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Computationally efficient hyperspectral data learning based on the doubly stochastic dirichlet process / Xing Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)
[article]
Titre : Computationally efficient hyperspectral data learning based on the doubly stochastic dirichlet process Type de document : Article/Communication Auteurs : Xing Sun, Auteur ; Nelson H. C. Yung, Auteur ; Edmund Y. Lam, Auteur ; Hayden K.-H. So, Auteur Année de publication : 2017 Article en page(s) : pp 363 - 374 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification
[Termes IGN] image hyperspectrale
[Termes IGN] modèle stochastique
[Termes IGN] problème de DirichletRésumé : (Auteur) The Dirichlet process (DP) prior is effective in modeling HSIs (HSI) and identifying land-cover classes. However, modeling a continuously varying intensity of these land covers elegantly and consistently is still a challenge. We propose a doubly stochastic DP (DSDP) as an efficient model of the global topic measurement space, which imposes a weaker assumption compared with the discrete Markov assumption, resulting in a lower computational cost than other DP-prior-based models. We also present a mixture model of DSDP, which is termed the marked sigmoidal Gaussian process (SGP) DSDP mixture model. It can be thinned from a DP mixture without massive auxiliary covariates, and the marked function prior makes the number of land-cover classes consistent, whereas the SGP function prior models the HSI land-cover variation globally. The consistency of the number of land covers is maintained for various HSIs with large-scale geographical areas. Experiments show that the model is robust and consistent on HSI identification with weak or even no supervision. Numéro de notice : A2017-020 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2606575 En ligne : https://doi.org/10.1109/TGRS.2016.2606575 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83951
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 363 - 374[article]Contributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar / Nesrine Chehata (2017)
Titre : Contributions méthodologiques pour la caractérisation des milieux par imagerie optique et lidar : Habilitation à Diriger des Recherches Type de document : Thèse/HDR Auteurs : Nesrine Chehata , Auteur Editeur : Champs/Marne : Université Paris-Est Année de publication : 2017 Importance : 219 p. Format : 21 x 30 cm Note générale : Bibliographie
Synthèse de travaux présentée en vue d’obtenir l’Habilitation à Diriger des Recherches délivrée par l’Université Paris-Est, Spécialité InformatiqueLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] apprentissage automatique
[Termes IGN] attribut géomètrique
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification orientée objet
[Termes IGN] détection d'objet
[Termes IGN] données lidar
[Termes IGN] données localisées 2D
[Termes IGN] données localisées 3D
[Termes IGN] forêt
[Termes IGN] fusion de données multisource
[Termes IGN] image optique
[Termes IGN] littoral
[Termes IGN] milieu urbain
[Termes IGN] modèle numérique de terrain
[Termes IGN] segmentation
[Termes IGN] semis de points
[Termes IGN] surface cultivéeIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) [Avant-propos] Ce manuscrit présente mon parcours professionnel et une synthèse de mes travaux de recherche actuels et passés et ouvre ensuite les perspectives sur les développements futurs que j’envisage d’approfondir. Mes travaux de recherche portent sur des développements méthodologiques pour le traitement et l’analyse des images et des nuages de points 3D afin de répondre à des applications environnementales. Je m’intéresse tout particulièrement aux données de télédétection optique à Haute et Très Haute Résolution spatiale (HR, THR) et spectrale (hyperspectral, superspectral) et aux données actives type LiDAR pour la caractérisation des milieux par nuages de points 3D. Ces données seront présentées dans le chapitre 3. J’ai fait le choix de regrouper les méthodologies développées suivant trois grands axes de recherche qui correspondent à différentes étapes de la chaîne de traitement des données : 1) calcul et sélection d’attributs, 2) les techniques de segmentation, et 3) les techniques de classification pour la cartographie de l’occupation du Sol (ocs). Ces développements méthodologiques répondent à des besoins thématiques exprimés sur différents milieux : l’urbain, la forêt, le littoral et les milieux cultivés. Le choix des thématiques s’est fait en fonction des institutions dans lesquelles j’ai travaillé et des partenariats établis. Dans le chapitre 1, après la présentation de mon parcours, mon curriculum vitae sera détaillé suivant 4 axes ; les activités pédagogiques, scientifiques, le rayonnement scientifique et enfin les responsabilités scientifiques et administratives. Le chapitre 2 présente les besoins principaux en caractérisation des milieu, qui sont ensuite détaillés par type de milieu. Ces besoins vont justifier le choix des données de télédétection adaptées et vont définir un certain nombre de verrous scientifiques à lever. Les contributions méthodologiques seront présentées au chapitre 3 qui synthétise mes propres développements méthodologiques ainsi que les travaux de doctorants que j’ai co-encadrés. Les trois axes méthodologiques seront détaillés. Pour chaque axe, les verrous sont présentés. Les méthodologies sont d’abord présentées d’un point de vue théorique et ensuite leurs applications sont présentées suivant différents projets de recherche. Mes perspectives de recherches seront ensuite présentées dans le chapitre 4 illustrant les deux grands axes de recherche pour la cartographie de l’ocs à grande et large échelle sur lesquels je souhaite continuer à travailler : 1) l’apprentissage automatique avec intégration de données temporelles et 2) Fusion de données multi-sources. Note de contenu : 1. PARCOURS ET CURRICULUM VITAE
