Descripteur
Termes IGN > géomatique > données localisées > données localisées numériques > données laser
données laserSynonyme(s)levé par laserVoir aussi |
Documents disponibles dans cette catégorie (1317)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
3D segmentation of forest structure using an adaptive mean shift based procedure / António Ferraz (2010)
contenu dans Proceedings Silvilaser 2010, The 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems, September 14th - 17th, 2010 Freiburg, Germany / Barbara Koch (2010)
Titre : 3D segmentation of forest structure using an adaptive mean shift based procedure Type de document : Article/Communication Auteurs : António Ferraz , Auteur ; Frédéric Bretar, Auteur ; Stéphane Jacquemoud, Auteur ; Gil Rito-Gonçalves , Auteur ; Luisa Pereira, Auteur Editeur : Fribourg [Allemagne] : University of Freiburg Année de publication : 2010 Conférence : SilviLaser 2010, 10th International Conference on LiDAR Applications for Assessing Forest Ecosystems 14/09/2010 17/09/2010 Fribourg Allemagne Importance : pp 281 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de décalage moyen
[Termes IGN] barycentre
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] Eucalyptus globulus
[Termes IGN] Pinus pinaster
[Termes IGN] Portugal
[Termes IGN] semis de points
[Termes IGN] strate végétaleRésumé : (auteur) Plant communities display a vertical structure based on the size and growth pattern of the dominant species. To a large extent, this pattern, called vertical stratification, depends on the climatic zone. Vertical structure analysis consists in detecting the number of layers and their limits within a forest stand. So far, there is a lack of robust approaches applied to airborne laser scanning (ALS) data that properly segment the different strata of forests having complex structures. In this study, we propose a procedure to characterize vertical forest stratification based on the mean shift (MS) algorithm. The MS is a non-linear filter that searches for local density maxima (modes). It is a non-parametric and unsupervised approach, which only requires a single criterion, the kernel bandwidth. Since the forest point cloud is a multi-modal distribution, the MS is used to find the modes which are supposed to be the barycenters of vegetation features. Once achieved, the modes are grouped together according to height range and the corresponding ALS points are assigned to each vegetation strata. Due to their complex pattern, using a single scale over the whole space is not recommended for the analysis of such environments. On this basis, the modes are computed using a variable kernel bandwidth according to the forest pattern. To depict such a pattern, we propose a new technique that segments the main forest layers at the plot level: overstory, understory, and surface vegetation. The procedure has been carried out on 45 plots of a Portuguese forest mainly composed of eucalyptus (Eucalyptus globulus) and pine (Pinus pinaster) trees that can be strongly populated by understory and surface vegetation. Numéro de notice : C2010-020 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : FORET/IMAGERIE Nature : Communication DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83729 Documents numériques
peut être téléchargé
3D segmentation of forest structure ... - pdf éditeurAdobe Acrobat PDF
Titre : 3D segmentation of forest structure using a mean-shift based algorithm Type de document : Article/Communication Auteurs : António Ferraz , Auteur ; Frédéric Bretar, Auteur ; Stéphane Jacquemoud, Auteur ; Gil Gonçalves, Auteur ; Luisa Pereira, Auteur Editeur : New York : Institute of Electrical and Electronics Engineers IEEE Année de publication : 2010 Conférence : ICIP 2010, 17th IEEE International Conference on Image Processing 25/09/2010 29/09/2010 Hong Kong Allemagne Proceedings IEEE Importance : pp 1413 - 1416 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme de décalage moyen
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] segmentation
[Termes IGN] strate végétale
