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A local structure and direction-aware optimization approach for three-dimensional tree modeling / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 8 (August 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 8 (August 2016) . - pp 4749 - 4757 Titre : A local structure and direction-aware optimization approach for three-dimensional tree modeling Type de document : Article/Communication Auteurs : Zhen Wang, Auteur ; Liqiang Zhang, Auteur ; Tian Fang, Auteur ; et al., Auteur Année de publication : 2016 Article en page(s) : pp 4749 - 4757 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] arbre (flore) [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] optimisation (mathématiques) [Termes IGN] reconstruction 3D [Termes IGN] semis de points [Termes IGN] squelettisation Résumé : (Auteur) Modeling 3-D trees from terrestrial laser scanning (TLS) point clouds remains a challenging task for several well-known reasons, including their complex structure and severe occlusions. In order to accurately reconstruct 3-D tree models from TLS point clouds that typically suffer from significant occlusions, in this paper, a novel local structure and direction-aware approach is presented to successfully complete missing structures of trees. In this method, we first extract the coarse tree skeleton from the input point cloud, and thus, the branch dominant direction and the point density of each branch are obtained. By a skeleton-based Laplacian algorithm, the point cloud is further shrunk into a skeleton point cloud to highlight the branch dominant direction of each branch. For obtaining even more accurate point densities, a dictionary-based algorithm is utilized to learn and reconstruct the local structure. Finally, the branch dominant direction and point density are integrated into an iterative optimization process to recover the missing data. Extensive experimental results have shown that the proposed method is very robust to incomplete data sets, and it is capable of accurately reconstructing 3-D trees, which are partially, or even to a large extent, missing from the input point cloud. Numéro de notice : A2016-890 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2551286 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2551286 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83070 [article]

A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 5 (mai 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 5 (mai 2015) . - pp 2409 - 2425 Titre : A multiscale and hierarchical feature extraction method for terrestrial laser scanning point cloud classification Type de document : Article/Communication Auteurs : Z. Wang, Auteur ; Liqiang Zhang, Auteur ; Tian Fang, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 2409 - 2425 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification orientée objet [Termes IGN] détection de piéton [Termes IGN] détection du bâti [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] extraction de traits caractéristiques [Termes IGN] lasergrammétrie [Termes IGN] objet mobile [Termes IGN] semis de points [Termes IGN] structure hiérarchique de données [Termes IGN] télémétrie laser terrestre [Termes IGN] zone urbaine dense Résumé : (Auteur) The effective extraction of shape features is an important requirement for the accurate and efficient classification of terrestrial laser scanning (TLS) point clouds. However, the challenge of how to obtain robust and discriminative features from noisy and varying density TLS point clouds remains. This paper introduces a novel multiscale and hierarchical framework, which describes the classification of TLS point clouds of cluttered urban scenes. In this framework, we propose multiscale and hierarchical point clusters (MHPCs). In MHPCs, point clouds are first resampled into different scales. Then, the resampled data set of each scale is aggregated into several hierarchical point clusters, where the point cloud of all scales in each level is termed a point-cluster set. This representation not only accounts for the multiscale properties of point clouds but also well captures their hierarchical structures. Based on the MHPCs, novel features of point clusters are constructed by employing the latent Dirichlet allocation (LDA). An LDA model is trained according to a training set. The LDA model then extracts a set of latent topics, i.e., a feature of topics, for a point cluster. Finally, to apply the introduced features for point-cluster classification, we train an AdaBoost classifier in each point-cluster set and obtain the corresponding classifiers to separate the TLS point clouds with varying point density and data missing into semantic regions. Compared with other methods, our features achieve the best classification results for buildings, trees, people, and cars from TLS point clouds, particularly for small and moving objects, such as people and cars. Numéro de notice : A2015-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2359951 En ligne : https://doi.org/10.1109/TGRS.2014.2359951 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=77533 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

