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A stixel approach for enhancing semantic image segmentation using prior map information / Sylvain Jonchery (2018)
Titre : A stixel approach for enhancing semantic image segmentation using prior map information Type de document : Article/Communication Auteurs : Sylvain Jonchery, Auteur ; Guillaume Bresson, Auteur ; Bruno Vallet , Auteur ; Rafal Żbikowski, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : ICARCV 2018, 15th International Conference on Control, Automation, Robotics and Vision 10/11/2018 21/11/2018 Singapour Singapour Proceedings IEEE Importance : pp 1715 - 1720 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] précision de la classification
[Termes IGN] scène urbaine
[Termes IGN] segmentation sémantiqueRésumé : (auteur) A key problem for autonomous car navigation is the understanding, at an object level, of the current driving situation. Addressing this issue requires the extraction of meaningful information from on-board stereo imagery by classifying the fundamental elements of urban scenes into semantic categories that can more easily be interpreted and be reflected upon (streets, buildings, pedestrians, vehicles, signs, etc.). A probabilistic method is proposed to fuse a coarse prior 3D map data with stereo imagery classification. A novel fusion architecture based on the Stixel framework is presented for combining semantic pixel-wise segmentation from a convolutional neural network (CNN) with depth information obtained from stereo imagery while integrating coarse prior depth and label information. The proposed approach was tested on a manually labeled data set in urban environments. The results show that the classification accuracy of the fundamental elements composing the urban scene was significantly enhanced by this method compared to what is obtained from the semantic pixel-wise segmentation of a CNN alone. Numéro de notice : C2018-094 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICARCV.2018.8581150 Date de publication en ligne : 20/12/2018 En ligne : https://doi.org/10.1109/ICARCV.2018.8581150 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94247 Superpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks / Tristan Postadjian (2018)
Titre : Superpixel partitioning of very high resolution satellite images for large-scale classification perspectives with deep convolutional neural networks Type de document : Article/Communication Auteurs : Tristan Postadjian , Auteur ; Arnaud Le Bris , Auteur ; Hichem Sahbi, Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2018 Projets : GeoSud / Conférence : IGARSS 2018, IEEE International Geoscience And Remote Sensing Symposium, observing, understanding and forecasting the dynamics of our planet 22/07/2018 27/07/2018 Valencia Espagne Proceedings IEEE Importance : pp 1328 - 1331 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage profond
[Termes IGN] base de données topographiques
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification pixellaire
[Termes IGN] image à très haute résolution
[Termes IGN] image infrarouge
[Termes IGN] image RVB
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation d'imageRésumé : (auteur) Supervised classification is the fundamental task for landcover map generation. Deep neural networks recently outperformed other state-of-the-art classifiers in many machine learning challenges, from semantic segmentation to speech recognition. Such strategies are now commonly employed in the literature for the purpose of land-cover mapping. This paper develops the strategy for the use of deep networks to label very high resolution satellite images, with the perspective of mapping regions at country scale. Therefore, a superpixel based method is introduced in order to (i) ensure correct delineation of objects and (ii) perform the classification in a dense way but with decent computing times. Numéro de notice : C2018-056 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2018.8519222 Date de publication en ligne : 05/11/2018 En ligne : https://doi.org/10.1109/IGARSS.2018.8519222 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91370 Automatic production of large-scale cloud-free orthomosaics from multitemporal satellite images / Nicolas Champion (2017)
Titre : Automatic production of large-scale cloud-free orthomosaics from multitemporal satellite images Type de document : Article/Communication Auteurs : Nicolas Champion , Auteur ; Emilie Le Hir, Auteur ; Stéphane Massera , Auteur ; Nicolas Bellaiche , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Projets : 2-Pas d'info accessible - article non ouvert / Conférence : Multitemp 2017, 9th International Workshop on the Analysis of Multitemporal Remote Sensing Images 27/06/2017 29/06/2017 Brugge Belgique Proceedings IEEE Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] chaîne de traitement
[Termes IGN] continuité cartographique
[Termes IGN] harmonisation des données
[Termes IGN] image Pléiades-HR
[Termes IGN] image SPOT 6
[Termes IGN] image SPOT 7
[Termes IGN] masque