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Consistent and accurate information on 3D forest canopy structure is required by many applications like forest inventory, management, logging, fuel mapping, habitat studies or biomass estimate. Compared to other remote sensing techniques (e.g., SAR or photogrammetry), airborne laser scanning is an adapted tool to provide such information by generating a three-dimensional georeferenced point cloud. Vertical structure analysis consists in detecting the number of layers within a forest stand and their limits. Until now, there is no approach that properly segments the different strata of a forest. In this study, we directly work on the 3D point cloud and we propose a mean shift (MS) based procedure for vertical forest segmentation. The approach that is carried out on complex forest plots improves the discrimination of vegetation strata. Numéro de notice : C2010-060 Affiliation des auteurs : MATIS+Ext (1993-2011) Thématique : FORET/IMAGERIE/INFORMATIQUE Nature : Communication nature-HAL : ComAvecCL&ActesPubliésIntl DOI : 10.1109/ICIP.2010.5651310 En ligne : https://doi.org/10.1109/ICIP.2010.5651310 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101942
Titre : Acquisition of 3D topography : automated 3D road and building reconstruction using airborne laser scanner data and topographic maps Type de document : Thèse/HDR Auteurs : Sander J. Oude Elberink, Auteur Editeur : Delft : Netherlands Geodetic Commission NGC Année de publication : 2010 Collection : Netherlands Geodetic Commission Publications on Geodesy, ISSN 0165-1706 num. 74 Importance : 172 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-90-6132-318-1 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bati
[Termes IGN] carte topographique
[Termes IGN] données laser
[Termes IGN] données localisées 3D
[Termes IGN] lasergrammétrie
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] reconstruction 3D
[Termes IGN] reconstruction 3D du bâti
[Termes IGN] réseau routier
[Termes IGN] télémétrie laser aéroportéIndex. décimale : 33.80 Lasergrammétrie Résumé : (Auteur) Introduction and research goal : Our research covers the automation in acquiring three dimensional (3D) topographic objects. The research tasks focus on two specific objects: roads and buildings. These objects are of high importance in 3D city models as they are two major topographic classes in the urban environment. Our activities are located between: -1. how topographic objects exist in reality; -2. how they are captured in the data, and -3. how they appear in a modelled/virtual world. To accomplish an automated approach, existing 2D topographic maps are upgraded to 3D using airborne laser scanner data. 3D topography also includes multiple heights or even multiple objects on top of each other at a certain location. The essence in the research activities on roads differs basically from those on buildings. For roads the focus is on reconstructing the edges' height of the objects, whereas for buildings the challenge is to reconstruct the 3D polyhedral roof shape inside the building edges.
3D Road reconstruction : When examining 3D road objects, we can expect that multiple road objects cross at a certain location. An automated method for 3D modelling of complex highway interchanges is presented. Laser data and 2D topographic map data are combined in an innovative 3D reconstruction procedure. Complex situations demand for knowledge to guide the automatic reconstruction. This knowledge is used in the fusion procedure to constrain the topological and geometrical properties of the reconstructed 3D model. Laser data has been segmented and filtered before it is fused with map data. In the surface-growing algorithm combining map and laser points, the laser data is assigned to the corresponding road element. Elevations of map points are determined by least squares plane fitting through a selection of neighbouring laser points. Although results are shown using two specific data sources, the algorithm is designed to be capable of dealing with any polygon-based topographic map and any aerial laser scanner data set. Quality analysis is essential for developing a reliable reconstruction process and for a proper use of 3D data. The quality of 3D reconstructed roads strongly depends on accuracy and type of input data and the reconstruction processing steps. We predict the precision of reconstructed map elevations by propagating errors in the input data through the processing steps. Besides this quality prediction, we test the reconstructed model against independent reference data. Differences between these two datasets are explained by the predicted uncertainty in the model. Map point heights can be reconstructed with an average precision of 10 to 15 cm, depending on the laser point configuration.
