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A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification / Walid Ouerghemmi (2017)
contenu dans ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17 / Christian Heipke (2017)
Titre : A two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification Type de document : Article/Communication Auteurs : Walid Ouerghemmi , Auteur ; Arnaud Le Bris , Auteur ; Nesrine Chehata , Auteur ; Clément Mallet , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2017 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 42-1/W1 Projets : HYEP / Weber, Christiane Conférence : ISPRS 2017, Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Annals Importance : pp 167 - 174 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification orientée objet
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] image multibande
[Termes IGN] incertitude géométrique
[Termes IGN] précision de la classificationRésumé : (auteur) Very high spatial resolution multispectral images and lower spatial resolution hyperspectral images are complementary sources for urban object classification. The first enables a fine delineation of objects, while the second can better discriminate classes and consider richer land cover semantics. This paper presents a decision fusion scheme taking advantage of both sources classification maps, to produce a better classification map. The proposed method aims at dealing with both semantic and spatial uncertainties and consists in two steps. First, class membership maps are merged at pixel level. Several fusion rules are considered and compared in this study. Secondly, classification is obtained from a global regularization of a graphical model, involving a fit-to-data term related to class membership measures and an image based contrast sensitive regularization term. Results are presented on three datasets. The classification accuracy is improved up to 5 %, with comparison to the best single source classification accuracy. Numéro de notice : C2017-022 Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLII-1-W1-167-2017 Date de publication en ligne : 10/06/2017 En ligne : https://doi.org/10.5194/isprs-archives-XLII-1-W1-167-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89282 Weakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)
contenu dans ISPRS Hannover Workshop: HRIGI 17 – CMRT 17 – ISA 17 – EuroCOW 17 / Christian Heipke (2017)
Titre : Weakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds Type de document : Article/Communication Auteurs : Stéphane Guinard , Auteur ; Loïc Landrieu , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2017 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 42-1/W1 Conférence : ISPRS 2017 Workshops HRIGI – CMRT – ISA – EuroCOW 06/06/2017 09/06/2017 Hanovre Allemagne ISPRS OA Archives Importance : pp 151 - 157 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] carte de confiance
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] classification dirigée
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] scène urbaine
[Termes IGN] segmentation
[Termes IGN] semis de pointsRésumé : (auteur) We consider the problem of the semantic classification of 3D LiDAR point clouds obtained from urban scenes when the training set is limited. We propose a non-parametric segmentation model for urban scenes composed of anthropic objects of simple shapes, partionning the scene into geometrically-homogeneous segments which size is determined by the local complexity. This segmentation can be integrated into a conditional random field classifier (CRF) in order to capture the high-level structure of the scene. For each cluster, this allows us to aggregate the noisy predictions of a weakly-supervised classifier to produce a higher confidence data term. We demonstrate the improvement provided by our method over two publicly-available large-scale data sets. Numéro de notice : C2017-034 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLII-1-W1-151-2017 Date de publication en ligne : 31/05/2017 En ligne : https://doi.org/10.5194/isprs-archives-XLII-1-W1-151-2017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89327
Titre de série : 23th ISPRS international congress, 3 Titre : Commission III Type de document : Actes de congrès Auteurs : Lena Halounova, Éditeur scientifique ; Konrad Schindler, Éditeur scientifique ; A. Limpouch, Éditeur scientifique ; T. Pajdla, Éditeur scientifique ; V. Safar, Éditeur scientifique ; Helmut Mayer, Éditeur scientifique ; Sander J. Oude Elberink, Éditeur scientifique ; Clément Mallet , Éditeur scientifique ; Franz Rottensteiner, Éditeur scientifique ; Mathieu Brédif , Éditeur scientifique ; Jan Skaloud, Éditeur scientifique ; Uwe Stilla, Éditeur scientifique Congrès : ISPRS 2016, 23th international congress (12 - 19 juillet 2016; Prague, République tchèque), Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3 Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3 Langues : Anglais (eng) Numéro de notice : 17375C Affiliation des auteurs : LASTIG MATIS+Ext (2012-2019) Thématique : IMAGERIE Nature : Actes nature-HAL : DirectOuvrColl/Actes Date de publication en ligne : 09/06/2016 En ligne : http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B3/index.html Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91850 Contient
- Automatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)
- Forest stand segmentation using airborne lidar data and very high resolution multispectral imagery / Clément Dechesne (2016)
- Evaluation of SIFT and SURF for vision based localization / Xiaozhi Qu (2016)
- Uncertainty propagation for terrestrial mobile laser scanner / Miloud Mezian (2016)
- The iQmulus urban showcase: automatic tree classification and identification in huge mobile mapping point clouds / Jan Böhm (2016)
contenu dans EuroSDR contributions to ISPRS Congress XXIII, 12 - 19 July 2016, Special Session 12 – EuroSDR Prague, Czech Republic / European Spatial Data Research EuroSDR (02/2017)