1.1. Parcours
1.2. Curriculum Vitae
2. BESOINS, DONNEES ET ENJEUX SCIENTIFIQUES
2.1. Besoins en cartographie de l'occupation du sol
2.2. Besoins en production de Modèles Numériques de Terrain (MNT) fins
2.3. Les données de télédétection utilisées
2.4. Besoins Vs. données
2.5. Les verrous scientifiques
3. BILAN DES ACTIVITES DE RECHERCHE
3.1. Méthodologies d'élaboration de produits cartographiques par télédétection
3.2. Calcul et sélection d'attributs
3.3. Segmentation
3.4. Classification - Apprentissage automatique
Conclusion
4. PERSPECTIVES DE RECHERCHE
4.1. Axe apprentissage automatique
4.2. Axe fusion de données multi-sources
4.3. Axe segmentation sémantique et méthodes d'évaluation appliquées
Conclusion
5. ANNEXES
5.1. Axe sélection d'attributs
5.2. Comparaison de données optiques et lidar
5.3. Axe apprentissage automatique
5.4. Axe segmentationNuméro de notice : 22685 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : HDR Note de thèse : HDR : Informatique : UPE : 2017 nature-HAL : HDR DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-01494206/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84445 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22685-01 THESE Livre Centre de documentation Thèses Disponible Fusing meter-resolution 4-D InSAR point clouds and optical images for semantic urban infrastructure monitoring / Yuanyuan Wang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)
[article]
Titre : Fusing meter-resolution 4-D InSAR point clouds and optical images for semantic urban infrastructure monitoring Type de document : Article/Communication Auteurs : Yuanyuan Wang, Auteur ; Xiao Xiang Zhu, Auteur ; Bernhard Zeisl, Auteur ; Marc Pollefeys, Auteur Année de publication : 2017 Article en page(s) : pp 14 - 26 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image mixte
[Termes IGN] données 4D
[Termes IGN] fusion d'images
[Termes IGN] géométrie de l'image
[Termes IGN] image à résolution métrique
[Termes IGN] image optique
[Termes IGN] image radar moirée
[Termes IGN] pont
[Termes IGN] semis de points
[Termes IGN] surveillance d'ouvrage
[Termes IGN] voie ferrée
[Termes IGN] zone urbaineRésumé : (Auteur) Using synthetic aperture radar (SAR) interferometry to monitor long-term millimeter-level deformation of urban infrastructures, such as individual buildings and bridges, is an emerging and important field in remote sensing. In the state-of-the-art methods, deformation parameters are retrieved and monitored on a pixel basis solely in the SAR image domain. However, the inevitable side-looking imaging geometry of SAR results in undesired occlusion and layover in urban area, rendering the current method less competent for a semantic-level monitoring of different urban infrastructures. This paper presents a framework of a semantic-level deformation monitoring by linking the precise deformation estimates of SAR interferometry and the semantic classification labels of optical images via a 3-D geometric fusion and semantic texturing. The proposed approach provides the first “SARptical” point cloud of an urban area, which is the SAR tomography point cloud textured with attributes from optical images. This opens a new perspective of InSAR deformation monitoring. Interesting examples on bridge and railway monitoring are demonstrated. Numéro de notice : A2017-018 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2554563 En ligne : https://doi.org/10.1109/TGRS.2016.2554563 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83949
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 14 - 26[article]Fusion of graph embedding and sparse representation for feature extraction and classification of hyperspectral imagery / Fulin Luo in Photogrammetric Engineering & Remote Sensing, PERS, vol 83 n° 1 (January 2017)
[article]
Titre : Fusion of graph embedding and sparse representation for feature extraction and classification of hyperspectral imagery Type de document : Article/Communication Auteurs : Fulin Luo, Auteur ; Hong Huang, Auteur ; Jiamin Liu, Auteur ; Zezhong Ma, Auteur Année de publication : 2017 Article en page(s) : pp 37 - 46 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse discriminante
[Termes IGN] classification
[Termes IGN] extraction
[Termes IGN] fusion de données multisource
[Termes IGN] graphe
