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Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection / Anandakumar M. Ramiya in Geocarto international, vol 31 n° 1 - 2 (January - February 2016)
[article]
Titre : Object-oriented semantic labelling of spectral–spatial LiDAR point cloud for urban land cover classification and buildings detection Type de document : Article/Communication Auteurs : Anandakumar M. Ramiya, Auteur ; Rama Rao Nidamanuri, Auteur ; R. Krishnan, Auteur Année de publication : 2016 Article en page(s) : pp 121 - 139 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] classificateur
[Termes IGN] détection de partie cachée
[Termes IGN] détection du bâti
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image multibande
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] semis de pointsRésumé : (Auteur) The urban land cover mapping and automated extraction of building boundaries is a crucial step in generating three-dimensional city models. This study proposes an object-based point cloud labelling technique to semantically label light detection and ranging (LiDAR) data captured over an urban scene. Spectral data from multispectral images are also used to complement the geometrical information from LiDAR data. Initial object primitives are created using a modified colour-based region growing technique. Multiple classifier system is then applied on the features extracted from the segments for classification and also for reducing the subjectivity involved in the selection of classifier and improving the precision of the results. The proposed methodology produces two outputs: (i) urban land cover classes and (ii) buildings masks which are further reconstructed and vectorized into three-dimensional buildings footprints. Experiments carried out on three airborne LiDAR datasets show that the proposed technique successfully discriminates urban land covers and detect urban buildings. Numéro de notice : A2016-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1034195 Date de publication en ligne : 06/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1034195 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80001
in Geocarto international > vol 31 n° 1 - 2 (January - February 2016) . - pp 121 - 139[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2016011 RAB Revue Centre de documentation En réserve L003 Disponible Photogrammetric computer vision / Wolfgang Förstner (2016)
Titre : Photogrammetric computer vision : statistics, geometry, orientation and reconstruction Type de document : Guide/Manuel Auteurs : Wolfgang Förstner, Auteur ; Bernhard P. Wrobel, Auteur Editeur : Springer Nature Année de publication : 2016 Collection : Geometry and computing, ISSN 1866-6795 num. 11 Importance : 816 p. Format : 21 x 28 cm ISBN/ISSN/EAN : 978-3-319-11549-8 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] aérotriangulation numérique
[Termes IGN] compensation par faisceaux
[Termes IGN] couple stéréoscopique
[Termes IGN] données maillées
[Termes IGN] données vectorielles
[Termes IGN] estimation statistique
[Termes IGN] géométrie
[Termes IGN] géométrie projective
[Termes IGN] image 2D
[Termes IGN] image 3D
[Termes IGN] incertitude géométrique
[Termes IGN] ligne (géométrie)
[Termes IGN] modèle de Gauss-Markov
[Termes IGN] modèle géométrique de prise de vue
[Termes IGN] plan (géométrie)
[Termes IGN] point
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction d'objet
[Termes IGN] rotation d'objet
[Termes IGN] semis de points
[Termes IGN] transformation géométrique
[Termes IGN] variable aléatoire
[Termes IGN] vision par ordinateur
