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Contributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques / Saadallah El Asmar (2016)
Titre : Contributions à la segmentation non supervisée d'images hyperspectrales : trois approches algébriques et géométriques Type de document : Thèse/HDR Auteurs : Saadallah El Asmar, Auteur ; Michel Berthier, Directeur de thèse ; Carl Frélicot, Directeur de thèse Editeur : La Rochelle : Université de La Rochelle Année de publication : 2016 Importance : 96 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse pour l'obtention du grade de docteur de l'Université de La Rochelle, Mathématiques et ApplicationsLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse de groupement
[Termes IGN] analyse spectrale
[Termes IGN] appariement
[Termes IGN] classification non dirigée
[Termes IGN] classification par nuées dynamiques
[Termes IGN] classification pixellaire
[Termes IGN] codage
[Termes IGN] géométrie de Riemann
[Termes IGN] image hyperspectrale
[Termes IGN] segmentation d'image
[Termes IGN] similitude
[Termes IGN] télédétectionIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Depuis environ une dizaine d’années, les images hyperspectrales produites par les systèmes de télédétection, “Remote Sensing”, ont permis d’obtenir des informations très fiables quant aux caractéristiques spectrales de matériaux présents dans une scène donnée. Nous nous intéressons dans ce travail au problème de la segmentation non supervisée d’images hyperspectrales suivant trois approches bien distinctes. La première, de type Graph Embedding, nécessite deux étapes : une première étape d’appariement des pixels de patchs de l’image initiale grâce à une mesure de similarité spectrale entre pixels et une seconde étape d’appariement d’objets issus des segmentations locales grâce à une mesure de similarité entre objets. La deuxième, de type Spectral Hashing ou Semantic Hashing, repose sur un codage binaire des variations des profils spectraux. On procède à des segmentations par clustering à l’aide d’un algorithme de k-modes adapté au caractère binaire des données à traiter et à l’aide d’une version généralisée de la distance classique de Hamming. La troisième utilise les informations riemanniennes des variétés issues des différentes façons de représenter géométriquement une image hyperspectrale. Les segmentations se font une nouvelle fois par clustering à l’aide d’un algorithme de k-means. Nous exploitons pour cela les propriétés géométriques de l’espace des matrices symétriques définies positives, induites par la métrique de Fisher Rao. Note de contenu : 1- Introduction
2- Segmentation par similarité
3- Segmentation par codage binaire
4- Segmentation riemanienne
5- ConclusionNuméro de notice : 25821 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse française Note de thèse : Thèse de Doctorat : Mathématiques et Applications : Université de La Rochelle : 2016 Organisme de stage : Laboratoire Mathématiques, Image et Applications nature-HAL : Thèse DOI : sans En ligne : https://tel.archives-ouvertes.fr/tel-01661468/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95094 Contient
- Study of lever-arm effect using embedded photogrammetry and on-board GPS receiver on UAV for metrological mapping purpose and proposal of a free ground measurements calibration procedure / Mehdi Daakir (2016)
- Exterior orientation of hyperspectral frame images collected with UAV for forest applications / Adilson Berveglieri (2015)
Fusion of hyperspectral images and digital surface models for urban object extraction / Janja Avbelj (2016)
Titre : Fusion of hyperspectral images and digital surface models for urban object extraction Type de document : Thèse/HDR Auteurs : Janja Avbelj, Auteur ; Xiaoxiang Zhu, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2016 Collection : DGK - C, ISSN 0065-5325 num. 771 Importance : 143 p. ISBN/ISSN/EAN : 978-3-7696-5183-6 Note générale : bibliographie
PhD DissertationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] classification bayesienne
[Termes IGN] détection d'objet
[Termes IGN] extraction automatique
[Termes IGN] fusion de données
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de surface
[Termes IGN] optimisation (mathématiques)
[Termes IGN] polygone
[Termes IGN] rectangle englobant minimumRésumé : (auteur) Buildings are prominent objects of the constantly changing urban environment. Accurate and up to date Building Polygons (BP) are needed for a variety of applications, e.g. 3D city visualisation, micro climate forecast, and real estate databases. The increasing number of earth observation remote sensing images enables the development of methods for building extraction. For instance, Hyperspectral Images (HSI) are a source of information about the material of the objects in the scene, whereas the Digital Surface Models (DSM) carry information about height of the surface and of objects. Thus, complementary information from multi-modal images, such as HSI and DSM, is needed to provide better understanding of the observed objects. A variation in material and height is represented by an edge in HSI and DSM, respectively. Edges in an image carry large portions of information about the geometry of the objects, because they delineate the boundaries