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A 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)
[article]
Titre : A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data Type de document : Article/Communication Auteurs : Qingwang Wang, Auteur ; Yanfeng Gu, Auteur Année de publication : 2020 Article en page(s) : pp 1568 -1586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] Amérique du nord
[Termes IGN] analyse discriminante
[Termes IGN] calcul tensoriel
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification multibande
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] état de l'art
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] image multibande
[Termes IGN] modèle géométrique
[Termes IGN] semis de points
[Termes IGN] tenseur
[Termes IGN] vectorisation
[Termes IGN] voisinage (relation topologique)Résumé : (Auteur) Multispectral light detection and ranging (MS-LiDAR) systems open the door to the possibility in the 3-D land cover classification at a finer scale using only point cloud data. This article proposes a model based on the tensor representation for multispectral point cloud classification. The proposed method combines the 3-D local spatial structure of each multispectral point by characterizing the point with a second-order tensor. The first mode of the tensor indicates the spatial location and spectral information of each point (i.e., the row of the second-order tensor) and the second mode denotes the neighborhood geometric and spectral structures (i.e., the column of the second-order tensor). Then we develop a novel tensor manifold discriminant embedding (TMDE) algorithm to extract the geometric–spectral features for multispectral point clouds classification. TMDE solves the mapping matrices of each mode by preserving the intraclass samples’ distribution further making it more compact and maximizing the distance of different classes. Finally, the support vector machine classifier with the extracted features as input is used to implement the classification of multispectral point clouds. Experiments are conducted on two real multispectral point cloud data sets. The experimental results demonstrate that the proposed method can achieve significant improvements in classification accuracies in comparison with several state-of-the-art algorithms. Numéro de notice : A2020-086 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947081 Date de publication en ligne : 30/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947081 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94660
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1568 -1586[article]Generation 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)
[article]
Titre : Generation of digital terrain model for forest areas using a new particle swarm optimization on LiDAR data Type de document : Article/Communication Auteurs : Behnaz Bigdeli, Auteur ; Masoomeh Gomroki, Auteur ; Parham Pahlavani, Auteur Année de publication : 2020 Article en page(s) : pp 115 - 125 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] erreur moyenne quadratique
[Termes IGN] filtrage de la végétation
[Termes IGN] interpolation polynomiale
[Termes IGN] Iran
[Termes IGN] modèle numérique de terrain
[Termes IGN] optimisation par essaim de particules
[Termes IGN] semis de points
[Termes IGN] surface forestièreRésumé : (auteur) Since Light Detection and Ranging (LiDAR) data are capable of distinguishing vegetation from bare earth, these data are used nowadays to produce digital terrain models (DTMs) for forest regions. In this research, raw LiDAR data were filtered using hybrid and slope-based filtering methods and the filtered data were then interpolated using the new modified particle swarm optimisation (PSO) and accordingly the results were compared with those achieved by the other intelligent and conventional interpolation methods. The new modified PSO optimized the polynomial degree for interpolation and found suitable parameters for optimisation. Two data sets from two forest regions in some northern regions of Iran located in Golestan province were selected to compare these methods. Region 1 with dense vegetation and region 2 with grass vegetation. The results indicated that the hybrid filter performed lower RMSE than the slope-based filter. Finally, the DTM with lowest RMSE was obtained using the hybrid filter and the modified PSO interpolation method with RMSE of 6 mm for region 1 (Tavar-kuh) and 61 mm for region 2 (Shastkola River Basin). Numéro de notice : A2020-078 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/00396265.2018.1530331 Date de publication en ligne : 10/10/2018 En ligne : https://doi.org/10.1080/00396265.2018.1530331 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94640
in Survey review > vol 52 n° 371 (March 2020) . - pp 115 - 125[article]Hierarchical classification of pole‐like objects in mobile laser scanning point clouds / Rufei Liu in Photogrammetric record, vol 35 n° 169 (March 2020)
[article]
Titre : Hierarchical classification of pole‐like objects in mobile laser scanning point clouds Type de document : Article/Communication Auteurs : Rufei Liu, Auteur ; Peng Wang, Auteur ; Zhaojin Yan, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 81 - 107 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse de la valeur
[Termes IGN] classification ascendante hiérarchique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] lidar mobile
[Termes IGN] milieu urbain
[Termes IGN] semis de points
[Termes IGN] valeur propreRésumé : (Auteur) For the classification of pole‐like objects (trees, lamp posts, traffic lights and traffic signs) in mobile laser scanning (MLS) point clouds, a hierarchical classification method is proposed. The method consists of three major steps. (1) The objects’ cylindrical column sections are detected based on the characteristics of arc‐like points using RANSAC after denoising. (2) These detected objects are roughly classified into trees and man‐made poles based on the azimuthal coverage of point clouds above the cylindrical column. (3) Eigenvalue analysis and the principal direction of the upper pole projections are used to differentiate lamp posts, traffic lights and traffic signs. Experimental analysis shows that the method can effectively identify different types of pole‐like objects. Numéro de notice : A2020-133 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12307 Date de publication en ligne : 10/01/2020 En ligne : https://doi.org/10.1111/phor.12307 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94819