[Termes IGN] visualisation 3DIndex. décimale : 33.30 Photogrammétrie numérique Résumé : (Editeur) This textbook offers a statistical view on the geometry of multiple view analysis, required for camera calibration and orientation and for geometric scene reconstruction based on geometric image features. The authors have backgrounds in geodesy and also long experience with development and research in computer vision, and this is the first book to present a joint approach from the converging fields of photogrammetry and computer vision. Part I of the book provides an introduction to estimation theory, covering aspects such as Bayesian estimation, variance components, and sequential estimation, with a focus on the statistically sound diagnostics of estimation results essential in vision metrology. Part II provides tools for 2D and 3D geometric reasoning using projective geometry. This includes oriented projective geometry and tools for statistically optimal estimation and test of geometric entities and transformations and their relations, tools that are useful also in the context of uncertain reasoning in point clouds. Part III is devoted to modelling the geometry of single and multiple cameras, addressing calibration and orientation, including statistical evaluation and reconstruction of corresponding scene features and surfaces based on geometric image features. The authors provide algorithms for various geometric computation problems in vision metrology, together with mathematical justifications and statistical analysis, thus enabling thorough evaluations. The chapters are self-contained with numerous figures and exercises, and they are supported by an appendix that explains the basic mathematical notation and a detailed index. The book can serve as the basis for undergraduate and graduate courses in photogrammetry, computer vision, and computer graphics. It is also appropriate for researchers, engineers, and software developers in the photogrammetry and GIS industries, particularly those engaged with statistically based geometric computer vision methods. Note de contenu : 1. Introduction
1.1. Tasks for Photogrammetric Computer Vision
1.2. Modelling in Photogrammetric Computer Vision
1.3. The Book
1.4. On Notation
Part One - Statistics and Estimation
2. Probability Theory and Random Variables
2.1. Notions of Probability
2.2. Axiomatic Definition of Probability
2.3. Random Variables
2.4. Distributions
2.5. Moments
2.6. Quantiles of a Distribution
2.7. Functions of Random Variables
2.8. Stochastic Processes
2.9. Generating Random Numbers
2.10. Exercises
3. Testing
3.1. Principles of Hypothesis Testing
3.2. Testability of an Alternative Hypothesis
3.3. Common Tests
3.4. Exercises
4. Estimation
4.1. Estimation Theory
4.2. The Linear Gauss–Markov Model
4.3. Gauss–Markov Model with Constraints
4.4. The Nonlinear Gauss–Markov Model
4.5. Datum or Gauge Definitions and Transformations
4.6. Evaluation
4.7. Robust Estimation and Outlier Detection
4.8. Estimation with Implicit Functional Models
4.9. Methods for Closed Form Estimations
4.10. Estimation in Autoregressive Models
4.11. Exercises
Part two - Geometry
5. Homogeneous Representations of Points, Lines and Planes
5.1. Homogeneous Vectors and Matrices
5.2. Homogeneous Representations of Points and Lines in 2D
5.3. Homogeneous Representations in IPn
5.4. Homogeneous Representations of 3D Lines
5.5. On Plücker Coordinates for Points, Lines and Planes
5.6. The Principle of Duality
5.7. Conics and Quadrics
5.8. Normalizations of Homogeneous Vectors
5.9. Canonical Elements of Coordinate Systems
5.10. Exercises
6. Transformations
6.1. Structure of Projective Collineations
6.2. Basic Transformations
6.3. Concatenation and Inversion of Transformations
6.4. Invariants of Projective Mappings
6.5. Perspective Collineations
6.6. Projective Correlations
6.7. Hierarchy of Projective Transformations and Their Characteristics
6.8. Normalizations of Transformations
6.9. Conditioning
6.10. Exercises
7. Geometric Operations
7.1. Geometric Operations in 2D Space
7.2. Geometric Operations in 3D Space
7.3. Vector and Matrix Representations for Geometric Entities
7.4. Minimal Solutions for Conics and Transformations
7.5. Exercises
8. Rotations
8.1. Rotations in 3D
8.2. Concatenation of Rotations
8.3. Relations Between the Representations for Rotations
8.4. Rotations from Corresponding Vector Pairs
8.5. Exercises
9. Oriented Projective Geometry
9.1. Oriented Entities and Constructions
9.2. Transformation of Oriented Entities
9.3. Exercises
10. Reasoning with Uncertain Geometric Entities
10.1. Motivation
10.2. Representing Uncertain Geometric Elements
10.3. Propagation of the Uncertainty of Homogeneous Entities
10.4. Evaluating Statistically Uncertain Relations
10.5. Closed Form Solutions for Estimating Geometric Entities
