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vol V-1-2022 - 2022 edition - XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission I 2022 edition, 6–11 June 2022, Nice, France (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Stefan Hinz  

Public [n° ou bulletin]  est un bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences / International society for photogrammetry and remote sensing (1980 -) (2012 - )   Titre : vol V-1-2022 - 2022 edition - XXIV ISPRS Congress “Imaging today, foreseeing tomorrow”, Commission I 2022 edition, 6–11 June 2022, Nice, France Type de document : Périodique Auteurs : Stefan Hinz, Éditeur scientifique ; Raul Queiroz Feitosa, Éditeur scientifique ; Martin Weinmann, Éditeur scientifique ; Boris Jutzi, Éditeur scientifique Année de publication : 2022 Conférence : ISPRS 2022, Commission 3, 24th ISPRS Congress, Imaging today, foreseeing tomorrow 06/06/2022 11/06/2022 Nice France OA ISPRS Annals Langues : Français (fre) Numéro de notice : sans Affiliation des auteurs : non IGN Nature : Numéro de périodique En ligne : https://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/V-1-2022/index. [...] Format de la ressource électronique : URL bulletin Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=34194 [n° ou bulletin]

Contient

• Calibration of a light hemispherical radiance field imaging system / Manchun Lei in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)  
• Cooperative image orientation considering dynamic objects / P. Trusheim in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)  
• Application oriented quality evaluation of Gaofen-7 optical stereo satellite imagery / Jiaojiao Tian in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-1-2022 (2022 edition)  

VD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification / Jihao Li in ISPRS Journal of photogrammetry and remote sensing, vol 186 (April 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 186 (April 2022) . - pp 19 - 33 Titre : VD-LAB: A view-decoupled network with local-global aggregation bridge for airborne laser scanning point cloud classification Type de document : Article/Communication Auteurs : Jihao Li, Auteur ; Martin Weinmann, Auteur ; Xian Sun, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 19 - 33 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] agrégation de détails [Termes IGN] apprentissage profond [Termes IGN] précision de la classification [Termes IGN] qualité du modèle [Termes IGN] semis de points [Termes IGN] télémétrie laser aéroporté Résumé : (Auteur) Airborne Laser Scanning (ALS) point cloud classification is a valuable and practical task in the fields of photogrammetry and remote sensing. It takes an extremely important role in many applications of surveying, monitoring, planning, production and living. Recently, driven by the wave of deep learning, the classification of ALS point clouds has also been gradually shifting from traditional feature design to careful deep network architecture construction. Although significant progress has been achieved by leveraging deep learning technology, there are still some matters demanding prompt solution: (1) the coupling phenomenon of high-level semantic features from multiple field-of-views; (2) information propagation without aggregated local–global features in different levels of symmetrical structure; (3) quite serious class-imbalanced distribution problems in large-scale ALS point clouds. In this paper, to tackle these matters, we propose a novel View-Decoupled Network with Local–global Aggregation Bridge (VD-LAB) model. More concretely, a View-Decoupled (VD) grouping method is set at the deepest layer of the network. Then, we establish a Local–global Aggregation Bridge (LAB) between down-sampling path and up-sampling path of the same level. After that, a Self-Amelioration (SA) loss is taken as the optimization objective to train the whole model in an end-to-end manner. Extensive experiments on four challenging ALS point cloud datasets (LASDU, US3D, ISPRS 3D and GML) demonstrate that our VD-LAB is able to achieve state-of-the-art performance in terms of Overall Accuracy (OA) and mean -score (e.g., reaching 88.01% and 78.42% for LASDU dataset, respectively) with very few model parameters and it possesses a strong generalization capability. In addition, the visualization of achieved results also reveals more satisfactory classification for some categories, such as Water in the US3D dataset and Powerline in the ISPRS 3D dataset. Ultimately, the effect of each module in VD-LAB is assessed in detailed ablation analyses as well. Numéro de notice : A2022-067 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2022.01.012 Date de publication en ligne : 10/02/2022 En ligne : https://doi.org/10.1016/j.isprsjprs.2022.01.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99789 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022041	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2022043	DEP-RECP	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2022042	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

XXIV ISPRS Congress, Commission 1 / Nicolas Paparoditis (2020)  

Public Titre : XXIV ISPRS Congress, Commission 1 Type de document : Actes de congrès Auteurs : Nicolas Paparoditis , Éditeur scientifique ; Clément Mallet , Éditeur scientifique ; Florent Lafarge, Éditeur scientifique ; Stefan Hinz, Éditeur scientifique ; R. Feitosa, Éditeur scientifique ; Martin Weinmann, Éditeur scientifique ; Boris Jutzi, Éditeur scientifique Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2020 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 43-B1-2020 Conférence : ISPRS 2020, Commission 1, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France ISPRS OA Archives Commission 1 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] lasergrammétrie [Termes IGN] traitement d'image Numéro de notice : 17625 Affiliation des auteurs : ENSG+Ext (2020- ) Thématique : IMAGERIE Nature : Actes nature-HAL : DirectOuvrColl/Actes DOI : sans Date de publication en ligne : 06/08/2020 En ligne : https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLIII-B1-2020/in [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97136

Voir aussi

• XXIV ISPRS Congress, Commission 2 / Nicolas Paparoditis (2020)  
• XXIV ISPRS Congress, Commission 3 / Nicolas Paparoditis (2020)  
• XXIV ISPRS Congress, Commission 4 / Nicolas Paparoditis (2020)  
• XXIV ISPRS Congress, Commission 5 and Youth Forum / Nicolas Paparoditis (2020)  

