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Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds / Zhou Guo in International journal of geographical information science IJGIS, vol 34 n° 4 (April 2020)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 34 n° 4 (April 2020) . - pp 661 - 680 Titre : Using multi-scale and hierarchical deep convolutional features for 3D semantic classification of TLS point clouds Type de document : Article/Communication Auteurs : Zhou Guo, Auteur ; Chen-Chieh Feng, Auteur Année de publication : 2020 Article en page(s) : pp 661 - 680 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse multiéchelle [Termes IGN] apprentissage profond [Termes IGN] approche hiérarchique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] données lidar [Termes IGN] modélisation 3D [Termes IGN] Oakland (Californie) [Termes IGN] régression [Termes IGN] semis de points Résumé : (auteur) Point cloud classification, which provides meaningful semantic labels to the points in a point cloud, is essential for generating three-dimensional (3D) models. Its automation, however, remains challenging due to varying point densities and irregular point distributions. Adapting existing deep-learning approaches for two-dimensional (2D) image classification to point cloud classification is inefficient and results in the loss of information valuable for point cloud classification. In this article, a new approach that classifies point cloud directly in 3D is proposed. The approach uses multi-scale features generated by deep learning. It comprises three steps: (1) extract single-scale deep features using 3D convolutional neural network (CNN); (2) subsample the input point cloud at multiple scales, with the point cloud at each scale being an input to the 3D CNN, and combine deep features at multiple scales to form multi-scale and hierarchical features; and (3) retrieve the probabilities that each point belongs to the intended semantic category using a softmax regression classifier. The proposed approach was tested against two publicly available point cloud datasets to demonstrate its performance and compared to the results produced by other existing approaches. The experiment results achieved 96.89% overall accuracy on the Oakland dataset and 91.89% overall accuracy on the Europe dataset, which are the highest among the considered methods. Numéro de notice : A2020-109 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1552790 Date de publication en ligne : 10/12/2018 En ligne : https://doi.org/10.1080/13658816.2018.1552790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94711 [article]

Polygonal clustering analysis using multilevel graph-partition / Wanyi Wang in Transactions in GIS, vol 19 n° 5 (October 2015)  

Public [article]  inTransactions in GIS > vol 19 n° 5 (October 2015) . - pp 716 – 736 Titre : Polygonal clustering analysis using multilevel graph-partition Type de document : Article/Communication Auteurs : Wanyi Wang, Auteur ; Shihong Du, Auteur ; Zhou Guo, Auteur ; Liqun Luo, Auteur Année de publication : 2015 Article en page(s) : pp 716 – 736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] analyse comparative [Termes IGN] analyse de groupement [Termes IGN] connexité (graphes) [Termes IGN] distance [Termes IGN] données spatiotemporelles [Termes IGN] figure géométrique [Termes IGN] groupe [Termes IGN] partition des données [Termes IGN] polygone [Termes IGN] similitude Résumé : (auteur) Existing methods of spatial data clustering have focused on point data, whose similarity can be easily defined. Due to the complex shapes and alignments of polygons, the similarity between non-overlapping polygons is important to cluster polygons. This study attempts to present an efficient method to discover clustering patterns of polygons by incorporating spatial cognition principles and multilevel graph partition. Based on spatial cognition on spatial similarity of polygons, four new similarity criteria (i.e. the distance, connectivity, size and shape) are developed to measure the similarity between polygons, and used to visually distinguish those polygons belonging to the same clusters from those to different clusters. The clustering method with multilevel graph-partition first coarsens the graph of polygons at multiple levels, using the four defined similarities to find clusters with maximum similarity among polygons in the same clusters, then refines the obtained clusters by keeping minimum similarity between different clusters. The presented method is a general algorithm for discovering clustering patterns of polygons and can satisfy various demands by changing the weights of distance, connectivity, size and shape in spatial similarity. The presented method is tested by clustering residential areas and buildings, and the results demonstrate its usefulness and universality. Numéro de notice : A2015-684 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12124 En ligne : http://dx.doi.org/10.1111/tgis.12124 Format de la ressource électronique : Url artticle Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78325 [article]