between them. Object extraction and delineation is more reliable if information content from HSI, DSM, and edge information is jointly accounted for. The focus in this thesis is on method development for BP extraction using complementary information from HSI and DSM by accounting for edge information. Furthermore, a new quality measure, which accounts for shape differences and geometric accuracy between extracted and reference polygons, is proposed. Object and edge detection from an image is meaningful only for some range of scales. Edge detection in scale space is motivated by showing that in the same image different edges appear at different scales. Instead of deterministic edge detection, edge probabilities are computed in a linear scale space. Bayesian fusion of edge probabilities is proposed, which employs a Gaussian mixture model. The scale, at which an edge probability is computed, is defined by a confidence probability. The impact of selecting mixing coefficients in the Gaussian mixture model according to a prior knowledge or by a fully automatic data-driven approach is investigated. Main limitations of joining the edge probabilities from different datasets are the coregistration between the datasets and the inaccuracies in the datasets. The rectilinear BP are adjusted by means of weighted least squares, where the weights are defined on the basis of joint edge probabilities. Two mathematical models for rectilinear BP are proposed, one with a strict rectilinearity constraint and the second one, which introduces a relaxed rectilinearity constraint through weighting. The experiments on synthetic images show that the model with strict constraint gives better results, if the BP under consideration are all rectilinear. Otherwise, the relaxed rectilinearity constraint through weighting balances better between the rectilinearity assumption and fitness to the data. The approximate BP are created by a Minimum Bounding Rectangle (MBR) method. A main contribution of the proposed iterative MBR method is the automatic selection of a level of complexity of MBR through analysis of a cost function. A metric for comparison of polygons and line segments, named PoLiS metric, is defined. It compares polygons with different number of vertices, is insensitive to the number of vertices on polygon's edges, is monotonic, and has a nearly linear response to small changes in translation, rotation, and scale. Its characteristics are discussed and compared to the commonly used measures for BP evaluation. In all experiments the BP are evaluated by computing the newly proposed PoLiS metric and quality rate. The feasibility of joining all the proposed methods in one workflow is shown through the experiment, which is carried out on 17 HSI-DSM dataset pairs with four different ground sampling distances. The main finding of the experiment is that joining the information from multi-modal images, i.e. HSI and DSM, results in better quality of the adjusted BP. For instance, even for datasets with 4 m ground sampling distance, the completeness, correctness and quality rate values of extracted BP are better than 0.83, 0.68, and 0.60. Inaccuracies of the images, such as holes in DSM or imperfect DSM for 1151 orthorectification, are influencing the accuracy and localisation of edge probabilities and consequently also the accuracy of adjusted BP. Note de contenu : bibliographie Numéro de notice : 19792 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Dissertation : Photogrammetry : Stuttgart : 2016 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85016 Documents numériques
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Fusion of Hyperspectral Images and Digital Surface Models for Urban Object ExtractionAdobe Acrobat PDF Multifractal analysis for multivariate data with application to remote sensing / Sébastien Combrexelle (2016)
Titre : Multifractal analysis for multivariate data with application to remote sensing Type de document : Thèse/HDR Auteurs : Sébastien Combrexelle, Auteur ; Jean-Yves Tourneret, Directeur de thèse ; Steve Mclaughlin, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2016 Importance : 211 p. Format : 21 x 30 cm Note générale : bibliographie
Thèse en vue de l'obtention du Doctorat de l'Université de Toulouse, Spécialité Signal, Image, Acoustique et OptimisationLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse fractale
[Termes IGN] analyse multivariée
[Termes IGN] approche hiérarchique
[Termes IGN] estimation bayesienne
[Termes IGN] image hyperspectrale
[Termes IGN] modèle statistique
[Termes IGN] télédétection
[Termes IGN] texture d'image
[Termes IGN] transformation en ondelettesRésumé : (auteur) Texture characterization is a central element in many image processing applications. Texture analysis