10.6. Iterative Solutions for Maximum Likelihood Estimation
10.7. Exercises
Part Three - Orientation and Reconstruction
11. Overview
11.1. Scene, Camera, and Image Models
11.2. The Setup of Orientation, Calibration, and Reconstruction
11.3. Exercises
12. Geometry and Orientation of the Single Image
12.1. Geometry of the Single Image
12.2. Orientation of the Single Image
12.3. Inverse Perspective and 3D Information from a Single Image
12.4. Exercises
13. Geometry and Orientation of the Image Pair
13.1. Motivation
13.2 The Geometry of the Image Pair
13.3 Relative Orientation of the Image Pair
13.4. Triangulation
13.5. Absolute Orientation and Spatial Similarity Transformation
13.6. Orientation of the Image Pair and Its Quality
13.7. Exercises
14. Geometry and Orientation of the Image Triplet
14.1. Geometry of the Image Triplet
14.2. Relative Orientation of the Image Triplet
14.3. Exercises
15. Bundle Adjustment
15.1. Motivation for Bundle Adjustment and Its Tasks
15.2. Block Adjustment
15.3. Sparsity of Matrices, Free Adjustment and Theoretical Precision
15.4. Self-calibrating Bundle Adjustment
15.5. Camera Calibration
15.6. Outlier Detection and Approximate Values
15.7. View Planning
15.8. Exercises
16. Surface Reconstruction
16.1. Introduction
16.2. Parametric 21/2D Surfaces
16.3. Models for Reconstructing One-Dimensional Surface Profiles
16.4. Reconstruction of 21/2D Surfaces from 3D Point Clouds
16.5. Examples for Surface Reconstruction
16.6. Exercises
Appendix: Basics and Useful Relations from Linear AlgebraNuméro de notice : 22610 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Manuel Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82915 Réservation
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Code-barres Cote Support Localisation Section Disponibilité 22610-01 33.30 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible 22610-02 33.30 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Sorti jusqu'au 28/03/2024 22610-03 DEP-ELZ Livre Marne-la-Vallée Dépôt en unité Exclu du prêt Surface-based matching of 3D point clouds with variable coordinates in source and target system / Xuming Ge in ISPRS Journal of photogrammetry and remote sensing, vol 111 (January 2016)
[article]
Titre : Surface-based matching of 3D point clouds with variable coordinates in source and target system Type de document : Article/Communication Auteurs : Xuming Ge, Auteur ; Thomas Wunderlich, Auteur Année de publication : 2016 Article en page(s) : pp 1 – 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] compensation par moindres carrés
[Termes IGN] image multicapteur
[Termes IGN] modèle de Gauss-Helmert
[Termes IGN] modèle mathématique
[Termes IGN] modèle numérique de surface
[Termes IGN] reconstruction 3D
[Termes IGN] semis de pointsRésumé : (auteur) The automatic co-registration of point clouds, representing three-dimensional (3D) surfaces, is an important technique in 3D reconstruction and is widely applied in many different disciplines. An alternative approach is proposed here that estimates the transformation parameters of one or more 3D search surfaces with respect to a 3D template surface. The approach uses the nonlinear Gauss–Helmert model, minimizing the quadratically constrained least squares problem. This approach has the ability to match arbitrarily oriented 3D surfaces captured from a number of different sensors, on different time-scales and at different resolutions. In addition to the 3D surface-matching paths, the mathematical model allows the precision of the point clouds to be assessed after adjustment. The error behavior of surfaces can also be investigated based on the proposed approach. Some practical examples are presented and the results are compared with the iterative closest point and the linear least-squares approaches to demonstrate the performance and benefits of the proposed technique. Numéro de notice : A2016-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2015.11.001 En ligne : https://doi.org/10.1016/j.isprsjprs.2015.11.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79514
in ISPRS Journal of photogrammetry and remote sensing > vol 111 (January 2016) . - pp 1 – 12[article]