Tree species classification using within crown localization of waveform LiDAR attributes / Rosmarie Blomley in ISPRS Journal of photogrammetry and remote sensing, vol 133 (November 2017)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 133 (November 2017) . - pp 142 - 156 Titre : Tree species classification using within crown localization of waveform LiDAR attributes Type de document : Article/Communication Auteurs : Rosmarie Blomley, Auteur ; Aarne Hovi, Auteur ; Martin Weinmann, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 142 - 156 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] analyse multiéchelle [Termes IGN] Betula pendula [Termes IGN] betula pubescens [Termes IGN] croissance des arbres [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] espèce végétale [Termes IGN] extraction de traits caractéristiques [Termes IGN] forêt boréale [Termes IGN] Norvège [Termes IGN] Picea abies [Termes IGN] Pinus sylvestris [Termes IGN] rotation d'objet Résumé : (Auteur) Since forest planning is increasingly taking an ecological, diversity-oriented perspective into account, remote sensing technologies are becoming ever more important in assessing existing resources with reduced manual effort. While the light detection and ranging (LiDAR) technology provides a good basis for predictions of tree height and biomass, tree species identification based on this type of data is particularly challenging in structurally heterogeneous forests. In this paper, we analyse existing approaches with respect to the geometrical scale of feature extraction (whole tree, within crown partitions or within laser footprint) and conclude that currently features are always extracted separately from the different scales. Since multi-scale approaches however have proven successful in other applications, we aim to utilize the within-tree-crown distribution of within-footprint signal characteristics as additional features. To do so, a spin image algorithm, originally devised for the extraction of 3D surface features in object recognition, is adapted. This algorithm relies on spinning an image plane around a defined axis, e.g. the tree stem, collecting the number of LiDAR returns or mean values of returns attributes per pixel as respective values. Based on this representation, spin image features are extracted that comprise only those components of highest variability among a given set of library trees. The relative performance and the combined improvement of these spin image features with respect to non-spatial statistical metrics of the waveform (WF) attributes are evaluated for the tree species classification of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.) and Silver/Downy birch (Betula pendula Roth/Betula pubescens Ehrh.) in a boreal forest environment. This evaluation is performed for two WF LiDAR datasets that differ in footprint size, pulse density at ground, laser wavelength and pulse width. Furthermore, we evaluate the robustness of the proposed method with respect to internal parameters and tree size. The results reveal, that the consideration of the crown-internal distribution of within-footprint signal characteristics captured in spin image features improves the classification results in nearly all test cases Numéro de notice : A2017-724 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88409 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017113	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds / Loïc Landrieu in ISPRS Journal of photogrammetry and remote sensing, vol 132 (October 2017)    

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 132 (October 2017) . - pp 102 - 118 Titre : A structured regularization framework for spatially smoothing semantic labelings of 3D point clouds Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Hugo Raguet, Auteur ; Bruno Vallet , Auteur ; Clément Mallet , Auteur ; Martin Weinmann, Auteur Année de publication : 2017 Projets : 1-Pas de projet / Article en page(s) : pp 102 - 118 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] attribut sémantique [Termes IGN] données localisées 3D [Termes IGN] interprétation automatique [Termes IGN] lissage de données [Termes IGN] optimisation (mathématiques) [Termes IGN] régularisation d'image [Termes IGN] scène [Termes IGN] semis de points Résumé : (Auteur) In this paper, we introduce a mathematical framework for obtaining spatially smooth semantic labelings of 3D point clouds from a pointwise classification. We argue that structured regularization offers a more versatile alternative to the standard graphical model approach. Indeed, our framework allows us to choose between a wide range of fidelity functions and regularizers, influencing the properties of the solution. In particular, we investigate the conditions under which the smoothed labeling remains probabilistic in nature, allowing us to measure the uncertainty associated with each label. Finally, we present efficient algorithms to solve the corresponding optimization problems. To demonstrate the performance of our approach, we present classification results derived for standard benchmark datasets. We demonstrate that the structured regularization framework offers higher accuracy at a lighter computational cost in comparison to the classic graphical model approach. Numéro de notice : A2017-641 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.08.010 Date de publication en ligne : 11/09/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.08.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86998 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2017101	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2017102	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt
081-2017103	DEP-EXM	Revue	Saint-Mandé	Dépôt en unité	Exclu du prêt

Documents numériques

en open access A structured regularization framework ... - preprint Adobe Acrobat PDF

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)  

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Analytical and numerical investigations on the accuracy and robustness of geometric features extracted from 3D point cloud data / André Dittrich in ISPRS Journal of photogrammetry and remote sensing, vol 126 (April 2017)  

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A classification-segmentation framework for the detection of individual trees in dense MMS point cloud data acquired in urban areas / Martin Weinmann in Remote sensing, vol 9 n° 3 (March 2017)  

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Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)    

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Involving different neighborhood types for the analysis of low-level geometric 2D and 3D features and their relevance for point cloud classification / Martin Weinmann (2017)    

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Detection, segmentation and localization of individual trees from MMS point cloud data / Martin Weinmann (2016)    

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Segmentation and localization of individual trees from MMS point cloud data acquired in urban areas / Martin Weinmann (2016)    

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Effiziente Interpretation von 3D-Punktwolken durch die Abschätzung der Relevanz von Merkmalen / Martin Weinmann in AVN Allgemeine Vermessungs-Nachrichten, vol 2015 n° 10 (Oktober 2015)    

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Semantic point cloud interpretation based on optimal neighborhoods, relevant features and efficient classifiers / Martin Weinmann in ISPRS Journal of photogrammetry and remote sensing, vol 105 (July 2015)  

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Distinctive 2D and 3D features for automated large-scale scene analysis in urban areas / Martin Weinmann in Computers and graphics, vol 49 (June 2015)  

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