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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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Fusion of Multi-Temporal... - pdf auteur -Adobe Acrobat PDF Handbook on advances in remote sensing and geographic information systems / Margarita N. Favorskaya (2017)
Titre : Handbook on advances in remote sensing and geographic information systems : paradigms and applications in forest landscape modeling Type de document : Guide/Manuel Auteurs : Margarita N. Favorskaya, Auteur ; Lakhmi C. Jain, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2017 Collection : Intelligent Systems Reference Library num. 122 Importance : 415 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-3-319-52306-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] arbre (flore)
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] éclairage
[Termes IGN] écosystème forestier
[Termes IGN] fusion de données
[Termes IGN] image optique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] lasergrammétrie
[Termes IGN] logiciel
[Termes IGN] modèle numérique de surface de la canopée
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation 3D
[Termes IGN] ombre
[Termes IGN] réalité virtuelle
[Termes IGN] reconstruction d'objet
[Termes IGN] rendu (géovisualisation)
[Termes IGN] semis de points
[Termes IGN] texturageIndex. décimale : 35.41 Applications de télédétection - végétation Résumé : (Editeur) This book presents the latest advances in remote-sensing and geographic information systems and applications. It is divided into four parts, focusing on Airborne Light Detection and Ranging (LiDAR) and Optical Measurements of Forests; Individual Tree Modelling; Landscape Scene Modelling; and Forest Eco-system Modelling. Given the scope of its coverage, the book offers a valuable resource for students, researchers, practitioners, and educators interested in remote sensing and geographic information systems and applications. Note de contenu : 1 INNOVATIONS IN REMOTE SENSING OF FORESTS
1.1 Introduction
1.2 Chapters Including in the Book
1.3 Conclusions
Part 1 - Airborne LiDAR and Optical Measurements of Forest
2 OVERVIEW OF LIDAR TECHNOLOGIES AND EQUIPMENT FOR LAND COVER SCANNING
2.1 Introduction
2.2 Development of LiDAR Technology
2.3 Overview of Airborne Laser Scanning
2.4 Overview of UAV Laser Scanning
2.5 Overview of Terrestrial Laser Scanning
2.6 Comparison of Remote Sensing Techniques for Forest Inventory
2.7 Conclusions
3 SOFTWARE TOOLS FOR TERRAIN AND FOREST MODELLING
3.1 Introduction
3.2 Survey of Software Tools for Terrain Modelling
3.3 Survey of Software Tools for Vegetation Modelling
3.4 Conclusions
4 DATA FUSION FOR EVALUATION OF WOODLAND PARAMETERS
4.1 Introduction
4.2 Related Work
4.3 Generalized Flowchart for Data Fusion of Airborne Laser Scanning, Imaging Spectroscopy, and Imaging Photography
4.4 Representation of Airborne LiDAR and Digital Photography Data
4.5 Method for Crown and Trunk Measurements
Active Contour Models
4.6 Experimental Results
4.7 Conclusions
Part 2 - Individual Tree Modelling
5 TREE MODELLING IN VIRTUAL REALITY ENVIRONMENT
5.1 Introduction
5.2 Related Work
5.3 Fundamentals of L-Systems
5.4 Procedural Modelling of Broad-Leaved Trees and Shrubs
5.5 Procedural Modelling of Coniferous Trees
5.6 Modelling Results
5.7 Conclusions
6 REALISTIC TREE MODELLING
6.1 Introduction
6.2 Related Work
6.3 Voxel Modelling of Vegetation
6.4 Improvement of Tree Models by Realistic Data
6.5 Experimental Results
6.6 Conclusions
Part 3 - Landscape Scene Modelling
7 DIGITAL MODELLING OF TERRAIN SURFACE
7.1 Introduction
7.2 Related Work
7.3 Densification of LiDAR Point Cloud
7.4 Filtering of LiDAR Points
7.5 Generation of Digital Terrain Model
7.6 Experimental Results
7.7 Conclusions
8 TEXTURING OF LANDSCAPE SCENES
8.1 Introduction
8.2 Related Work
8.3 Fundamentals of Texture Mapping
8.4 Multi-resolution Texturing for Digital Earth Surface Model
8.5 Multi-resolution Texturing for Vegetation Models
8.6 Experimental Results
8.7 Conclusions
9 LARGE SCENE RENDERING
9.1 Introduction
9.2 Related Work
9.3 Large Landscape Scene Rendering
9.4 Large Terrain Rendering
9.5 Vegetation Rendering
9.6 Realistic Lighting
9.7 Shaders
9.8 Conclusions
10 SCENE RENDERING UNDER METEOROLOGICAL IMPACTS
10.1 Introduction
10.2 Related Work
10.3 Wind Rendering
10.4 Fog Simulation
10.5 Rain Simulation
10.6 Snow Covering Simulation
10.7 Natural Objects’ Rendering
10.8 Experimental Results
10.9 Conclusions
Part 4 - Forest Ecosystem Modelling
11 LIGHTING AND SHADOWS RENDERING IN NATURAL SCENES
11.1 Introduction
11.2 Related Work
11.3 Background of Lighting
11.4 Simulation of Lighting in Modelling Scene
11.5 Simulation of Lighting Changes in Modelling Scene
11.6 Implementation
11.7 Conclusions
12 MODELLING OF FOREST ECOSYSTEMS
12.1 Introduction
12.2 Related Work
12.3 Forest Scene Modelling
12.4 Forest Ecosystems
12.5 Modelling of Living Conditions
12.6 ConclusionsNuméro de notice : 22742 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85782 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22742-01 35.41 Livre Centre de documentation Télédétection Disponible 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 Hyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkPermalinkImproving FOSS photogrammetric workflows for processing large image datasets / Oscar Martinez-Rubi in Open Geospatial Data, Software and Standards, vol 2 (2017)PermalinkModeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)PermalinkPermalinkPré-segmentation pour la classification faiblement supervisée de scènes urbaines à partir de nuages de points 3D LIDAR / Stéphane Guinard (2017)PermalinkRandom-walker-based collaborative learning for hyperspectral image classification / Bin Sun in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)PermalinkSegmentation sémantique de données de télédétection multimodale : application aux peuplements forestiers / Clément Dechesne (2017)PermalinkSegmentation sémantique de peuplements forestiers par analyse conjointe d’imagerie multispectrale très haute résolution et de données 3D Lidar aéroportées / Clément Dechesne (2017)PermalinkSVM et réseaux neuronaux convolutifs pour la classification de scènes urbaines / Amaury Zarzelli (2017)PermalinkTélédétection pour l'observation des surfaces continentales, ch. 1. Application de l'optique aux milieux urbains / Xavier Briottet (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Ch. 2. Analyse de scènes urbaines avec un véhicule de cartographie mobile / Bruno Vallet (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 1. Observation des surfaces continentales par télédétection optique / Nicolas Baghdadi (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 4. Observation des surfaces continentales par télédétection 2 / Nicolas Baghdadi (2017)PermalinkTélédétection pour l'observation des surfaces continentales, Volume 5. Observation des surfaces continentales par télédétection 3 / Nicolas Baghdadi (2017)PermalinkA two-step decision fusion strategy: application to hyperspectral and multispectral images for urban classification / Walid Ouerghemmi (2017)PermalinkUrban objects classification by spectral library: Feasibility and applications / Walid Ouerghemmi (2017)PermalinkWeakly supervised segmentation-aided classification of urban scenes from 3D LIDAR point clouds / Stéphane Guinard (2017)PermalinkAn integrated framework for the spatio–temporal–spectral fusion of remote sensing images / Huanfeng Shen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkAntiques secrets et technologies futuristes / Marielle Mayo in Géomètre, n° 2142 (décembre 2016)PermalinkAutomatic parameter selection for intensity-based registration of imagery to LiDAR data / Ebadat Ghanbari Parmehr in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkClass-specific sparse multiple kernel learning for spectral–spatial hyperspectral image classification / Tianzhu Liu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkDictionary learning for promoting structured sparsity in hyperspectral compressive sensing / Lei Zhang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkHyperspectral feature extraction using total variation component analysis / Behnood Rasti in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkMultiband image fusion based on spectral unmixing / Qi Wei in IEEE Transactions on geoscience and remote sensing, vol 54 n° 12 (December 2016)PermalinkProgressive visualization of complex 3D models over the internet / Jing Chen in Transactions in GIS, vol 20 n° 6 (December 2016)PermalinkA robust background regression based score estimation algorithm for hyperspectral anomaly detection / Zhao Rui in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)PermalinkThe practical application of 3D vision in the field: Measuring reindeer (rangifer tarandus) antler growth velocities / Derek D. Lichti in Photogrammetric record, vol 31 n° 156 (December 2016 - February 2017)PermalinkBlind hyperspectral unmixing using total variation and ℓq sparse regularization / Jakob Sigurdsson in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkClose-range photogrammetric tools for epigraphic surveys / Mariam Samaan in Journal on Computing and Cultural Heritage, JOCCH, vol 9 n° 3 (November 2016)PermalinkMultiple kernel learning based on discriminative kernel clustering for hyperspectral band selection / Jie Feng in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkRobust multitask learning with three-dimensional empirical mode decomposition-based features for hyperspectral classification / Zhi He in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkSemi-supervised hyperspectral classification from a small number of training samples using a co-training approach / Michał Romaszewski in ISPRS Journal of photogrammetry and remote sensing, vol 121 (November 2016)PermalinkWave period and coastal bathymetry using wave propagation on optical images / Céline Danilo in IEEE Transactions on geoscience and remote sensing, vol 54 n° 11 (November 2016)PermalinkAn operational high-resolution forest inventory / Julianno Sambatti in GIM international, vol 30 n° 10 (October 2016)PermalinkA Computationally efficient algorithm for fusing multispectral and hyperspectral images / Raúl Guerra in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkDeep feature extraction and classification of hyperspectral images based on convolutional neural networks / Yushi Chen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkDevelopment of a large-format UAS imaging system with the construction of a one sensor geometry from a multicamera array / Jiann-Yeou Rau in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkEvaluating EO1-Hyperion capability for mapping conifer and broadleaved forests / Nicola Puletti in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkFast and accurate target detection based on multiscale saliency and active contour model for high-resolution SAR images / Song Tu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkHabitat change on Horn Island, Mississippi, 1940-2010, determined from textural features in panchromatic vertical aerial imagery / Guy W. Jeter Jr in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)PermalinkInfluence of tree species complexity on discrimination performance of vegetation indices / Azadeh Ghiyamat in European journal of remote sensing, vol 49 n° 1 (2016)PermalinkObject-based morphological profiles for classification of remote sensing imagery / Christian Geiss in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)PermalinkSemisupervised classification for hyperspectral image based on multi-decision labeling and deep feature learning / Xiaorui Ma in ISPRS Journal of photogrammetry and remote sensing, vol 120 (october 2016)Permalink