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Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops / Nina Kranjec in Geodetski vestnik, vol 65 n° 2 (June - August 2021)  

Public [article]  inGeodetski vestnik > vol 65 n° 2 (June - August 2021) . - pp 234 - 259 Titre : Predicting tree species based on the geometry and density of aerial laser scanning point cloud of treetops Type de document : Article/Communication Auteurs : Nina Kranjec, Auteur ; Mihaela Triglav Cekada, Auteur ; Milan Kobal, Auteur Année de publication : 2021 Article en page(s) : pp 234 - 259 Note générale : bibliographie Langues : Anglais (eng) Slovène (slv) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Acer pseudoplatanus [Termes IGN] apprentissage automatique [Termes IGN] arbre de décision [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] Fagus sylvatica [Termes IGN] feuillu [Termes IGN] figure géométrique [Termes IGN] Fraxinus excelsior [Termes IGN] houppier [Termes IGN] identification automatique [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Larix decidua [Termes IGN] modèle de simulation [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] Picea abies [Termes IGN] Pinophyta [Termes IGN] Pinus sylvestris [Termes IGN] semis de points [Termes IGN] Slovénie Résumé : (auteur) Based on the laser point clouds of 240 individual trees that were also identified in the field, we developed decision trees to distinguish deciduous and coniferous trees and individual tree species: Picea abies, Larix decidua, Pinus sylvestris, Fagus sylvatica, Acer pseudoplatanus, Fraxinus excelsior. The volume of the upper part of the tree crown (height of 3 m) and the average intensity of the laser reflections were used as explanatory variables. There were four aerial laser datasets: May 2012, September 2012, March 2013 and July 2015. We found that the combination of the volume and the average intensity of the first three laser datasets was the most reliable for predicting the selected tree species (60% model performance). A slightly poorer model performance was obtained if only the average intensity of the first three datasets was used (54% model performance). The worst model performance was given by the intensities (31 % model performance) or the volumes (21 % model performance) of dataset 4, which represents the national laser scanning of Slovenia (LSS). The best performing was the deciduous and coniferous separation, which achieved 75% and 95% success based on the test data (combination of volume and average intensity of the first three laser datasets). Using only the LSS intensities, deciduous and coniferous trees could be separated with 81% success. Numéro de notice : A2021-559 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.15292/geodetski-vestnik.2021.02.234-259 date de publication en ligne : 27/05/2021 En ligne : https://doi.org/10.15292/geodetski-vestnik.2021.02.234-259 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98113 [article]

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3D change detection using adaptive thresholds based on local point cloud density / Dan Liu in ISPRS International journal of geo-information, vol 10 n° 3 (March 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 3 (March 2021) . - n° 127 Titre : 3D change detection using adaptive thresholds based on local point cloud density Type de document : Article/Communication Auteurs : Dan Liu, Auteur ; Dajun Li, Auteur ; Meizhen Wang, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 127 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification barycentrique [Termes IGN] densité des points [Termes IGN] détection de changement [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] MNS lidar [Termes IGN] scène urbaine [Termes IGN] semis de points [Termes IGN] seuillage de points Résumé : (auteur) In recent years, because of highly developed LiDAR (Light Detection and Ranging) technologies, there has been increasing demand for 3D change detection in urban monitoring, urban model updating, and disaster assessment. In order to improve the effectiveness of 3D change detection based on point clouds, an approach for 3D change detection using point-based comparison is presented in this paper. To avoid density variation in point clouds, adaptive thresholds are calculated through the k-neighboring average distance and the local point cloud density. A series of experiments for quantitative evaluation is performed. In the experiments, the influencing factors including threshold, registration error, and neighboring number of 3D change detection are discussed and analyzed. The results of the experiments demonstrate that the approach using adaptive thresholds based on local point cloud density are effective and suitable. Numéro de notice : A2021-231 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10030127 date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.3390/ijgi10030127 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97222 [article]

Classification 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)  

Public [article]  inISPRS International journal of geo-information > vol 9 n° 3 (March 2020) . - 19 p. Titre : Classification and segmentation of mining area objects in large-scale spares Lidar point cloud using a novel rotated density network Type de document : Article/Communication Auteurs : Yueguan Yan, Auteur ; Haixu Yan, Auteur ; Junting Guo, Auteur ; Huayang Dai, Auteur Année de publication : 2020 Article en page(s) : 19 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] classification barycentrique [Termes IGN] classification orientée objet [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] corrélation automatique de points homologues [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] objet 3D [Termes IGN] reconnaissance d'objets [Termes IGN] semis de points clairsemés Résumé : (auteur) The classification and segmentation of large-scale, sparse, LiDAR point cloud with deep learning are widely used in engineering survey and geoscience. The loose structure and the non-uniform point density are the two major constraints to utilize the sparse point cloud. This paper proposes a lightweight auxiliary network, called the rotated density-based network (RD-Net), and a novel point cloud preprocessing method, Grid Trajectory Box (GT-Box), to solve these problems. The combination of RD-Net and PointNet was used to achieve high-precision 3D classification and segmentation of the sparse point cloud. It emphasizes the importance of the density feature of LiDAR points for 3D object recognition of sparse point cloud. Furthermore, RD-Net plus PointCNN, PointNet, PointCNN, and RD-Net were introduced as comparisons. Public datasets were used to evaluate the performance of the proposed method. The results showed that the RD-Net could significantly improve the performance of sparse point cloud recognition for the coordinate-based network and could improve the classification accuracy to 94% and the segmentation per-accuracy to 70%. Additionally, the results concluded that point-density information has an independent spatial–local correlation and plays an essential role in the process of sparse point cloud recognition. Numéro de notice : A2020-256 Affiliation des auteurs : non IGN Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 0.3390/ijgi9030182 date de publication en ligne : 24/03/2020 En ligne : https://doi.org/10.3390/ijgi9030182 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95012 [article]