A structure-aware global optimization method for reconstructing 3-D tree models from terrestrial laser scanning data / Z. Wang in IEEE Transactions on geoscience and remote sensing, vol 52 n° 9 Tome 2 (September 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 9 Tome 2 (September 2014) . - pp 5653 - 5669 Titre : A structure-aware global optimization method for reconstructing 3-D tree models from terrestrial laser scanning data Type de document : Article/Communication Auteurs : Z. Wang, Auteur ; L. Zhang, Auteur ; Tian Fang, Auteur Année de publication : 2014 Article en page(s) : pp 5653 - 5669 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] optimisation (mathématiques) [Termes IGN] reconstruction 3D Résumé : (Auteur) A 3-D tree structure plays an important role in many scientific fields, including forestry and agriculture. For example, terrestrial laser scanning (TLS) can efficiently capture high-precision 3-D spatial arrangements and structure of trees as a point cloud. In the past, several methods to reconstruct 3-D trees from the TLS point cloud were proposed. However, in general, they fail to process incomplete TLS data. To address such incomplete TLS data sets, a new method that is based on a structure-aware global optimization approach (SAGO) is proposed. The SAGO first obtains the approximate tree skeleton from a distance minimum spanning tree (DMst) and then defines the stretching directions of the branches on the tree skeleton. Based on these stretching directions, the SAGO recovers missing data in the incomplete TLS point cloud. The DMst is applied again to obtain the refined tree skeleton from the optimized data, and the tree skeleton is smoothed by employing a Laplacian function. To reconstruct 3-D tree models, the radius of each branch section is estimated, and leaves are added to form the crown geometry. The developed methodology has been extensively evaluated by employing a dozen TLS point clouds of various types of trees. Both qualitative and quantitative performance evaluation results have indicated that the SAGO is capable of effectively reconstructing 3-D tree models from grossly incomplete TLS point clouds with significant amounts of missing data. Numéro de notice : A2014-440 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2291815 En ligne : https://ieeexplore.ieee.org/document/6693725 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74171 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2014091B	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Graph-based feature selection for object-oriented classification in VHR airborne imagery / Tianen Chen in IEEE Transactions on geoscience and remote sensing, vol 49 n° 1 Tome 2 (January 2011)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 49 n° 1 Tome 2 (January 2011) . - pp 353 - 365 Titre : Graph-based feature selection for object-oriented classification in VHR airborne imagery Type de document : Article/Communication Auteurs : Tianen Chen, Auteur ; Tian Fang, Auteur ; H. Huo, Auteur ; D. Li, Auteur Année de publication : 2011 Article en page(s) : pp 353 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification dirigée [Termes IGN] classification orientée objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] graphe [Termes IGN] image à ultra haute résolution [Termes IGN] image aérienne [Termes IGN] Kappa de Cohen [Termes IGN] matrice [Termes IGN] pondération [Termes IGN] similitude [Termes IGN] voisinage (relation topologique) Résumé : (Auteur) Linearly nonseparability and class imbalance of very high resolution (VHR) imagery make feature selection for object-oriented classification quite challenging, while such characteristics, especially class imbalance, have usually been ignored in open literature. To cope with the challenges, this paper proposes a new graph-based feature selection method named locally weighted discriminating projection (LWDP). First, the popular graph-based criteria of feature selection are reformulated to present linear or nonlinear mapping in feature space. Second, weight matrices of graphs characterize dissimilarity rather than similarity between pairwise neighbors, to well-preserved local structure when the difference of distance between a sample and its neighbors is large. Finally, LWDP provides a new perspective to alleviate class imbalance at both global and local levels, by restricting the pairwise relationships in the weight matrices. Specifically, neighborhood unions are introduced to employ the local class distribution and class size to constrain pairwise relationships in the weight matrices when classifying unbalanced sample sets. To evaluate the performances of LWDP in low dimensions, a holistic scoring scheme is proposed to stress the performances under low dimensions. In addition, overall accuracy curves and Kappa Index of Agreement (KIA) curves, which exhibit KIA in dimensions, are also used. The experimental results show that LWDP and its kernel extension outperform the other classic or latest methods in processing unbalanced sample set of VHR airborne imagery. Numéro de notice : A2011-051 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2010.2054832 Date de publication en ligne : 12/08/2010 En ligne : https://doi.org/10.1109/TGRS.2010.2054832 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30832 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2011011B	RAB	Revue	Centre de documentation	En réserve L003	Disponible