 (September - November 2019)PermalinkPpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? / Sandra Bujan in Photogrammetric record, vol 34 n° 167 (September 2019)PermalinkReduction of measurement data before Digital Terrain Model generation vs. DTM generalisation / Wioleta Błaszczak-Bąk in Survey review, vol 51 n° 368 (September 2019)PermalinkReview of mobile laser scanning target‐free registration methods for urban areas using improved error metrics / Hoang Long Nguyen in Photogrammetric record, vol 34 n° 167 (September 2019)PermalinkAutomatic extraction of accurate 3D tie points for trajectory adjustment of mobile laser scanners using aerial imagery / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkExplanation for the seam line discontinuity in terrestrial laser scanner point clouds / Derek D. Lichti in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkModelling of buildings from aerial LiDAR point clouds using TINs and label maps / Minglei Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkPavement marking retroreflectivity estimation and evaluation using mobile Lidar data / Erzhuo Che in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 8 (August 2019)PermalinkSemantic segmentation of road furniture in mobile laser scanning data / Fashuai Li in ISPRS Journal of photogrammetry and remote sensing, vol 154 (August 2019)PermalinkTotal Vertical Uncertainty (TVU) modeling for topo-bathymetric LIDAR systems / Firat Eren in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 8 (August 2019)PermalinkVers une maquette numérique « foncière » ? / Anonyme in Géomatique expert, n° 129 (août - septembre 2019)Permalink