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Auteur Martin Weinmann |
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Contextual classification of point cloud data by exploiting individual 3d neigbourhoods / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W4 (March 2015)
[article]
Titre : Contextual classification of point cloud data by exploiting individual 3d neigbourhoods Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; A. Schmidt, Auteur ; Clément Mallet , Auteur ; Stefan Hinz, Auteur ; Franz Rottensteiner, Auteur ; Boris Jutzi, Auteur Année de publication : 2015 Conférence : ISPRS 2015, PIA 2015 - HRIGI 2015 Joint ISPRS conference 25/03/2015 27/03/2015 Munich Allemagne ISPRS OA Annals Article en page(s) : pp 271 - 278 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse d'image orientée objet
[Termes IGN] classification contextuelle
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] scène urbaine
[Termes IGN] semis de points
[Termes IGN] voisinage (relation topologique)
[Termes IGN] zone urbaineRésumé : (auteur) The fully automated analysis of 3D point clouds is of great importance in photogrammetry, remote sensing and computer vision. For reliably extracting objects such as buildings, road inventory or vegetation, many approaches rely on the results of a point cloud classification, where each 3D point is assigned a respective semantic class label. Such an assignment, in turn, typically involves statistical methods for feature extraction and machine learning. Whereas the different components in the processing workflow have extensively, but separately been investigated in recent years, the respective connection by sharing the results of crucial tasks across all components has not yet been addressed. This connection not only encapsulates the interrelated issues of neighborhood selection and feature extraction, but also the issue of how to involve spatial context in the classification step. In this paper, we present a novel and generic approach for 3D scene analysis which relies on (i) individually optimized 3D neighborhoods for (ii) the extraction of distinctive geometric features and (iii) the contextual classification of point cloud data. For a labeled benchmark dataset, we demonstrate the beneficial impact of involving contextual information in the classification process and that using individual 3D neighborhoods of optimal size significantly increases the quality of the results for both pointwise and contextual classification. Numéro de notice : A2015--052 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-W4-271-2015 Date de publication en ligne : 11/03/2015 En ligne : http://dx.doi.org/10.5194/isprsannals-II-3-W4-271-2015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82698
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-3 W4 (March 2015) . - pp 271 - 278[article]Documents numériques
en open access
Contextual classification of point cloud data ... - pdf éditeurAdobe Acrobat PDF Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 (September 2014)
[article]
Titre : Semantic 3D scene interpretation: A framework combining optimal neighborhood size selection with relevant features Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; Boris Jutzi, Auteur ; Clément Mallet , Auteur Année de publication : 2014 Conférence : PCV 2014, ISPRS Technical Commission 3 Symposium Photogrammetric Computer vision 05/09/2014 07/09/2014 Zurich Suisse OA ISPRS Annals Article en page(s) : pp 181 - 188 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] analyse d'image numérique
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] extraction de points
[Termes IGN] scène urbaine
[Termes IGN] semis de pointsRésumé : (auteur) 3D scene analysis by automatically assigning 3D points a semantic label has become an issue of major interest in recent years. Whereas the tasks of feature extraction and classification have been in the focus of research, the idea of using only relevant and more distinctive features extracted from optimal 3D neighborhoods has only rarely been addressed in 3D lidar data processing. In this paper, we focus on the interleaved issue of extracting relevant, but not redundant features and increasing their distinctiveness by considering the respective optimal 3D neighborhood of each individual 3D point. We present a new, fully automatic and versatile framework consisting of four successive steps: (i) optimal neighborhood size selection, (ii) feature extraction, (iii) feature selection, and (iv) classification. In a detailed evaluation which involves 5 different neighborhood definitions, 21 features, 6 approaches for feature subset selection and 2 different classifiers, we demonstrate that optimal neighborhoods for individual 3D points significantly improve the results of scene interpretation and that the selection of adequate feature subsets may even further increase the quality of the derived results. Numéro de notice : A2014-799 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-3-181-2014 Date de publication en ligne : 07/08/2014 En ligne : http://dx.doi.org/10.5194/isprsannals-II-3-181-2014 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82699
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-3 (September 2014) . - pp 181 - 188[article]Feature relevance assessment for the semantic interpretation of 3D point cloud data / Martin Weinmann in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-5 W2 (November 2013)
[article]
Titre : Feature relevance assessment for the semantic interpretation of 3D point cloud data Type de document : Article/Communication Auteurs : Martin Weinmann, Auteur ; Boris Jutzi, Auteur ; Clément Mallet , Auteur Année de publication : 2013 Conférence : ISPRS 2013, Workshop Laser Scanning 11/11/2013 13/11/2013 Antalya Turquie OA ISPRS Annals Article en page(s) : pp 313 - 318 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] semis de points Résumé : (auteur) The automatic analysis of large 3D point clouds represents a crucial task in photogrammetry, remote sensing and computer vision. In this paper, we propose a new methodology for the semantic interpretation of such point clouds which involves feature relevance assessment in order to reduce both processing time and memory consumption. Given a standard benchmark dataset with 1.3 million 3D points, we first extract a set of 21 geometric 3D and 2D features. Subsequently, we apply a classifier-independent ranking procedure which involves a general relevance metric in order to derive compact and robust subsets of versatile features which are generally applicable for a large variety of subsequent tasks. This metric is based on 7 different feature selection strategies and thus addresses different intrinsic properties of the given data. For the example of semantically interpreting 3D point cloud data, we demonstrate the great potential of smaller subsets consisting of only the most relevant features with 4 different state-of-the-art classifiers. The results reveal that, instead of including as many features as possible in order to compensate for lack of knowledge, a crucial task such as scene interpretation can be carried out with only few versatile features and even improved accuracy. Numéro de notice : A2013-812 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprsannals-II-5-W2-313-2013 En ligne : http://dx.doi.org/10.5194/isprsannals-II-5-W2-313-2013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80665
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol II-5 W2 (November 2013) . - pp 313 - 318[article]Documents numériques
en open access
Feature relevance assessmentAdobe Acrobat PDF