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Titre : Filtering mislabeled data for improving time series classification Type de document : Article/Communication Auteurs : Charlotte Pelletier, Auteur ; Silvia Valero, Auteur ; Jordi Inglada, Auteur ; Gérard Dedieu, Auteur ; Nicolas Champion , 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] classification par forêts d'arbres décisionnels
[Termes IGN] données d'entrainement (apprentissage automatique)
[Termes IGN] image Sentinel-MSI
[Termes IGN] série temporelleRésumé : (auteur) The supervised classification of optical image time series allow the production of accurate land cover maps over large areas. However, the precision yielded by learning algorithms strongly depends on the quality of the reference data. The reference databases covering a large geographical area usually contain noisy data with an important number of mislabeled instances. These labeling errors result in longer training time, less accurate classifiers, and ultimately poorer results. To address this issue, we proposed a new iterative learning framework that removes mislabeled data from the training set. Specifically, a preliminary outlier rejection procedure based on the well-known Random Forest algorithm is proposed. The presented strategy is tested with the classification of Sentinel-2 image time series acquired on 2016 by using an out-of-date reference dataset collected on 2014. Numéro de notice : C2017-059 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/Multi-Temp.2017.8035217 Date de publication en ligne : 14/09/2017 En ligne : https://doi.org/10.1109/Multi-Temp.2017.8035217 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97497 Fully automatic analysis of archival aerial images : Current status and challenges / Sébastien Giordano (2017)
Titre : Fully automatic analysis of archival aerial images : Current status and challenges Type de document : Article/Communication Auteurs : Sébastien Giordano , Auteur ; Arnaud Le Bris , Auteur ; Clément Mallet , Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2017 Projets : 1-Pas de projet / 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] analyse d'image numérique
[Termes IGN] chaîne de traitement
[Termes IGN] modèle numérique de surface
[Termes IGN] orthophotoplan numérique
[Termes IGN] traitement d'imageRésumé : (auteur) Archival aerial images are a unique and relatively unexplored means to generate detailed land-cover information in 3D over the past 100 years. Many long-term environmental monitoring studies can be based on this type of image series. Such data provide a relatively dense temporal sampling of the territories with very high spatial resolution. Furthermore, photogrammetric workflows exist in order to both produce orthoimages and Digital Surface Models, with reasonable interactive actions. However, today, there is no fully automatic pipeline for generating such kind of data. This paper presents the main avenues of research in order to develop such workflow, starting from registration and radiometric issues up to land-cover classification challenges. Numéro de notice : C2017-030 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2017.7924620 Date de publication en ligne : 11/05/2017 En ligne : https://doi.org/10.1109/JURSE.2017.7924620 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89294 Hierarchically exploring the width of spectral bands for urban material classification / Arnaud Le Bris (2017)
Titre : Hierarchically exploring the width of spectral bands for urban material classification Type de document : Article/Communication Auteurs : Arnaud Le Bris , Auteur ; Nicolas Paparoditis , Auteur ; Nesrine Chehata , Auteur ; Xavier Briottet , 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 : 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] Acquisition d'image(s) et de donnée(s)
[Termes IGN] bande spectrale
[Termes IGN] capteur superspectral
[Termes IGN] limite de résolution géométrique
[Termes IGN] limite de résolution radiométrique
[Termes IGN] propriété optique des matériaux
[Termes IGN] réflectance de surface
[Termes IGN] signature spectrale
[Termes IGN] toit
[Termes IGN] zone urbaineRésumé : (auteur) In urban areas, material maps, i.e. knowledge concerning the roofing materials or the different kinds of ground areas, are necessary for several city modeling or monitoring applications. Airborne remote sensing techniques appear to be convenient for providing them at a large scale but require an enhanced imagery spectral resolution. A superspectral sensor with a limited number of bands dedicated to urban materials classification could be a solution. Within this context, this study focused on the optimization of this band subset from hyperspectral data, considering both the position of the bands and their width. The used approach first builds a hierarchy of groups of adjacent bands, according to a relevance criterion to decide which adjacent bands must be merged. Then, band selection is performed at the different levels of this hierarchy. Several band configurations are thus explored within this hierarchy. This method was applied to a data set consisting of spectra generated from reflectance spectral signatures of 9 common urban materials collected from 7 spectral libraries. At the end, the potential of a superspectral sensor with wider bands was confirmed. Numéro de notice : C2017-031 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/JURSE.2017.7924628 Date de publication en ligne : 11/05/2017 En ligne : https://doi.org/10.1109/JURSE.2017.7924628 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89295 How to combine lidar and very high resolution multispectral images for forest stand segmentation? / Clément Dechesne (2017)