3D. Building reconstruction : The building reconstruction task contains three main goals: -1. to select laser points belonging to building roofs, -2. to detect the roof structure of that building, and -3. to reconstruct the outlines of the roof. We present a building reconstruction approach, which is based on a target graph matching algorithm as intermediate step to relate laser data with building models. Establishing this relation is important for adding building knowledge to the data. Our targets are topological representations of the most common roof structures which are stored in a database. Laser data is segmented into planar patches. The segments that are selected in the segment-in-polygon algorithm are considered initial roof segments. Topological relations between segments, in terms of intersection lines and height jumps, are represented in a building roof graph. These relations are labelled according to their geometry and that of the segments (e.g. same/opposite normal direction, convex/concave, tilted/horizontal). This graph is matched with the graphs from the target database. Matching results describe which target objects appear topologically in the data. Our target based graph matching algorithm supports the first two goals. The matching algorithm performs a filtering task: data features that topologically correspond with common roof structures are considered to be part of the roof structure of that building. These data features will be transferred to our automated building reconstruction, where the outlines of the roof faces have to be reconstructed. Segments and intersection lines that do not fit to an existing target roof topology will be removed from the further automated reconstruction approach. The reconstruction algorithm covers the third main goal of our building reconstruction task. For the geometric reconstruction, we present two approaches that vary in the amount of information they take from the data. The first, more data driven approach starts with laser data features that have been matched with target models. In general, the matched intersection lines represent the interior of the roof structure, so the task is to find an appropriate solution for the remaining roof edges, e.g. eaves and gutters. Map data is used for selection of roof segments and is taken as location for walls. Therefore we need to split up map polygons in order to build walls that distinguish various height levels, e.g. at step edge locations. The second, more model driven approach reconstructs parameterised building models. This approach relies more on geometric assumptions, such as roof symmetry, but the models can be refined if the data deviates significantly from the model. The target information includes the details on how these deviations are determined and on the thresholds to decide what is significant or not. We present results of 3D reconstructed models, including several quality checks. These quality measures describe the completeness of the match results plus the correctness of assumptions to the roof outline. About 20% of the buildings are affected by segments that did not completely match with the target graphs. In a few of these cases, this is correct because the segment is not representing a roof face. However, in about 40% of these cases, a neighbouring segment that would complete a target match is missing. Adapting processing parameters, such as minimum segment size, may improve the result but it may also disturb other topological relations. Setting the parameters is therefore an important task for the operator. Specially, parameters that define the segmentation algorithm are crucial as the segment is the key data feature in our building reconstruction algorithm. In order to improve our matching algorithm, the likelihood of relations between segments could be included in the attribute list of edges in the roof topology graph. At the moment only information on the geometric appearance of the intersection line is given as attribute value to the corresponding graph edge. Future work includes defining likelihood functions for graph edges and analysing the effect of likelihood attributes.Note de contenu : Part 1: Introduction to acquisition of 3D topography
1 Introduction
1.1 3D Topography
1.2 Scope and limitations
1.3 Input data
1.4 Research problems
1.5 Goal and objectives
1.6 Importance
1.7 Thesis outline
2 Use of 3D topography
2.1 Introduction
2.2 User requirements
2.2.1 Municipality of Den Bosch
2.2.2 Survey Department of Rijkswaterstaat
2.2.3 Water board "Hoogheemraadschap de Stichtsche Rijnlanden"
2.2.4 Topographic Service of the Dutch Cadastre
2.3 Re-using 3D models
2.3.1 Municipality of Den Bosch
2.3.2 Survey Department of Rijkswaterstaat
2.3.3 Water board "Hoogheemraadschap de Stichtsche Rijnlanden"
