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Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds / Hamid Hamraz in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : Vertical stratification of forest canopy for segmentation of understory trees within small-footprint airborne LiDAR point clouds Type de document : Article/Communication Auteurs : Hamid Hamraz, Auteur ; Marco A. Contreras, Auteur ; Jun Zhang, Auteur Année de publication : 2017 Article en page(s) : pp 385 - 392 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] arbre caducifolié
[Termes IGN] canopée
[Termes IGN] croissance végétale
[Termes IGN] densité des points
[Termes IGN] distribution spatiale
[Termes IGN] houppier
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Kentucky (Etats-Unis)
[Termes IGN] modèle numérique de surface
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Termes IGN] sous-bois
[Termes IGN] strate végétale
[Termes IGN] structure d'un peuplement forestierRésumé : (Auteur) Airborne LiDAR point cloud representing a forest contains 3D data, from which vertical stand structure even of understory layers can be derived. This paper presents a tree segmentation approach for multi-story stands that stratifies the point cloud to canopy layers and segments individual tree crowns within each layer using a digital surface model based tree segmentation method. The novelty of the approach is the stratification procedure that separates the point cloud to an overstory and multiple understory tree canopy layers by analyzing vertical distributions of LiDAR points within overlapping locales. The procedure does not make a priori assumptions about the shape and size of the tree crowns and can, independent of the tree segmentation method, be utilized to vertically stratify tree crowns of forest canopies. We applied the proposed approach to the University of Kentucky Robinson Forest – a natural deciduous forest with complex and highly variable terrain and vegetation structure. The segmentation results showed that using the stratification procedure strongly improved detecting understory trees (from 46% to 68%) at the cost of introducing a fair number of over-segmented understory trees (increased from 1% to 16%), while barely affecting the overall segmentation quality of overstory trees. Results of vertical stratification of the canopy showed that the point density of understory canopy layers were suboptimal for performing a reasonable tree segmentation, suggesting that acquiring denser LiDAR point clouds would allow more improvements in segmenting understory trees. As shown by inspecting correlations of the results with forest structure, the segmentation approach is applicable to a variety of forest types. Numéro de notice : A2017-519 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.07.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.07.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86481
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 385 - 392[article]Exemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2017081 RAB Revue Centre de documentation En réserve L003 Disponible 081-2017083 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2017082 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt 3D tree modeling from incomplete point clouds via optimization and L1-MST / Jie Mei in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)
[article]
Titre : 3D tree modeling from incomplete point clouds via optimization and L1-MST Type de document : Article/Communication Auteurs : Jie Mei, Auteur ; Liqiang Zhang, Auteur ; Shihao Wu, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 999 - 1021 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme STA
[Termes IGN] arbre (flore)
[Termes IGN] branche (arbre)
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] méthode robuste
[Termes IGN] modèle numérique d'objet
[Termes IGN] optimisation (mathématiques)
[Termes IGN] semis de points
[Termes IGN] semis de points clairsemés
[Termes IGN] squelettisationRésumé : (auteur) Reconstruction of 3D trees from incomplete point clouds is a challenging issue due to their large variety and natural geometric complexity. In this paper, we develop a novel method to effectively model trees from a single laser scan. First, coarse tree skeletons are extracted by utilizing the L1-median skeleton to compute the dominant direction of each point and the local point density of the point cloud. Then we propose a data completion scheme that guides the compensation for missing data. It is an iterative optimization process based on the dominant direction of each point and local point density. Finally, we present a L1-minimum spanning tree (MST) algorithm to refine tree skeletons from the optimized point cloud, which integrates the advantages of both L1-median skeleton and MST algorithms. The proposed method has been validated on various point clouds captured from single laser scans. The experiment results demonstrate the effectiveness and robustness of our method for coping with complex shapes of branching structures and occlusions. Numéro de notice : A2017-239 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1264075 En ligne : http://dx.doi.org/10.1080/13658816.2016.1264075 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85173
in International journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 999 - 1021[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2017031 RAB Revue Centre de documentation En réserve L003 Disponible Planar-based adaptive down-sampling of point clouds / Yun-Jou Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 12 (December 2016)
[article]
Titre : Planar-based adaptive down-sampling of point clouds Type de document : Article/Communication Auteurs : Yun-Jou Lin, Auteur ; Ronald R Benziger, Auteur ; Ayman Habib, Auteur Année de publication : 2016 Article en page(s) : pp 955 - 966 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] densité des points
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] surface plane
[Termes IGN] traitement automatique de données
[Termes IGN] traitement de données localiséesRésumé : (auteur) Derived point clouds from laser scanners and image-based dense-matching techniques usually include tremendous number of points. Processing (e.g., segmenting) such huge dataset is time-consuming and might not be necessary. For example, a planar surface just needs few points to be defined. In contrast, linear/cylindrical and rough features require more points for reliable modeling since during the data acquisition process, only a portion of linear/cylindrical features is present in the point cloud.
