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Termes IGN > 1-Candidats > semis de points
semis de points
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- Ensemble de points répartis de façon régulière ou quelconque sur une zone géographique donnée. (Glossaire de cartographie / CFC) Ces points peuvent être issus d'images ou de données lidar ...
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Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning / Tim Ritter in Forests, vol 8 n° 8 (August 2017)
[article]
Titre : Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning Type de document : Article/Communication Auteurs : Tim Ritter, Auteur ; Marcel Schwarz, Auteur ; Andreas Tockner, Auteur ; Friedrich Leisch, Auteur ; Arne Nothdurft, Auteur Année de publication : 2017 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] Abies alba
[Termes IGN] analyse de groupement
[Termes IGN] Autriche
[Termes IGN] diamètre à hauteur de poitrine
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Fagus sylvatica
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Larix decidua
[Termes IGN] peuplement forestier
[Termes IGN] Picea abies
[Termes IGN] Pinus sylvestris
[Termes IGN] Préalpes (Europe)
[Termes IGN] semis de points
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) Mapping of exact tree positions can be regarded as a crucial task of field work associated with forest monitoring, especially on intensive research plots. We propose a two-stage density clustering approach for the automatic mapping of tree positions, and an algorithm for automatic tree diameter estimates based on terrestrial laser-scanning (TLS) point cloud data sampled under limited sighting conditions. We show that our novel approach is able to detect tree positions in a mixed and vertically structured stand with an overall accuracy of 91.6%, and with omission- and commission error of only 5.7% and 2.7% respectively. Moreover, we were able to reproduce the stand’s diameter in breast height (DBH) distribution, and to estimate single trees DBH with a mean average deviation of ±2.90 cm compared with tape measurements as reference. Numéro de notice : A2017-876 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f8080265 Date de publication en ligne : 25/07/2017 En ligne : https://doi.org/10.3390/f8080265 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91195
in Forests > vol 8 n° 8 (August 2017)[article]Image matching as a data source for forest inventory – Comparison of semi-global matching and next-generation automatic terrain extraction algorithms in a typical managed boreal forest environment / Mari Kukkonen in International journal of applied Earth observation and geoinformation, vol 60 (August 2017)
[article]
Titre : Image matching as a data source for forest inventory – Comparison of semi-global matching and next-generation automatic terrain extraction algorithms in a typical managed boreal forest environment Type de document : Article/Communication Auteurs : Mari Kukkonen, Auteur ; Matti Maltamo, Auteur ; Petteri Packalen, Auteur Année de publication : 2017 Article en page(s) : pp 11 - 21 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse comparative
[Termes IGN] appariement d'images
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Finlande
[Termes IGN] forêt boréale
[Termes IGN] image aérienne
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle numérique de terrain
[Termes IGN] semis de points
[Termes IGN] sous-boisRésumé : (auteur) Image matching is emerging as a compelling alternative to airborne laser scanning (ALS) as a data source for forest inventory and management. There is currently an open discussion in the forest inventory community about whether, and to what extent, the new method can be applied to practical inventory campaigns. This paper aims to contribute to this discussion by comparing two different image matching algorithms (Semi-Global Matching [SGM] and Next-Generation Automatic Terrain Extraction [NGATE]) and ALS in a typical managed boreal forest environment in southern Finland. Spectral features from unrectified aerial images were included in the modeling and the potential of image matching in areas without a high resolution digital terrain model (DTM) was also explored. Plot level predictions for total volume, stem number, basal area, height of basal area median tree and diameter of basal area median tree were modeled using an area-based approach. Plot level dominant tree species were predicted using a random forest algorithm, also using an area-based approach. The statistical difference between the error rates from different datasets was evaluated using a bootstrap method. Results showed that ALS outperformed image matching with every forest attribute, even when a high resolution DTM was used for height normalization and spectral information from images was included. Dominant tree species classification with image matching achieved accuracy levels similar to ALS regardless of the resolution of the DTM when spectral metrics were used. Neither of the image matching algorithms consistently outperformed the other, but there were noticeably different error rates depending on the parameter configuration, spectral band, resolution of DTM, or response variable. This study showed that image matching provides reasonable point cloud data for forest inventory purposes, especially when a high resolution DTM is available and information from the understory is redundant. Numéro de notice : A2017-364 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.jag.2017.03.012 En ligne : https://doi.org/10.1016/j.jag.2017.03.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85791
in International journal of applied Earth observation and geoinformation > vol 60 (August 2017) . - pp 11 - 21[article]Joint classification and contour extraction of large 3D point clouds / Timo Hackel in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)
[article]
Titre : Joint classification and contour extraction of large 3D point clouds Type de document : Article/Communication Auteurs : Timo Hackel, Auteur ; Jan Dirk Wegner, Auteur ; Konrad Schindler, Auteur Année de publication : 2017 Article en page(s) : pp 231 - 245 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] attribut sémantique
[Termes IGN] classification dirigée
[Termes IGN] compréhension de l'image
[Termes IGN] densité des points
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] données massives
[Termes IGN] segmentation sémantique
[Termes IGN] semis de pointsRésumé : (Auteur) We present an effective and efficient method for point-wise semantic classification and extraction of object contours of large-scale 3D point clouds. What makes point cloud interpretation challenging is the sheer size of several millions of points per scan and the non-grid, sparse, and uneven distribution of points. Standard image processing tools like texture filters, for example, cannot handle such data efficiently, which calls for dedicated point cloud labeling methods. It turns out that one of the major drivers for efficient computation and handling of strong variations in point density, is a careful formulation of per-point neighborhoods at multiple scales. This allows, both, to define an expressive feature set and to extract topologically meaningful object contours.