2.3.4 Topographic Service of the Dutch Cadastre
2.3.5 Availability and distribution
2.3.6 Data fusion
2.3.7 Generalization and filtering
2.3.8 3D Represents as-is situation
2.4 Role of use cases in research project
2.5 Recent developments in using 3D topography
2.6 Conclusions
Part 2: 3D Roads
3 3D Reconstruction of roads
3.1 Introduction
3.2 Related work
3.2.1 Road reconstruction from aerial images
3.2.2 2D Road mapping from laser data
3.2.3 3D Reconstruction from laser data
3.3 Proposed approach
3.4 Data sources
3.4.1 Airborne laser scanner data
3.4.2 Pre-processing laser data
3.4.3 2D Topographic map data
3.4.4 Pre-processing 2D map
3.5 Fusion of map and laser data
3.5.1 Research problems on fusing map and laser data
3.5.2 Proposed fusion algorithm
3.6 3D Reconstruction of polygons
3.6.1 Polygon boundaries
3.6.2 Additional polygons
3.6.3 Assumptions on boundaries
3.6.4 Surfaces
3.7 Results
3.7.1 Interchange "Prins Clausplein"
3.7.2 Interchange "Waterberg"
3.8 Discussion
3.8.1 Parameter settings
3.8.2 Topological correctness
4 Quality analysis on 3D roads
4.1 Error propagation
4.1.1 Quality of plane at map point location
4.1.2 Quality of laser block
4.1.3 Quality of plane model
4.2 Reference data
4.2.1 Height differences between reference data and 3D model
4.3 Testing of predicted quality
4.4 Discussion
Part 3: 3D Buildings
5 Building shape detection
5.1 Introduction
5.1.1 Real buildings vs 3D model representation
5.1.2 Real buildings vs appearance in input data
5.1.3 Appearance in input data vs 3D model representation
5.2 Related work
5.2.1 2D Mapping of building outlines
5.2.2 3D Reconstruction of buildings
5.3 Research problems
5.3.1 Problems on roof shape detection
5.3.2 Problems on scene complexity
5.4 Proposed approach
5.5 Information from map data
5.6 Features from laser data
5.6.1 Segmentation of laser scanner data
5.6.2 Intersection lines
5.6.3 Step edges
5.6.4 Roof topology graph
5.7 Target graphs
5.8 Target based graph matching
5.9 Complete matching results
5.10 Incomplete matching results
6 3D Building Reconstruction
6.1 Introduction
6.2 Components of a roof boundary
6.3 Approach 1: Combine features from complete match results
6.4 Extension of horizontal intersection lines
6.5 Outer boundaries of roof faces
6.5.1 Flat roof faces
6.5.2 Eave construction
6.5.3 Gutter construction
6.6 Dormers and step edges
6.6.1 Simple dormers
6.6.2 Step edges
6.6.3 Step edges for map subdivision
6.7 Reconstruction of walls
6.8 Approach 2: reconstructed targets
6.8.1 Parameterised target models
6.8.2 Use of map data
6.8.3 Limitations
6.8.4 Potential use
6.9 Summary
7 Results and evaluation
7.1 Introduction
7.2 Results
7.2.1 Approach 1: Combined features
7.2.2 Approach 2: Reconstructed targets
7.3 Evaluation
7.3.1 Laser data features
7.3.2 Evaluation on target based matching
7.3.3 Reconstructed models
7.3.4 Problematic situations
7.3.5 Performance in time
7.4 Potential for nation wide 3D building database
7.5 Summary
Part 4: Conclusions and recommendations
8 Conclusions and recommendations
8.1 Conclusions
8.1.1 3D Topographic object reconstruction
8.1.2 3D Road reconstruction
8.1.3 3D Building reconstruction
8.2 RecommendationsNuméro de notice : 10833 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère DOI : sans En ligne : https://www.ncgeo.nl/index.php/en/publicatiesgb/publications-on-geodesy/item/258 [...] Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=62510 Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 10833-01 33.80 Livre Centre de documentation Photogrammétrie - Lasergrammétrie Disponible Alternative methodologies for the internal quality control of parallele LIDAR strips / A. Habib in IEEE Transactions on geoscience and remote sensing, vol 48 n° 1 Tome 1 (January 2010)
[article]
Titre : Alternative methodologies for the internal quality control of parallele LIDAR strips Type de document : Article/Communication Auteurs : A. Habib, Auteur ; A.P. Kesrting, Auteur ; K.I. Bang, Auteur ; D.C. Lee, Auteur Année de publication : 2010 Article en page(s) : pp 221 - 236 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] bruit (théorie du signal)
[Termes IGN] contrôle qualité
[Termes IGN] données lidar
[Termes IGN] erreur systématique
[Termes IGN] lasergrammétrie
[Termes IGN] procédé
[Termes IGN] recouvrement d'images