[Termes IGN] orthophotoplan numériqueRésumé : (auteur) The main goal of this paper is to present the automatic pipeline designed at IGN-France to produce automatic large-scale cloud-free orthomosaics from multiple Spot 6/7 and Pléiades-HR satellite images. Our pipeline is composed of three steps: the radiometric harmonisation of satellite images, the automatic production of cloud masks and the stitching procedure. Our evaluation shows that our pipeline is capable to remove all clouds in a given scene and produce seamless orthomosaics. Numéro de notice : C2017-058 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/Multi-Temp.2017.8035214 Date de publication en ligne : 14/09/2017 En ligne : https://doi.org/10.1109/Multi-Temp.2017.8035214 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97496 Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data / Loïc Landrieu (2017)
Titre : Comparison of belief propagation and graph-cut approaches for contextual classification of 3D LIDAR point cloud data Type de document : Article/Communication Auteurs : Loïc Landrieu , Auteur ; Clément Mallet , Auteur ; Martin Weinmann, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Autre Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN (2012-) Projets : 1-Pas de projet / Conférence : IGARSS 2017, IEEE International Geoscience And Remote Sensing Symposium 23/07/2017 28/07/2017 Fort Worth Texas - Etats-Unis Proceedings IEEE Importance : pp 2768 - 2771 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme Graph-Cut
[Termes IGN] analyse comparative
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inférence
[Termes IGN] semis de points
[Termes IGN] test de performance
[Termes IGN] théorie de Dempster-ShaferRésumé : (auteur) In this paper, we focus on the classification of lidar point cloud data acquired via mobile laser scanning, whereby the classification relies on a context model based on a Conditional Random Field (CRF). We present two approximate inference algorithms based on belief propagation, as well as a graph-cut-based approach not yet applied in this context. To demonstrate the performance of our approach, we present the classification results derived for a standard benchmark dataset. These results clearly indicate that the graph-cut-based method is able to retrieve a labeling of higher likelihood in only a fraction of the time needed for the other approaches. The higher likelihood, in turn, translates into a significant gain in the accuracy of the obtained classification. Numéro de notice : C2017-026 Affiliation des auteurs : IGN+Ext (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2017.8127571 Date de publication en ligne : 04/12/2017 En ligne : https://doi.org/10.1109/IGARSS.2017.8127571 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89289 Documents numériques
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Comparison of belief propagation ... - postprintAdobe Acrobat PDF
Titre : Cross-domain image localization by adaptive feature fusion Type de document : Article/Communication Auteurs : Neelanjan Bhowmik , Auteur ; Li Weng , Auteur ; Valérie Gouet-Brunet , Auteur ; Bahman Soheilian , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Projets : POEME / Da Silva, Jean-Claude Conférence : JURSE 2017, Joint urban remote sensing event 06/03/2017 08/03/2017 Lausanne Suisse Proceedings IEEE Importance : 4 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] appariement d'images
[Termes IGN] environnement de développement
[Termes IGN] estimation de pose
[Termes IGN] géopositionnement
[Termes IGN] modèle de régression
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] recherche d'information géographique
[Termes IGN] similitudeRésumé : (auteur) We address the problem of cross-domain image localization, i.e., the ability of estimating the pose of a landmark from visual content acquired under various conditions, such as old photographs, paintings, photos taken at a particular season, etc. We explore a 2D approach where the pose is estimated from geo-localized reference images that visually match the query image. This work focuses on the retrieval of similar images, which is a challenging task for images across different domains. We propose a Content-Based Image Retrieval (CBIR) framework that adaptively combines multiple image descriptions. A regression model is used to select the best feature combinations according to their spatial complementarity, globally for a whole dataset as well as adaptively for each given image. The framework is evaluated on different datasets and the experiments prove its advantage over classical retrieval approaches. Numéro de notice : C2017-028 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2017.7924572 Date de publication en ligne : 11/05/2017 En ligne : https://doi.org/10.1109/JURSE.2017.7924572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89292 PermalinkFully automatic analysis of archival aerial images : Current status and challenges / Sébastien Giordano (2017)PermalinkHierarchically exploring the width of spectral bands for urban material classification / Arnaud Le Bris (2017)PermalinkHow to combine lidar and very high resolution multispectral images for forest stand segmentation? / Clément Dechesne (2017)PermalinkPermalinkNew iterative learning strategy to improve classification systems by using outlier detection techniques / Charlotte Pelletier (2017)PermalinkUrban objects classification by spectral library: Feasibility and applications / Walid Ouerghemmi (2017)PermalinkAn assessment of image features and random forest for land cover mapping over large areas using high resolution Satellite Image Time Series / Charlotte Pelletier (2016)PermalinkPermalinkFirst results from the GLORIE polarimetric GNSS-R airborne campaign dedicated to land parameters estimation / Erwan Motte (2016)Permalink