Titre : Towards operational very high resolution land-cover mapping Titre original : Outils pour des occupations du sol opérationnelles à très haute résolution spatiale Type de document : Thèse/HDR Auteurs : Clément Mallet , Auteur Editeur : Champs/Marne : Université Paris-Est Année de publication : 2016 Importance : 144 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 « Sciences et Techniques de l’Information Géographique »Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] base de données d'occupation du sol
[Termes IGN] carte d'occupation du sol
[Termes IGN] classification dirigée
[Termes IGN] détection de changement
[Termes IGN] données localisées 2D
[Termes IGN] données localisées 3D
[Termes IGN] image à très haute résolution
[Termes IGN] image satellite
[Termes IGN] information sémantique
[Termes IGN] reconstruction 3D
[Termes IGN] segmentation d'image
[Termes IGN] sémantisation
[Termes IGN] semis de pointsIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) [Preamble] This document is the manuscript presented in order to obtain the Habilitation à Diriger des Recherches of Paris-Est University, France. My main professional activities, since my PhD defence in 2010, in the MATIS team of the French National Institute of Geographic and Forest Information (IGN), are described here. It may also include recall to previous works of the 2007-2010 period. I was recruited in the research department of IGN in 2005, being a civil servant. I first started as engineer on 3D airborne lidar point cloud processing in urban, natural, and seashore environments during the 2005-2007 period. Then, I focused on the emerging fullwaveform lidar technology during my PhD thesis, with a slight emphasis on urban areas (2007-2010). Since 2010, I took the lead of the research group that deals with land-cover mapping and change detection as well as 2D/3D geodatabase evaluation. Now, it encompasses more generally semantization and reconstruction issues for scene understanding. I naturally moved from airborne lidar data to any kind of geospatial/overhead remote sensing data (mainly satellite and airborne vertical imagery). Some of the developed methods has been eventually specialized to 3D terrestrial point clouds. [...] Note de contenu : PART ONE - SCIENTIFIC SYNTHESIS
1. INTRODUCTION
1.1 Relevance of remote sensing for land-cover mapping
1.2 A fastly evolving context for research in remote sensing
1.3 What is operational land-cover mapping?
1.4 Datasets
2. RESEARCH RESULTS 2011-2016
2.1 Remote sensing data processing
2.2 Feature extraction and selection
2.3 Object extraction/segmentation
2.4 Land-cover classification
2.5 Fusion
3. PERSPECTIVES
3.1 Efficient classifications
3.2 Semantic segmentation
3.3 Humans in the loop
3.4 Optimal data fusion
3.5 Exploitation of existing geospatial databases
3.6 Towards land-use mapping
3.7 Change detection and LC updating
3.8 Land-cover dynamics monitoring
PART TWO - CURRICULUM VITAE
4. EMPLOYEMENT AND EDUCATION
4.1 Civil status
4.2 Employement
4.3 Education
4.4 Awards
5. STUDENT SUPERVISION AND TEACHING
5.1 Supervision
6. CONTENTS
5.2 Teaching
6 Projects and collaborations
6.1 Funded projects
6.2 National and international cooperations
7. SCIENTIFIC ANIMATION
7.1 PhD committee membership
7.2 Editorial work and reviews
7.3 Conference and workshop organisation
7.4 Program committee membership
7.5 Scientific societies
8. PUBLICATION LIST
8.1 Book chapters
8.2 Journal papers
8.3 Peer-reviewed conference papers
8.4 MiscellaneousNuméro de notice : 22673 Affiliation des auteurs : LASTIG MATIS (2012-2019) Thématique : FORET/IMAGERIE Nature : HDR Note de thèse : HDR : Sciences et Technologies de l’Information Géographique : UPE : 2016 Organisme de stage : MATIS (IGN) nature-HAL : HDR DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=84127 Documents numériques
en open access
22673_Towards operational very high resolution land-cover mapping.pdfAdobe Acrobat PDF Application of technical measures and software in constructing photorealistic 3D models of historical building using ground-based and aerial (UAV) digital images / Aleksander Zarnowski in Reports on geodesy and geoinformatics, vol 99 (December 2015)
[article]