[Termes IGN] semis de pointsRésumé : (Auteur) Light Detection and Ranging (LiDAR) systems have been widely adopted for the acquisition of dense and accurate topographic data over extended areas. Although the utilization of this technology has increased in different applications, the development of standard methodologies for the quality control (QC) of LiDAR data has not followed the same trend. In other words, a lack in reliable, practical, cost-effective, and commonly acceptable QC procedures is evident. A frequently adopted procedure for QC is comparing the LiDAR data to ground control points. Aside from being expensive, this approach is not accurate enough for the verification of horizontal accuracy, unless specifically designed LiDAR targets are used. This paper is dedicated to providing accurate, economical, and convenient internal QC procedures for the evaluation of LiDAR data, which is captured from parallel flight lines. The underlying concept of the proposed methodologies is that, in the absence of systematic and random errors in system parameters and measurements, conjugate surface elements in overlapping strips should perfectly match each other. Consistent incompatibilities and the quality of fit between conjugate surface elements in overlapping strips can be used to detect systematic errors in the system parameters/measurements and to evaluate the noise level in the LiDAR point cloud, respectively. Experimental results from real data demonstrate that all the proposed methods, with one exception, produce compatible estimates of systematic discrepancies between the involved data sets, as well as good quantification of inherent noise. Copyright IEEE Numéro de notice : A2010-020 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2009.2026424 Date de publication en ligne : 15/09/2009 En ligne : https://doi.org/10.1109/TGRS.2009.2026424 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30216
in IEEE Transactions on geoscience and remote sensing > vol 48 n° 1 Tome 1 (January 2010) . - pp 221 - 236[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2010011A RAB Revue Centre de documentation En réserve L003 Disponible Analyse de données lidar à retour d'onde complète pour la classification en milieu urbain = Analysis of Full-Waveform lidar data for urban area mapping / Clément Mallet (2010)
Titre : Analyse de données lidar à retour d'onde complète pour la classification en milieu urbain = Analysis of Full-Waveform lidar data for urban area mapping Type de document : Thèse/HDR Auteurs : Clément Mallet , Auteur ; Christian Heipke, Directeur de thèse ; Michel Roux, Directeur de thèse Editeur : Paris : Institut Géographique National - IGN (1940-2007) Année de publication : 2010 Importance : 245 p. Format : 21 x 30 cm Note générale : Bibliographie
Thèse présentée pour obtenir le grade de docteur de Télécom ParisTech, spécialité Signal et ImagesLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] classification dirigée
[Termes IGN] classification par séparateurs à vaste marge
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] forme d'onde pleine
[Termes IGN] lidar à retour d'onde complète
[Termes IGN] lidar topographique
[Termes IGN] milieu urbain
[Termes IGN] modèle de Gauss-Markov
[Termes IGN] onde lidar
[Termes IGN] semis de points
[Termes IGN] traitement de donnéesIndex. décimale : THESE Thèses et HDR Résumé : (Auteur) Avec l'émergence récente des systèmes lidar aéroportés à retour d'onde complète, capables de fournir plus qu'une représentation topographique en trois dimensions, se pose la question, entre autres, de son utilité pour l'analyse du milieu urbain. Nous souhaitons en particulier comparer ses performances aux systèmes lidar multi-échos traditionnels. Les signaux lidar fournis sont en effet porteurs d'informations supplémentaires sur les objets atteints. L'objectif final visé dans cette thèse est une cartographie automatique 3D améliorée des zones d'occupation du sol, comme socle de grands nombres d'applications déjà existantes, en partant des données brutes enregistrées. L'approche proposée se compose de deux grandes phases. La première étape consiste à traiter les signaux enregistrés pour générer des nuages de points 3D de qualité maîtrisée, ainsi que pour extraire des informations sur la morphologie de ces derniers. Deux méthodes distinctes sont présentées. L'une cherche à améliorer la méthode standard consistant à supposer que tous les échos suivent un modèle gaussien. La deuxième permet d'explorer l'hypothèse d'un mélange de modèles, donc de caractériser chaque écho séparément, tout en proposant une formulation physique simple et flexible du problème. Dans un second temps, les nuages 3D ainsi générés sont classés en se servant d'attributs spatiaux mais également des attributs morphologiques extraits lors de l'étape précédente. Une approche supervisée utilisant les Séparateurs à Vaste Marge est adoptée pour séparer les zones de sol, de bâtiments, et de végétation. Elle est couplée à un processus de sélection des attributs les plus pertinents. En plus de l'obtention d'une classification de bonne qualité, cette étape met en évidence l'apport des données à retour d'onde complète dans un cadre de cartographie automatique des paysages urbains. Note de contenu : I INTRODUCTION