Titre : How to combine lidar and very high resolution multispectral images for forest stand segmentation? Type de document : Article/Communication Auteurs : Clément Dechesne , Auteur ; Clément Mallet , Auteur ; Arnaud Le Bris , Auteur ; Valérie Gouet-Brunet , 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 : IGARSS 2017, IEEE International Geoscience And Remote Sensing Symposium 23/07/2017 28/07/2017 Fort Worth Texas - Etats-Unis Proceedings IEEE Importance : pp 2772 - 2775 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image infrarouge
[Termes IGN] image multibande
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] peuplement forestier
[Termes IGN] segmentation sémantique
[Termes IGN] semis de points
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Forest stands are a basic unit of analysis for forest inventory and mapping. Stands are defined as large forested areas of homogeneous tree species composition and age. Their accurate delineation is usually performed by human operators through visual analysis of very high resolution (VHR) infra-red and visible images. This task is tedious, highly time consuming, and needs to be automated for scalability and efficient updating purposes. The most appropriate fusion of two remote sensing modalities (lidar and multispectral images) is investigated here. The multispectral images give information about the tree species while 3D lidar point clouds provide geometric information. The fusion is operated at three different levels within a semantic segmentation workflow: over-segmentation, classification, and regularization. Results show that over-segmentation can be performed either on lidar or optical images without performance loss or gain, whereas fusion is mandatory for efficient semantic segmentation. Eventually, the fusion strategy dictates the composition and nature of the forest stands, assessing the high versatility of our approach. Numéro de notice : C2017-039 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IGARSS.2017.8127572 Date de publication en ligne : 04/12/2017 En ligne : https://doi.org/10.1109/IGARSS.2017.8127572 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91921
Titre : Image retrieval based on saliency for urban image contents Type de document : Article/Communication Auteurs : Kamel Guissous , Auteur ; Valérie Gouet-Brunet , 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 : IPTA 2017, 7th International Conference on Image Processing Theory, Tools and Applications 28/11/2017 01/12/2017 Montréal Canada Proceedings IEEE Importance : 6 p. Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] recherche d'image basée sur le contenu
[Termes IGN] saillance
[Termes IGN] scène urbaineRésumé : (auteur) With the increase of image datasets size and of descriptors complexity in Content-Based Image Retrieval (CBIR) and Computer Vision, it is essential to find a way to limit the amount of manipulated data, while keeping its quality. Instead of treating the entire image, the selection of regions which hold the essence of information is a relevant option to reach this goal. As the visual saliency aims at highlighting the areas of the image which are the most important for a given task, in this paper we propose to exploit visual saliency maps to prune the most salient image features. A novel visual saliency approach based on the local distribution analysis of the edges orientation, particularly dedicated to structured contents, such as street view images of urban environments, is proposed. It is evaluated for CBIR according to three criteria: quality of retrieval, volume of manipulated features and computation time. The proposal can be exploited into various applications involving large sets of local visual features; here it is experimented within two applications: cross-domain image retrieval and image-based vehicle localisation. Numéro de notice : C2017-040 Affiliation des auteurs : LASTIG MATIS (2012-2019) Autre URL associée : vers HAL Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/IPTA.2017.8310131 Date de publication en ligne : 12/03/2018 En ligne : https://doi.org/10.1109/IPTA.2017.8310131 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91922 New 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)PermalinkLandmark based localization: LBA refinement using MCMC-optimized projections of RJMCMC-extracted road marks / Bahman Soheilian (2016)PermalinkPermalinkPermalinkAssessment of the relevance of information derived from the unmixing of polarimetric radar images / Sébastien Giordano (2015)PermalinkContribution of textural information from TerraSAR-X image for forest mapping / Cécile Cazals (2015)PermalinkPermalinkFusion of Lidar and SAR data for land-cover mapping in natural environments / Clara Barbanson (2015)PermalinkKite-borne photogrammetry for decimetric 3D mapping of several square kilometres areas / Denis Feurer (2015)PermalinkPermalinkA Random Forest class memberships based wrapper band selection criterion : application to hyperspectral / Arnaud Le Bris (2015)PermalinkRetrieving the stand age from a retrospective detection of multinannual forest changes using Landsat data. Application on the heavily managed maritime pine forest in Southwestern France from a 30-year Landsat time-series (1984–2014) / Dominique Guyon (2015)PermalinkPermalinkPermalinkAgricultural field delimitation using active learning and random forests margin / Karim Ghariani (2014)PermalinkCombining top-down and bottom-up approaches for building detection in a single very high resolution satellite image / Mahmoud Mohammed Sidi Youssef (2014)Permalink