Titre : Application of technical measures and software in constructing photorealistic 3D models of historical building using ground-based and aerial (UAV) digital images Type de document : Article/Communication Auteurs : Aleksander Zarnowski, Auteur ; Anna Banaszek, Auteur ; Sebastian Banaszek, Auteur Année de publication : 2015 Article en page(s) : pp 54 - 63 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] chambre non métrique
[Termes IGN] drone
[Termes IGN] image aérienne
[Termes IGN] intégration de données
[Termes IGN] modélisation 3D
[Termes IGN] monument historique
[Termes IGN] patrimoine immobilier
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] rendu réalisteRésumé : (auteur) Preparing digital documentation of historical buildings is a form of protecting cultural heritage. Recently there have been several intensive studies using non-metric digital images to construct realistic 3D models of historical buildings. Increasingly often, non-metric digital images are obtained with unmanned aerial vehicles (UAV). Technologies and methods of UAV flights are quite different from traditional photogrammetric approaches. The lack of technical guidelines for using drones inhibits the process of implementing new methods of data acquisition.
This paper presents the results of experiments in the use of digital images in the construction of photo-realistic 3D model of a historical building (Raphaelsohns’ Sawmill in Olsztyn). The aim of the study at the first stage was to determine the meteorological and technical conditions for the acquisition of aerial and ground-based photographs. At the next stage, the technology of 3D modelling was developed using only ground-based or only aerial non-metric digital images. At the last stage of the study, an experiment was conducted to assess the possibility of 3D modelling with the comprehensive use of aerial (UAV) and ground-based digital photographs in terms of their labour intensity and precision of development. Data integration and automatic photo-realistic 3D construction of the models was done with Pix4Dmapper and Agisoft PhotoScan software Analyses have shown that when certain parameters established in an experiment are kept, the process of developing the stock-taking documentation for a historical building moves from the standards of analogue to digital technology with considerably reduced cost.Numéro de notice : A2015--024 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.2478/rgg-2015-0012 En ligne : https://doi.org/10.2478/rgg-2015-0012 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80848
in Reports on geodesy and geoinformatics > vol 99 (December 2015) . - pp 54 - 63[article]Automatically enhancing CityGML LOD2 models with a corresponding indoor geometry / Roeland Boeters in International journal of geographical information science IJGIS, vol 29 n° 12 (December 2015)PermalinkCreation of parametric BIM objects from point clouds using nurbs / Luigi Barazzetti in Photogrammetric record, vol 30 n° 152 (December 2015 - February 2016)PermalinkLe relevé 3D du patrimoine culturel : la Ca' Vendramin dei Leoni, musée Guggenheim de Venise / Caterina Balletti in XYZ, n° 145 (décembre 2015 - février 2016)PermalinkConstruction of 3D volumetric objects for a 3D cadastral system / Shen Ying in Transactions in GIS, vol 19 n° 5 (October 2015)PermalinkStochastic geometrical model and Monte Carlo optimization methods for building reconstruction from InSAR data / Yue Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)PermalinkTracking bats movements / Vivian Raiborde in GIM international [en ligne], vol 29 n° 10 (October 2015)PermalinkStreetgen: in-base procedural-based road generation / Rémi Cura in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol II-3 W5 (October 2015)PermalinkAutomatic registration of optical aerial imagery to a LiDAR point cloud for generation of city models / Bernard O. Abayowa in ISPRS Journal of photogrammetry and remote sensing, vol 106 (August 2015)PermalinkCalibration of a camera–projector monochromatic system / Cristina Portales in Photogrammetric record, vol 30 n° 149 (March - May 2015)PermalinkDéveloppement d'un logiciel de calcul de trajectoire pour un drone / Valerio Baiocchi in Géomatique expert, n° 103 (mars - avril 2015)Permalink