Titre : Automated generalisation within NMAS in 2016 Type de document : Article/Communication Auteurs : Jantien E. Stoter, Auteur ; Vincent Van Altena, Auteur ; Marc Post, Auteur ; Dirk Burghardt, Auteur ; Cécile Duchêne , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B4 Conférence : ISPRS 2016, Commission 4, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 4 Importance : pp 647 - 652 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] base de données multi-représentation
[Termes IGN] chaîne de traitement
[Termes IGN] état de l'art
[Termes IGN] généralisation cartographique automatisée
[Termes IGN] organisme cartographique national
[Vedettes matières IGN] GénéralisationRésumé : (auteur) Producing maps and geo-data at different scales is traditionally one of the main tasks of National (and regional) Mapping Agencies (NMAs). The derivation of low-scale maps (i.e. with less detail) from large-scale maps (with more detail), i.e. generalisation, used to be a manual task of cartographers. With the need for more up-to-date data as well as the development of automated generalisation solutions in both research and industry, NMAs are implementing automated generalisation production lines. To exchange experiences and identify remaining issues, a workshop was organised end 2015 by the Commission on Generalisation and Multirepresentation of the International Cartographic Association and the Commission on Modelling and Processing of the European Spatial Data Research. This paper reports about the workshop outcomes. It shows that, most NMAs have implemented a certain form of automation in their workflows, varying from generalisation of certain features while still maintaining a manual workflow; semiautomated editing and generalisation to a fully automated procedure. Numéro de notice : C2016-015 Affiliation des auteurs : LASTIG COGIT+Ext (2012-2019) Thématique : GEOMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B4-647-2016 Date de publication en ligne : 14/06/2016 En ligne : http://doi.org/10.5194/isprs-archives-XLI-B4-647-2016 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84320 Automatic detection of clouds and shadows using high resolution satellite image time series / Nicolas Champion (2016)
Titre : Automatic detection of clouds and shadows using high resolution satellite image time series Type de document : Article/Communication Auteurs : Nicolas Champion , Auteur Editeur : International Society for Photogrammetry and Remote Sensing ISPRS Année de publication : 2016 Collection : International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, ISSN 1682-1750 num. 41-B3 Projets : 1-Pas de projet / Weber, Christiane Conférence : ISPRS 2016, Commission 3, 23th international congress 12/07/2016 19/07/2016 Prague République tchèque ISPRS OA Archives Commission 3 Importance : pp 475 - 479 Format : 21 x 30 cm Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] détection d'ombre
[Termes IGN] détection des nuages
[Termes IGN] image Landsat-8
[Termes IGN] image Pléiades-HR
[Termes IGN] orthoimage
[Termes IGN] réflectance de surface
[Termes IGN] séquence d'images
[Termes IGN] série temporelleRésumé : (auteur) Detecting clouds and their shadows is one of the primaries steps to perform when processing satellite images because they may alter the quality of some products such as large-area orthomosaics. The main goal of this paper is to present the automatic method developed at IGN-France for detecting clouds and shadows in a sequence of satellite images. In our work, surface reflectance orthoimages are used. They were processed from initial satellite images using a dedicated software. The cloud detection step consists of a region-growing algorithm. Seeds are firstly extracted. For that purpose and for each input ortho-image to process, we select the other ortho-images of the sequence that intersect it. The pixels of the input ortho-image are secondly labelled seeds if the difference of reflectance (in the blue channel) with overlapping ortho-images is bigger than a given threshold. Clouds are eventually delineated using a region-growing method based on a radiometric and homogeneity criterion. Regarding the shadow detection, our method is based on the idea that a shadow pixel is darker when comparing to the other images of the time series. The detection is basically composed of three steps. Firstly, we compute a synthetic ortho-image covering the whole study area. Its pixels have a value corresponding to the median value of all input reflectance ortho-images intersecting at that pixel location. Secondly, for each input ortho-image, a pixel is labelled shadows if the difference of reflectance (in the NIR channel) with the synthetic ortho-image is below a given threshold. Eventually, an optional region-growing step may be used to refine the results. Note that pixels labelled clouds during the cloud detection are not used for computing the median value in the first step; additionally, the NIR input data channel is used to perform the shadow detection, because it appeared to better discriminate shadow pixels. The method was tested on times series of Landsat 8 and Pléiades-HR images and our first experiments show the feasibility to automate the detection of shadows and clouds in satellite image sequences. Numéro de notice : C2016-038 Affiliation des auteurs : IGN (2012-2019) Thématique : IMAGERIE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.5194/isprs-archives-XLI-B3-475-2016 Date de publication en ligne : 09/06/2016 En ligne : http://dx.doi.org/10.5194/isprs-archives-XLI-B3-475-2016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91849 PermalinkPermalinkForest stand segmentation using airborne lidar data and very high resolution multispectral imagery / Clément Dechesne (2016)PermalinkPermalinkStudy of lever-arm effect using embedded photogrammetry and on-board GPS receiver on UAV for metrological mapping purpose and proposal of a free ground measurements calibration procedure / Mehdi Daakir (2016)PermalinkThe iQmulus urban showcase: automatic tree classification and identification in huge mobile mapping point clouds / Jan Böhm (2016)PermalinkPermalinkPermalinkPermalinkPermalink
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