Centre dedocumentation
scientifique

Multi-modal learning in photogrammetry and remote sensing / Michael Ying Yang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 54 - 54 Titre : Multi-modal learning in photogrammetry and remote sensing Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Loïc Landrieu , Auteur ; Devis Tuia, Auteur ; Charles Toth, Auteur Année de publication : 2021 Projets : 1-Pas de projet / Article en page(s) : pp 54 - 54 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] acquisition d'images [Termes IGN] apprentissage automatique [Termes IGN] données multisources Résumé : (Auteur) [Editorial] There is a growing interest in the photogrammetry and remote sensing community for multi-modal data, i. e., data simultaneously acquired from a variety of platforms, including satellites, aircraft, UAS/UGS, autonomous vehicles, etc., by different sensors, such as radar, optical, LiDAR. Thanks to their different spatial, spectral, or temporal resolutions, the use of complementary data sources leads to richer and more robust information extraction. We expect that the use of multiple modalities will rapidly become a standard approach in the future. The main difficulty of jointly processing multi-modal data is due to the differences in structure among modalities. Another issue is the unbalanced number of labelled samples available across modalities, resulting in a significant gap in performance when models are trained separately. Clearly, the photogrammetry and remote sensing community has not yet exploited the full potential of multi-modal data. Neural networks seem well suited for accommodating different data sources, thanks to their capabilities to learn representations adapted to each task in an end-to-end fashion. In this context, there is a strong need for research and development of approaches for multi-sensory and multi-modal deep learning within the geospatial domain. Numéro de notice : A2021-364 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.03.022 date de publication en ligne : 23/04/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.03.022 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97660 [article]

Semantic façade segmentation from airborne oblique images / Yaping Lin in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 6 (June 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 6 (June 2019) . - pp 425 - 433 Titre : Semantic façade segmentation from airborne oblique images Type de document : Article/Communication Auteurs : Yaping Lin, Auteur ; Francesco Nex, Auteur ; Michael Ying Yang, Auteur Année de publication : 2019 Article en page(s) : pp 425 - 433 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] champ aléatoire conditionnel [Termes IGN] classification par forêts aléatoires [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] façade [Termes IGN] image aérienne oblique [Termes IGN] image RVB [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique Résumé : (Auteur) In this paper, oblique airborne images with very high resolution are used to address the problem from aerial views in urban areas. Traditional classification method (i.e., random forests) is compared with state-of-the-art fully convolutional networks (FCNs). Random forests use hand-craft image features including red, green, blue (RGB), scale-invariant feature transform (SIFT), and Texton, and point cloud features consisting of normal vector and planarity extracted from different scales. In contrast, the inputs of FCNs are the RGB bands and the third components of normal vectors. In both cases, three-dimensional (3D) features are projected back into the image space to support the facade interpretation. Fully connected conditional random field (CRF) is finally taken as a post-processing of the FCN to refine the segmentation results. Several tests have been performed and the achieved results show that the models embedding the 3D component outperform the solution using only images. FCNs significantly outperformed random forests, especially for the balcony delineation. Numéro de notice : A2019-247 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.6.425 date de publication en ligne : 01/06/2019 En ligne : https://doi.org/10.14358/PERS.85.6.425 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93003 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2019061	SL	Revue	Centre de documentation	Revues en salle	Disponible

Foreword to the theme issue on geospatial computer vision / Jan Dirk Wegner in ISPRS Journal of photogrammetry and remote sensing, vol 140 (June 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 140 (June 2018) . - pp 1 - 2 Titre : Foreword to the theme issue on geospatial computer vision Type de document : Article/Communication Auteurs : Jan Dirk Wegner, Auteur ; Devis Tuia, Auteur ; Michael Ying Yang, Auteur ; Clément Mallet , Auteur Année de publication : 2018 Projets : 1-Pas de projet / Article en page(s) : pp 1 - 2 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] données localisées [Termes IGN] vision par ordinateur Numéro de notice : A2018-387 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.011 date de publication en ligne : 09/01/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.011 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90801 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018061	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2018063	DEP-EXM	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2018062	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

vol 140 - June 2018 - Geospatial computer vision (Bulletin de ISPRS Journal of photogrammetry and remote sensing) / Jan Dirk Wegner  

