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Geometric features and their relevance for 3D point cloud classification / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-1/W1 (May 2017) . - pp 157 - 164 Titre : Geometric features and their relevance for 3D point cloud classification Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; Boris Jutzi, Auteur ; Clément Mallet , Auteur ; Michael Weinmann, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Conférence : ISPRS 2017, Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Annals Article en page(s) : pp 157 - 164 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classe d'objets [Termes IGN] classification [Termes IGN] données localisées 3D [Termes IGN] échantillonnage de données [Termes IGN] étiquette de classe [Termes IGN] extraction de traits caractéristiques [Termes IGN] interprétation automatique [Termes IGN] semis de points [Termes IGN] voisinage (relation topologique) Résumé : (auteur) In this paper, we focus on the automatic interpretation of 3D point cloud data in terms of associating a class label to each 3D point. While much effort has recently been spent on this research topic, little attention has been paid to the influencing factors that affect the quality of the derived classification results. For this reason, we investigate fundamental influencing factors making geometric features more or less relevant with respect to the classification task. We present a framework which consists of five components addressing point sampling, neighborhood recovery, feature extraction, classification and feature relevance assessment. To analyze the impact of the main influencing factors which are represented by the given point sampling and the selected neighborhood type, we present the results derived with different configurations of our framework for a commonly used benchmark dataset for which a reference labeling with respect to three structural classes (linear structures, planar structures and volumetric structures) as well as a reference labeling with respect to five semantic classes (Wire, Pole/Trunk, Façade, Ground and Vegetation) is available. Numéro de notice : A2017-860 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-1-W1-157-2017 Date de publication en ligne : 30/05/2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-1-W1-157-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89840 [article]

Investigating the potential of deep neural networks for large-scale classification of very high resolution satellite images / Tristan Postadjian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-1/W1 (May 2017) . - pp 183 - 190 Titre : Investigating the potential of deep neural networks for large-scale classification of very high resolution satellite images Type de document : Article/Communication Auteurs : Tristan Postadjian , Auteur ; Arnaud Le Bris , Auteur ; Hichem Sahbi, Auteur ; Clément Mallet , Auteur Année de publication : 2017 Projets : 1-Pas de projet / Conférence : ISPRS 2017, Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Annals Article en page(s) : pp 183 - 190 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] Brest [Termes IGN] carte d'occupation du sol [Termes IGN] classification [Termes IGN] géodatabase [Termes IGN] image satellite [Termes IGN] image SPOT 6 [Termes IGN] image SPOT 7 [Termes IGN] réseau neuronal convolutif Résumé : (auteur) Semantic classification is a core remote sensing task as it provides the fundamental input for land-cover map generation. The very recent literature has shown the superior performance of deep convolutional neural networks (DCNN) for many classification tasks including the automatic analysis of Very High Spatial Resolution (VHR) geospatial images. Most of the recent initiatives have focused on very high discrimination capacity combined with accurate object boundary retrieval. Therefore, current architectures are perfectly tailored for urban areas over restricted areas but not designed for large-scale purposes. This paper presents an end-to-end automatic processing chain, based on DCNNs, that aims at performing large-scale classification of VHR satellite images (here SPOT 6/7). Since this work assesses, through various experiments, the potential of DCNNs for country-scale VHR land-cover map generation, a simple yet effective architecture is proposed, efficiently discriminating the main classes of interest (namely buildings, roads, water, crops, vegetated areas) by exploiting existing VHR land-cover maps for training. Numéro de notice : A2017-861 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-1-W1-183-2017 Date de publication en ligne : 30/05/2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-1-W1-183-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89844 [article]

Disocclusion of 3D LiDAR point clouds using range images / Pierre Biasutti in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol IV-1/W1 (May 2017)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol IV-1/W1 (May 2017) . - pp 75 - 82 Titre : Disocclusion of 3D LiDAR point clouds using range images Type de document : Article/Communication Auteurs : Pierre Biasutti , Auteur ; Jean-François Aujol, Auteur ; Mathieu Brédif , Auteur ; Aurélie Bugeau, Auteur Année de publication : 2017 Projets : GOTMI / Papadakis, Nicolas Conférence : ISPRS 2017, Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Annals Article en page(s) : pp 75 - 82 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] objet mobile [Termes IGN] retouche [Termes IGN] scène urbaine [Termes IGN] semis de points Résumé : (auteur) This paper proposes a novel framework for the disocclusion of mobile objects in 3D LiDAR scenes aquired via street-based Mobile Mapping Systems (MMS). Most of the existing lines of research tackle this problem directly in the 3D space. This work promotes an alternative approach by using a 2D range image representation of the 3D point cloud, taking advantage of the fact that the problem of disocclusion has been intensively studied in the 2D image processing community over the past decade. First, the point cloud is turned into a 2D range image by exploiting the sensor’s topology. Using the range image, a semi-automatic segmentation procedure based on depth histograms is performed in order to select the occluding object to be removed. A variational image inpainting technique is then used to reconstruct the area occluded by that object. Finally, the range image is unprojected as a 3D point cloud. Experiments on real data prove the effectiveness of this procedure both in terms of accuracy and speed. Numéro de notice : A2017-898 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-IV-1-W1-75-2017 Date de publication en ligne : 30/05/2017 En ligne : https://doi.org/10.5194/isprs-annals-IV-1-W1-75-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91913 [article]