[Termes IGN] image hyperspectraleRésumé : (Auteur) The graph embedding algorithms have been widely applied for feature extraction (FE) of hyperspectral imagery (HSI). These methods need to construct a similarity graph with k-nearest neighbors or ∈-radius ball. However, the neighborhood size is difficult to select in real-world applications. To solve the problem, we propose a new unsupervised FE method, called sparsity preserving analysis (SPA), based on sparse representation and graph embedding. The proposed algorithm utilizes sparse representation to obtain the sparse coefficients of data. Then, it constructs a new graph with the sparse coefficients that reveals the sparse properties of data. Finally, the structure of the graph is preserved in low-dimensional space to obtain a transformation matrix for FE. In addition, a new classification method, termed sparse neighborhood classifier (SNC), is designed using the sparse representation-based methodology. It uses the sparse coefficients of a new sample to obtain the similarity weights in each class. Then, the label information of the new sample is obtained by the weights. The classification accuracies of SPA with SNC reach to 86.9 percent and 80.6 percent on PaviaU and Urban HSI data sets, respectively. The results demonstrate that SPA with SNC can effectively extract low-dimensional features and improve the discriminating power for HSI classification. Numéro de notice : A2017-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.83.1.37 En ligne : https://doi.org/10.14358/PERS.83.1.37 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84090
in Photogrammetric Engineering & Remote Sensing, PERS > vol 83 n° 1 (January 2017) . - pp 37 - 46[article]Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas / Cyril Wendl (2017)
Titre : Fusion of multi-temporal Sentinel-2 image series and very-high spatial resolution images for detection of urban areas Type de document : Mémoire Auteurs : Cyril Wendl, Auteur ; Arnaud Le Bris , Encadrant Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2017 Importance : 67 p. Format : 21 x 30 cm Note générale : bibliographie
Rapport de stage, Ecole Polytechnique Fédérale de Lausanne EPFLLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] détection du bâti
[Termes IGN] estimation bayesienne
[Termes IGN] image à très haute résolution
[Termes IGN] image multibande
[Termes IGN] image Sentinel-MSI
[Termes IGN] image SPOT 6
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation
[Termes IGN] théorie de Dempster-ShaferIndex. décimale : MASTX Mémoires de masters divers Résumé : (auteur) Fusion of very high spatial resolution multispectral images with lower spatial resolution image time series having a higher number of bands can improve land use classification, combining geometric and semantic advantages of both sources. This study presents a workflow to extract the extent of urbanized ground using decision-level fusion and regularization of individual classifications on Sentinel-2 and SPOT-6 satellite images. First, both images are classified individually in five classes, using state-of-the-art supervised classification approaches and Convolutional Neural Networks. Decision-level fusion and regularization are used to combine the spatial and spectral advantages of both sources: First, both sources are merged in order to extract building labels with as high semantic and spatial precision as possible. Second, the building labels are used together with the Sentinel-2 classification as input for a binary classification of the artificialized area; the building labels from the regularization are dilated in order to connect the building objects and a binary classification is derived from the original Sentinel-2 classification before these two separate binary classifications are reintroduced in a fusion and regularization to find the artificialized area. Segmentation of the Sentinel-2 satellite image and majority voting of the object-level classification are also used to refine the contours of the artificialized area. Note de contenu : Introduction
1 - Methodology
2 - Artificialized area
3 - Results
ConclusionNuméro de notice : 21702 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Rapport de stage Organisme de stage : MATIS (IGN) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90951 Documents numériques
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White in Canadian journal of remote sensing, vol 42 n° 5 ([01/05/2016])PermalinkUnsupervised multitemporal spectral unmixing for detecting multiple changes in hyperspectral images / Sicong Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 5 (May 2016)PermalinkForest above ground biomass inversion by fusing GLAS with optical remote sensing data / Xiaohuan Xi in ISPRS International journal of geo-information, vol 5 n° 4 (April 2016)PermalinkComparative analysis on utilisation of linear spectral unmixing and band ratio methods for processing ASTER data to delineate bauxite over a part of Chotonagpur plateau, Jharkhand, India / Arindam Guha in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkComparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)PermalinkNoise simulation and correction in synthetic airborne TIR Data for mineral quantification / Christoph Hecker in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkA penalized spline-based attitude model for high-resolution satellite imagery / Hongbo Pan in IEEE Transactions on geoscience and remote sensing, vol 54 n° 3 (March 2016)PermalinkUniformity-based superpixel segmentation of hyperspectral images / Arun M. 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