Semantic classification and contour extraction are interlaced problems. Point-wise semantic classification enables extracting a meaningful candidate set of contour points while contours help generating a rich feature representation that benefits point-wise classification. These methods are tailored to have fast run time and small memory footprint for processing large-scale, unstructured, and inhomogeneous point clouds, while still achieving high classification accuracy. We evaluate our methods on the semantic3d.net benchmark for terrestrial laser scans with
points.Numéro de notice : A2017-515 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.05.012 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.05.012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86476
in ISPRS Journal of photogrammetry and remote sensing > vol 130 (August 2017) . - pp 231 - 245[article]Réservation
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[article]
Titre : On visibility and empty-region graphs Type de document : Article/Communication Auteurs : Sagi Katz, Auteur ; Ayellet Tal, Auteur Année de publication : 2017 Article en page(s) : pp 45 - 52 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique
[Termes IGN] graphe
[Termes IGN] semis de points
[Termes IGN] traitement de données localisées
[Termes IGN] triangulation de Delaunay
[Termes IGN] visibilitéRésumé : (auteur) Empty-region graphs are well-studied in Computer Graphics, Geometric Modeling, Computational Geometry, as well as in Robotics and Computer Vision. The vertices of these graphs are points in space, and two vertices are connected by an arc if there exists an empty region of a certain shape and size between them. In most of the graphs discussed in the literature, the empty region is assumed to be a circle or the union/intersection of circles. In this paper we propose a new type of empty-region graphs—the γ-visibility graph. This graph can accommodate a variety of shapes of empty regions and may be defined in any dimension. Interestingly, we will show that commonly-used shapes are a special case of our graph. In this sense, our graph generalizes some empty-region graphs. Though this paper is mostly theoretical, it may have practical implication—the numerous applications that make use of empty-region graphs would be able to select the best shape that suits the problem at hand. Numéro de notice : A2017-564 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.cag.2017.05.007 En ligne : https://doi.org/10.1016/j.cag.2017.05.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86646
in Computers and graphics > vol 66 (August 2017) . - pp 45 - 52[article]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]Réservation
Réserver ce documentExemplaires (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 building roof reconstruction from airborne LiDAR point clouds : a framework based on a spatial database / Rujun Cao in International journal of geographical information science IJGIS, vol 31 n° 7-8 (July - August 2017)PermalinkNorthern conifer forest species classification using multispectral data acquired from an unmanned aerial vehicle / Steven E. Franklin in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 7 (July 2017)PermalinkA novel automatic method for the fusion of ALS and TLS LiDAR data for robust assessment of tree crown structure / Claudia Paris in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)PermalinkRobust point cloud classification based on multi-level semantic relationships for urban scenes / Qing Zhu in ISPRS Journal of photogrammetry and remote sensing, vol 129 (July 2017)PermalinkPermalinkAutomation of point cloud processing to increase the deformation monitoring accuracy / Ján Erdélyi in Applied geomatics, vol 9 n° 2 (June 2017)PermalinkDéveloppement du processus de modélisation 3D de maquettes numériques à partir de nuages de points / Thibault Bavoux in XYZ, n° 151 (juin - août 2017)PermalinkObject-based analysis of multispectral airborne laser scanner data for land cover classification and map updating / Leena Matikainen in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)PermalinkA review of the use of terrestrial laser scanning application for change detection and deformation monitoring of structures / Wallace Mukupa in Survey review, vol 49 n° 353 (June 2017)PermalinkTotal canopy transmittance estimated from small-footprint, full-waveform airborne LiDAR / Milutin Milenković in ISPRS Journal of photogrammetry and remote sensing, vol 128 (June 2017)Permalink