can be embedded in the mathematical framework of multifractal analysis, enabling the study of the fluctuations in regularity of image intensity and providing practical tools for their assessment, the wavelet coefficients or wavelet leaders. Although successfully applied in various contexts, multifractal analysis suffers at present from two major limitations. First, the accurate estimation of multifractal parameters for image texture remains a challenge, notably for small image sizes. Second, multifractal analysis has so far been limited to the analysis of a single image, while the data available in applications are increasingly multivariate. The main goal of this thesis is to develop practical contributions to overcome these limitations. The first limitation is tackled by introducing a generic statistical model for the logarithm of wavelet leaders, parametrized by multifractal parameters of interest. This statistical model enables us to counterbalance the variability induced by small sample sizes and to
embed the estimation in a Bayesian framework. This yields robust and accurate estimation procedures, effective both for small and large images. The multifractal analysis of multivariate images is then addressed by generalizing this Bayesian framework to hierarchical models able to account for the assumption that multifractal properties evolve smoothly in the dataset. This is achieved via the design of suitable priors relating the dynamical properties of the multifractal parameters of the different components composing the dataset. Different priors are investigated and compared in this thesis by means of numerical simulations conducted on synthetic multivariate multifractal images. This work is further completed by the investigation of the potential benefits of multifractal analysis and the proposed Bayesian methodology for remote sensing via the example of hyperspectral imaging.Note de contenu : Introduction
1- Multifractal analysis
2- Statistical model and univariate Bayesian estimation
3- Bayesian multifractal analysis of
multivariate imagesNuméro de notice : 25811 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Spécialité : Signal, Image, Acoustique et Optimisation : Toulouse : 2016 Organisme de stage : Institut de Recherche en Informatique de Toulouse (I.R.I.T.) En ligne : http://www.theses.fr/2016INPT0078 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95074 A multilinear mixing model for nonlinear spectral unmixing / Rob Heylen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)
[article]
Titre : A multilinear mixing model for nonlinear spectral unmixing Type de document : Article/Communication Auteurs : Rob Heylen, Auteur ; Paul Scheunders, Auteur Année de publication : 2016 Article en page(s) : pp 240 - 251 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] image hyperspectrale
[Termes IGN] modèle de mélange multilinéaire
[Termes IGN] modèle linéaireRésumé : (Auteur) In hyperspectral unmixing, bilinear and linear-quadratic models have become popular recently, and also the polynomial postnonlinear model shows promising results. These models do not consider endmember interactions involving more than two endmembers, although such interactions might compose a nontrivial part of the observed spectrum in scenarios involving bright materials and complex geometrical structures, such as vegetation and intimate mixtures. In this paper, we present an extension of these models to include an infinite number of interactions. Several technical problems, such as divergence of the resulting series, can be avoided by introducing an optical interaction probability, which becomes the only free parameter of the model in addition to the abundances. We present an unmixing strategy based on this multilinear mixing (MLM) model; present comparisons with the bilinear models and the Hapke model for intimate mixing; and show that, in several scenarios, the MLM model obtains superior results. Numéro de notice : A2016-072 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2453915 En ligne : https://doi.org/10.1109/TGRS.2015.2453915 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79837
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Code-barres Cote Support Localisation Section Disponibilité 065-2016011 SL Revue Centre de documentation Revues en salle Disponible PermalinkSpectral–spatial adaptive sparse representation for hyperspectral image denoising / Ting Lu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkTotal-variation-regularized low-rank matrix factorization for hyperspectral image restoration / Wei He in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)PermalinkPermalinkClassification of hyperspectral images by exploiting spectral–spatial information of superpixel via multiple kernels / Leyuan Fang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 12 (December 2015)PermalinkSemi-supervised SVM for individual tree crown species classification / Michele Dalponte in ISPRS Journal of photogrammetry and remote sensing, vol 110 (December 2015)PermalinkUrban classification by the fusion of thermal infrared hyperspectral and visible data / Jiayi Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 81 n° 12 (December 2015)PermalinkCombining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment / Tal Rapaport in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)PermalinkEfficient superpixel-level multitask joint sparse representation for hyperspectral image classification / Jiayi Li in IEEE Transactions on geoscience and remote sensing, vol 53 n° 10 (October 2015)PermalinkFusion of waveform LiDAR data and hyperspectral imagery for land cover classification / Hongzhou Wang in ISPRS Journal of photogrammetry and remote sensing, vol 108 (October 2015)Permalink