Public [n° ou bulletin]  est un bulletin de ISPRS Journal of photogrammetry and remote sensing / International society for photogrammetry and remote sensing (1990 - )   Titre : vol 140 - June 2018 - Geospatial computer vision Type de document : Périodique Auteurs : Jan Dirk Wegner, Editeur scientifique ; Devis Tuia, Editeur scientifique ; Michael Ying Yang, Editeur scientifique ; Clément Mallet , Editeur scientifique Année de publication : 2018 Langues : Anglais (eng) Numéro de notice : 081-201806 Affiliation des auteurs : LaSTIG MATIS+Ext (2012-2019) Nature : Numéro de périodique nature-HAL : DirectOuvrColl/Actes En ligne : https://www.sciencedirect.com/journal/isprs-journal-of-photogrammetry-and-remote [...] Format de la ressource électronique : URL Sommaire Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=31066 [n° ou bulletin]

Contient

• Landmark based localization in urban environment / Xiaozhi Qu in ISPRS Journal of photogrammetry and remote sensing, vol 140 (June 2018)  
• Foreword to the theme issue on geospatial computer vision / Jan Dirk Wegner in ISPRS Journal of photogrammetry and remote sensing, vol 140 (June 2018)  

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018061	RAB	Revue	Centre de documentation	En réserve 3L	Disponible
081-2018063	DEP-EXM	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2018062	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Video event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models / Michael Ying Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 4 (April 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 4 (April 2018) . - pp 203 - 214 Titre : Video event recognition and anomaly detection by combining gaussian process and hierarchical dirichlet process models Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Wentong Liao, Auteur ; Yanpeng Cao, Auteur ; Bodo Rosenhahn, Auteur Année de publication : 2018 Article en page(s) : pp 203 - 214 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] agent (intelligence artificielle) [Termes IGN] apprentissage non-dirigé [Termes IGN] approche hiérarchique [Termes IGN] image vidéo [Termes IGN] modèle de Markov [Termes IGN] modèle orienté agent [Termes IGN] séquence d'images Résumé : (Auteur) In this paper, we present an unsupervised learning framework for analyzing activities and interactions in surveillance videos. In our framework, three levels of video events are connected by Hierarchical Dirichlet Process (HDP) model: low-level visual features, simple atomic activities, and multi-agent interactions. Atomic activities are represented as distribution of low-level features, while complicated interactions are represented as distribution of atomic activities. This learning process is unsupervised. Given a training video sequence, low-level visual features are extracted based on optic flow and then clustered into different atomic activities and video clips are clustered into different interactions. The HDP model automatically decides the number of clusters, i.e., the categories of atomic activities and interactions. Based on the learned atomic activities and interactions, a training dataset is generated to train the Gaussian Process (GP) classifier. Then, the trained GP models work in newly captured video to classify interactions and detect abnormal events in real time. Furthermore, the temporal dependencies between video events learned by HDP-Hidden Markov Models (HMM) are effectively integrated into GP classifier to enhance the accuracy of the classification in newly captured videos. Our framework couples the benefits of the generative model (HDP) with the discriminant model (GP). We provide detailed experiments showing that our framework enjoys favorable performance in video event classification in real-time in a crowded traffic scene. Numéro de notice : A2018-139 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.4.203 date de publication en ligne : 01/04/2018 En ligne : https://doi.org/10.14358/PERS.84.4.203 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89689 [article]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
105-2018041	RAB	Revue	Centre de documentation	En réserve 3L	Disponible

IV-1/W1 - ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17, 6–9 June 2017, Hannover, Germany (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, IV-1/W1 [30/05/2017]) / Christian Heipke  

Permalink

Foreword to the Special Issue on 'GeoVision: Computer Vision for Geospatial Applications' / Devis Tuia in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 9 n° 7 (July 2016)  

Permalink

II-3 W2 - November 2013 - WGIII/3 ISA13 – The ISPRS Workshop on Image Sequence Analysis 2013 [actes] (Bulletin de ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences) / Clément Mallet  

Permalink

Fusion of camera images and laser scans for wide baseline 3D scene alignment in urban environments / Michael Ying Yang in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 6 supplement (December 2011)  

Permalink