This paper introduces an adaptive down-sampling strategy for removing redundant points from high density planar regions while retaining points in planar areas with sparse points and all the points within linear/cylindrical and rough neighborhoods. To demonstrate the feasibility and performance of the proposed procedure, a comparison of segmentation results using original laser and image-based point clouds as well as the adaptively, uniformly, and point-spacing-based down-sampled point clouds are presented while commenting on the computational efficiency and the segmentation quality.Numéro de notice : A2016-984 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.82.12.955 En ligne : https://doi.org/10.14358/PERS.82.12.955 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83700
in Photogrammetric Engineering & Remote Sensing, PERS > vol 82 n° 12 (December 2016) . - pp 955 - 966[article]Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns / Kaja Kandare in European journal of remote sensing, vol 49 n° 1 (2016)
[article]
Titre : Effects of forest structure and airborne laser scanning point cloud density on 3D delineation of individual tree crowns Type de document : Article/Communication Auteurs : Kaja Kandare, Auteur ; Hans Ole Ørka, Auteur ; Jonathan Cheung-Wai Chan, Auteur ; Michele Dalponte, Auteur Année de publication : 2016 Article en page(s) : pp 337 - 359 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Alpes
[Termes IGN] délimitation
[Termes IGN] densité des points
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forêt alpestre
[Termes IGN] houppier
[Termes IGN] Italie
[Termes IGN] semis de points
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) This paper presents a 3D delineation method for airborne laser scanning point cloud. The method is based on an unsupervised clustering technique applied on horizontal slices followed by vertical merging based on overlapping among clusters. On an Alpine forest dataset, we analysed the effects of different forest structures and point cloud densities on tree crown delineation. Forest structure affects mainly the omission error, which eases with homogeneous tree spacing and sizes, while on the commission error forest structure has only slight effect. Delineation accuracy increases with higher point densities where Mann-Whitney-Wilcoxon test shows that accuracy differences between thinned data and original data are statistically significant. Numéro de notice : A2016-829 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.5721/EuJRS20164919 En ligne : http://dx.doi.org/10.5721/EuJRS20164919 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82709
in European journal of remote sensing > vol 49 n° 1 (2016) . - pp 337 - 359[article]The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering / Yi Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)
[article]
Titre : The D-FCM partitioned D-BSP tree for massive point cloud data access and rendering Type de document : Article/Communication Auteurs : Yi Zhang, Auteur Année de publication : 2016 Article en page(s) : pp 25 - 36 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse en composantes principales
[Termes IGN] arbre BSP
[Termes IGN] classification floue
[Termes IGN] densité des points
[Termes IGN] semis de points
[Termes IGN] traitement de semis de points
[Termes IGN] valeur propreRésumé : (Auteur) The spatial partitioning of massive point cloud data involves dividing the space into a multi-tree structure step by step, so as to achieve the purpose of fast access and to render the point cloud. The current methods are based on spatial regularity and equal division, which is not consistent with the irregular and non-uniform distribution of most point clouds. This paper presents a directional fuzzy c-means (D-FCM) method for irregular spatial partitioning. The distance metric is weighted by a direction coefficient, which is determined by the eigenvalue of the point cloud. The orientation of each node is adaptively calculated by principal component analysis of the point cloud, and Karhunen-Loeve (KL) transform is applied to the points coordinates in node. A binary space partitioning (BSP) tree structure is used to partition the point cloud data node by node, and a directional BSP (D-BSP) tree is formed. The D-BSP tree structure was tested with point clouds of 0.1 million to over 2 billion points (up to 60 GB). The experimental results showed that the D-BSP tree can ensure that the bounding boxes are close to the actual spatial distribution of the point cloud, it can completely expand along the spatial configuration of the point cloud without generating unnecessary partitioning, and it can achieve a higher rendering speed with less memory requirement. Numéro de notice : A2016-795 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.08.002 En ligne : https://doi.org/10.1016/j.isprsjprs.2016.08.002 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82529
in ISPRS Journal of photogrammetry and remote sensing > vol 120 (october 2016) . - pp 25 - 36[article]Dynamic occlusion detection and inpainting of in situ captured terrestrial laser scanning point clouds sequence / Chi Chen in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)PermalinkInternational benchmarking of the individual tree detection methods for modeling 3-D canopy structure for silviculture and forest ecology using airborne laser scanning / Yunsheng Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 9 (September 2016)PermalinkUnderstanding the effects of ALS pulse density for metric retrieval across diverse forest types / Phil Wilkes in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 8 (August 2015)PermalinkEffects of LiDAR point density and landscape context on estimates of urban forest biomass / Kunwar K. Singh in ISPRS Journal of photogrammetry and remote sensing, vol 101 (March 2015)PermalinkStudying commuting behaviours using collaborative visual analytics / Roger Beecham in Computers, Environment and Urban Systems, vol 47 (September 2014)PermalinkUsing mobile laser scanning data for automated extraction of road markings / Haiyan Guan in ISPRS Journal of photogrammetry and remote sensing, vol 87 (January 2014)PermalinkA framework for the registration and segmentation of heterogeneous lidar data / M. Al-Durgham in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 2 (February 2013)PermalinkGround filtering and vegetation mapping using multi-return terrestrial laser scanning / Francesco Pirotti in ISPRS Journal of photogrammetry and remote sensing, vol 76 (February 2013)PermalinkNew approaches for estimating local point density and its impact on lidar data segmentation / Z. Lari in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 2 (February 2013)PermalinkLidar strip adjustment with automatically reconstructed roof shapes / M. Rentsch in Photogrammetric record, vol 27 n° 139 (September - November 2012)Permalink