1 Introduction
1.1 The analysis of urban areas
1.2 Airborne laser scanning.
1.3 and the full-waveform technology
1.4 Specific context of the thesis
1.5 Objectives and limitations
1.6 Overall strategy
1.7 Structure of the document
2 Full-waveform topographic lidar
2.1 Principle and technology of laser scanning
2.1.1 Physical principles
2.1.2 Lidar measurement formulas
2.1.3 Limitations of multiple pulse systems
2.2 Full-waveform lidar data
2.2.1 What does a waveform represent ?
2.2.2 Recording full-waveform data
2.2.3 Advantages and limitations
2.3 Typology of full-waveform systems
2.3.1 Bathymetric systems
2.3.2 Experimental topographic systems
2.3.3 Commercial lidar systems
2.3.4 Technical specifications of the main existing systems
2.4 Processing the backscatter waveforms
2.4.1 Deconvolution
2.4.2 Advanced echo extraction methods
2.5 Waveform modelling and echo fitting
2.5.1 Modelling the waveforms with Gaussian mixtures
2.5.2 Fitting the waveforms
2.6 FW point clouds: quantitative analysis and processing
2.6.1 Pulse shape analysis
2.6.2 Calibration and correction of the intensity feature and the target cross-section
2.6.3 Parameter behaviour and subsequent classification
2.6.4 Filtering and surface reconstruction
2.6.5 Applications in forested areas
2.6.6 Applications in urban areas
2.7 Data sets
II PROCESSING LIDAR WAVEFORMS
3 A Generalized Gaussian model for decomposition and modelling
3.1 Introduction
3.2 Methodology
3.2.1 Presentation
3.2.2 Model selection
3.2.3 Peak detection and initial parameters estimation
3.2.4 Fitting the waveforms
3.2.5 Georeferencing process
3.3 Results and evaluation
3.3.1 Decomposition results
3.3.2 Quality analysis
3.4 Analysis of the extracted parameters
3.4.1 Full-waveform parameters
3.4.2 Calibration and correction
3.4.3 Point-based analysis
3.5 Conclusions
4 A marked point process approach for waveform modelling
4.1 Motivation
4.2 Library of modelling functions
4.3 Marked point processes
4.3.1 Point processes
4.3.2 Density of a point process
4.3.3 Marks and object library
4.4 Energy formulation
4.4.1 Data term
4.4.2 Regularization term
4.4.3 Parameter estimation
4.5 Optimization
4.5.1 RJMCMC sampler
4.5.2 Proposition kernels
4.5.3 Simulated annealing
4.6 Results
4.6.1 Simulated data
4.6.2 Medium and large footprint waveforms
4.6.3 Small footprint waveforms
4.7 Conclusions and perspectives
III CLASSIFICATION OF URBAN AREAS
5 Classification of 3D lidar point clouds
5.1 Introduction
5.1.1 Context
5.1.2 Related works
5.1.3 Strategy
5.2 Support Vector Machines and feature selection
5.2.1 Principle of SVMs
5.2.2 Kernel selection
5.3 Features of interest
5.3.1 Various kinds of features (and computational strategies)
5.3.2 Description
5.3.3 Optimal neighbourhood analysis
5.4 Classification strategies with feature selection
5.4.1 Classification strategies
5.4.2 Feature selection methods
5.5 Results
5.5.1 Some preliminary remarks
5.5.2 Feature selection
5.5.3 Classification accuracy
5.6 An alternative approach: the Random Forests
5.6.1 Motivation
5.6.2 Strategy
5.6.3 Results and discussions
5.6.4 Outlook
5.7 Conclusions and perspectives
5.7.1 Conclusions
5.7.2 Perspectives
IV CONCLUSIONS
6 Conclusions and perspectives
6.1 Conclusions
6.1.1 Summary
6.1.2 Contributions and limitations
6.2 Perspectives
6.3 Outlook on the real contributions and limitations of FW data
6.4 Final outlookNuméro de notice : 14430 Affiliation des auteurs : MATIS (1993-2011) Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de doctorat : Signal et Images : Télécom ParisTech : 2010 Organisme de stage : MATIS (IGN) & LTCI nature-HAL : Thèse DOI : sans En ligne : https://hal.science/pastel-00566992v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=45248 Assessment of terrain elevation derived from satellite laser altimetry over mountainous forest areas using airborne lidar data / Q. Chen in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 1 (January - February 2010)PermalinkCoupling polarimetric L-Band insar and airborne lidar to characterize the geomorphological deformations in the piton de la fournaise volcano / Essam Heggy (2010)PermalinkPermalinkPermalinkIn-flight quality assessment and data processing for airborne laser scanning / Philipp Schaer (2010)PermalinkPermalinkPermalinkModélisation de façades par analyse conjointe d'images terrestres et de données laser / Antoine Pinte (2010)PermalinkModelling vertical error in LiDAR-derived digital elevation models / F. Aguilar in ISPRS Journal of photogrammetry and remote sensing, vol 65 n° 1 (January - February 2010)PermalinkPhotogrammetric computer vision and image analysis, ISPRS Commission 3 symposium, Saint-Mandé, 1-3 septembre 2010, volume 1. Papers accepted on the basis of peer-reviewed full manuscripts / Nicolas Paparoditis (2010)Permalink