in Photogrammetric record > vol 35 n° 169 (March 2020) . - pp 81 - 107[article]Learning 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)
[article]
Titre : Learning sequential slice representation with an attention-embedding network for 3D shape recognition and retrieval in MLS point clouds Type de document : Article/Communication Auteurs : Zhipeng Luo, Auteur ; Di Liu, Auteur ; Jonathan Li, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 147 - 163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] balayage laser
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] graphe
[Termes IGN] reconnaissance de formes
[Termes IGN] réseau neuronal convolutif
[Termes IGN] réseau routier
[Termes IGN] semis de points
[Termes IGN] télémétrie laser mobileRésumé : (Auteur) The representation of 3D data is the key issue for shape analysis. However, most of the existing representations suffer from high computational cost and structure information loss. This paper presents a novel sequential slice representation with an attention-embedding network, named RSSNet, for 3D point cloud recognition and retrieval in road environments. RSSNet has two main branches. Firstly, a sequential slice module is designed to map disordered 3D point clouds to ordered sequence of shallow feature vectors. A gated recurrent unit (GRU) module is applied to encode the spatial and content information of these sequential vectors. The second branch consists of a key-point based graph convolution network (GCN) with an embedding attention strategy to fuse the sequential and global features to refine the structure discriminability. Three datasets were used to evaluate the proposed method, one acquired by our mobile laser scanning (MLS) system and two public datasets (KITTI and Sydney Urban Objects). Experimental results indicated that the proposed method achieved better performance than recognition and retrieval state-of-the-art methods. RSSNet provided recognition rates of 98.08%, 95.77% and 70.83% for the above three datasets, respectively. For the retrieval task, RSSNet obtained excellent mAP values of 95.56%, 87.16% and 69.99% on three datasets, respectively. Numéro de notice : A2020-064 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.003 Date de publication en ligne : 22/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.003 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94582
in ISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 147 - 163[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2020031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Object-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)
[article]
Titre : Object-based incremental registration of terrestrial point clouds in an urban environment Type de document : Article/Communication Auteurs : Xuming Ge, Auteur ; Han Hu, Auteur Année de publication : 2020 Article en page(s) : pp 218 - 232 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] compensation par moindres carrés
[Termes IGN] conception orientée objet
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] primitive géométrique
[Termes IGN] scène urbaine
[Termes IGN] semis de points
[Termes IGN] télémétrie laser terrestreRésumé : (Auteur) Registration of terrestrial point clouds is essential for large-scale urban applications. The robustness, accuracy, and runtime are generally given the highest priority in the design of appropriate algorithms. Most approaches that target general scenarios can only fulfill some of these factors, that is, robustness and accuracy come at the cost of increased runtime and vice versa. This paper proposes an object-based incremental registration strategy that accomplishes all of these objectives without the need for artificial targets, aiming at a specific scenario, the urban environment. The key is to decompose the degrees of freedom for the SE(3) transformation to three separate but closely related steps, considering that scanners are generally leveled in urban scenes: (1) 2D transformation with matches from line primitives, (2) vertical offset compensation by robust least-squares optimization, and (3) full SE(3) least-squares refinement using uniformly selected local patches. The robustness is prioritized in the whole pipeline, as structured first by a primitive-based registration and two least-squares optimizations with robust estimations that do not require specific keypoints. An object-based strategy for terrestrial point clouds is used to increase the reliability of the first step by the line primitives, which significantly reduces the search space without affecting the recall ratio. The least-squares optimization contributes to achieve a global optimum for the accurate registration. The three coupling steps are also more efficient than segregated coarse-to-fine registration. Experimental evaluations for point clouds acquired in both a metropolis and in old-style cities reveal that the proposed methods are superior to or on par with the state-of-the-art in robustness, accuracy, and runtime. In addition, the methods are also agnostic to the primitives adopted. Numéro de notice : A2020-066 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2020.01.020 Date de publication en ligne : 29/01/2020 En ligne : https://doi.org/10.1016/j.isprsjprs.2020.01.020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94584
in ISPRS Journal of photogrammetry and remote sensing > vol 161 (March 2020) . - pp 218 - 232[article]Réservation
Réserver ce documentExemplaires(3)
Code-barres Cote Support Localisation Section Disponibilité 081-2020031 RAB Revue Centre de documentation En réserve L003 Disponible 081-2020033 DEP-RECP Revue LASTIG Dépôt en unité Exclu du prêt 081-2020032 DEP-RECF Revue Nancy Dépôt en unité Exclu du prêt Unsupervised 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)Permalink