PpC: 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)  

Public [article]  inPhotogrammetric record > vol 34 n° 167 (September 2019) . - pp Titre : PpC: a new method to reduce the density of lidar data. Does it affect the DEM accuracy? Type de document : Article/Communication Auteurs : Sandra Bujan, Auteur ; Edouardo M. González‐Ferreiro, Auteur ; Miguel Cordero, Auteur ; David Miranda, Auteur Année de publication : 2019 Article en page(s) : pp Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] échantillonnage [Termes IGN] modèle numérique de surface [Termes IGN] pente [Termes IGN] précision [Termes IGN] R (langage) [Termes IGN] réduction Résumé : (auteur) In cost–benefit analysis of lidar data acquisition, point density is often artificially reduced in order to examine how this affects the quality of derived products. However, the performance of the different density reduction methods has not yet been compared and their influence on the accuracy of the models and results has not been evaluated. A novel method for reducing the point density, termed Proportional per Cell (PpC), is presented and compared with the performance of three other reduction methods, examining their influence on the accuracy of lidar‐derived digital surface models using ISPRS reference data. The results indicate that the PpC method was better at conserving the characteristics of the original data. However, point density, sample type and slope had a greater influence than the reduction method used. Numéro de notice : A2019-499 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12295 date de publication en ligne : 10/10/2019 En ligne : https://doi.org/10.1111/phor.12295 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93763 [article]

Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density / Yuan Li in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 151 - 165 Titre : Structural segmentation and classification of mobile laser scanning point clouds with large variations in point density Type de document : Article/Communication Auteurs : Yuan Li, Auteur ; Bo Wu, Auteur ; Xuming Ge, Auteur Année de publication : 2019 Article en page(s) : pp 151 - 165 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] champ aléatoire conditionnel [Termes IGN] classification [Termes IGN] classification basée sur les régions [Termes IGN] densité des points [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] Hong-Kong [Termes IGN] modèle 3D de l'espace urbain [Termes IGN] Paris (75) [Termes IGN] scène urbaine [Termes IGN] segmentation en régions [Termes IGN] segmentation hiérarchique [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (Auteur) Objects are formed by various structures and such structural information is essential for the identification of objects, especially for street facilities presented by mobile laser scanning (MLS) data with abundant details. However, due to the large volume of data, large variations in point density, noise and complexity of scanned scenes, the achievement of effective decomposition of objects into physical meaningful structures remains a challenge issue. And structural information has been rarely considered to improve the accuracy of distinguishing between objects with global or local similarity, such as traffic signs and traffic lights. Therefore, we propose a structural segmentation and classification method for MLS point clouds that is efficient and robust to variations in point density and complex urban scenes. During the segmentation stage, a novel region growing approach and a multi-size supervoxel segmentation algorithm robust to noise and varying density are combined to extract effective local shape descriptors. Structural components with physically meaningful labels are generated via structural labelling and clustering. During the classification stage, we consider the structural information at various scales and locations and encode it into a conditional random-field model for unary and pairwise inferences. High-order potentials are also introduced into the conditional random field to eliminate regional label noise. These high-order potentials are defined upon regions independent of connection relationships and can therefore take effect on isolated nodes. Experiments with two MLS datasets of typical urban scenes in Paris and Hong Kong were used to evaluate the performance of the proposed method. Nine and eleven different object classes were recognized from these two datasets with overall accuracies of 97.13% and 95.79%, respectively, indicating the effectiveness of the proposed method of interpreting complex urban scenes from point clouds with large variations in point density. Compared with previous studies on the Paris dataset, our method was able to recognize more classes and obtained a mean F1-score of 72.70% of seven common classes, being higher than the best of previous results. Numéro de notice : A2019-262 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.05.007 date de publication en ligne : 28/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.05.007 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93075 [article]

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Demonstrating the transferability of forest inventory attribute models derived using airborne laser scanning data / Piotr Tompalski in Remote sensing of environment, vol 227 (15 June 2019)  

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A new method of equiangular sectorial voxelization of single-scan terrestrial laser scanning data and its applications in forest defoliation estimation / Langning Huo in ISPRS Journal of photogrammetry and remote sensing, vol 151 (May 2019)  

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Diffusion and inpainting of reflectance and height LiDAR orthoimages / Pierre Biasutti in Computer Vision and image understanding, vol 179 (February 2019)    

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Repeated structure detection for 3D reconstruction of building façade from mobile lidar data / Yanming Chen in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 2 (February 2019)  

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Visibility estimation in point clouds with variable density / Pierre Biasutti (2019)    

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3D local feature BKD to extract road information from mobile laser scanning point clouds / Yang Bisheng in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Joint classification and contour extraction of large 3D point clouds / Timo Hackel in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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Reducing classification error of grassland overgrowth by combing low-density lidar acquisitions and optical remote sensing data / Timo P Pitkänen in ISPRS Journal of photogrammetry and remote sensing, vol 130 (August 2017)  

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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)  

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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)  

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