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Hyperspectral image classification with canonical correlation forests / Junshi Xia in IEEE Transactions on geoscience and remote sensing, vol 55 n° 1 (January 2017)
[article]
Titre : Hyperspectral image classification with canonical correlation forests Type de document : Article/Communication Auteurs : Junshi Xia, Auteur ; Naoto Yokoya, Auteur ; Akira Iwasaki, Auteur Année de publication : 2017 Article en page(s) : pp 421 - 431 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse canonique
[Termes IGN] analyse en composantes indépendantes
[Termes IGN] champ aléatoire de Markov
[Termes IGN] classificateur
[Termes IGN] classification
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] image hyperspectrale
[Termes IGN] Rotation Forest classificationRésumé : (Auteur) Multiple classifier systems or ensemble learning is an effective tool for providing accurate classification results of hyperspectral remote sensing images. Two well-known ensemble learning classifiers for hyperspectral data are random forest (RF) and rotation forest (RoF). In this paper, we proposed to use a novel decision tree (DT) ensemble method, namely, canonical correlation forest (CCF). More specifically, several individual canonical correlation trees (CCTs) that are binary DTs, which use canonical correlation components for the hyperplane splitting, are used to construct the CCF. Additionally, we adopt the projection bootstrap technique in CCF, in which the full spectral bands are retained for split selection in the projected space. The techniques aforementioned allow the CCF to improve the accuracy of member classifiers and diversity within the ensemble. Furthermore, the CCF is extended to the spectral-spatial frameworks that incorporate Markov random fields, extended multiattribute profiles (EMAPs), and the ensemble of independent component analysis and rolling guidance filter (E-ICA-RGF). Experimental results on six hyperspectral data sets are used to indicate the comparative effectiveness of the proposed method, in terms of accuracy and computational complexity, compared with RF and RoF, and it turns out that CCF is a promising approach for hyperspectral image classification not only with spectral information but also in the spectral-spatial frameworks. Numéro de notice : A2017-021 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2607755 En ligne : https://doi.org/10.1109/TGRS.2016.2607755 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83953
in IEEE Transactions on geoscience and remote sensing > vol 55 n° 1 (January 2017) . - pp 421 - 431[article]
Titre : Instantaneous estimation of attitude from GNSS Type de document : Thèse/HDR Auteurs : Hendy Fitrian Suhandri, Auteur ; Alfred Kleusberg, Directeur de thèse ; Hasanuddin Zainal Abidin, Directeur de thèse Editeur : Stuttgart : University of Stuttgart Année de publication : 2017 Importance : 143 p. Format : 21 x 30 cm Note générale : Bibliographie
thesis accepted by the Faculty of Aerospace Engineering and Geodesy of the University of Stuttgart in partial fulfilment of the requirements for the degree of Doctor of Engineering Sciences (Dr.-Ing.)Langues : Anglais (eng) Descripteur : [Termes IGN] ambiguïté entière
[Termes IGN] angle d'Euler
[Termes IGN] double différence
[Termes IGN] filtre de Kalman
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle stochastique
[Termes IGN] orientation de véhicule
[Termes IGN] positionnement cinématique
[Termes IGN] positionnement par GNSS
[Termes IGN] récepteur GNSS
[Termes IGN] simple différence
[Vedettes matières IGN] Traitement de données GNSSRésumé : (auteur) The use of the Global Navigation Satellite System (GNSS) is widely spread from position determination to attitude determination of a platform in space. This system offers time invariant estimation position. Another thing that can be an advantage is that the flexibility to operate the GNSS receiver variants, from the low-cost until the high-performance GNSS receivers. In terms of attitude determination application at least three receivers are required to determine three spatial axes, where the cost-effective GNSS attitude determination systems can be constructed with today’s receiver technology. At the moment, however, algorithms are lacking which are fast and efficient enough to estimate the position angles without delay. For this reason, the present work deals with the development of algorithms for the attitude determination in space of a platform under the help of the "GNSS" Global Positioning System (GPS). The investigation through this work is classified into three sequential parts: The first part is the estimation of the optimal configuration of baseline array as well as the estimation of the integer ambiguity of carrier phase differences. The estimated integer ambiguity is then used to estimate the high precision baseline coordinates. The second part is to estimate the attitude of the platform in space by means of quaternion using batch process, and the last part is to improve the algorithm using a recursive algorithm for the kinematic application purpose. The precise attitude determination about three spatial axes is possible if at least three GNSS receivers with fixed baselines are used in particular array configurations. Assuming that the basic lengths of the baselines are known a priori, the attitude angles can be calculated via the combination of carrier phase and pseudorange observations. Since the carrier of the GPS signal is propagated in short-wave form, the measured phase differences are ambiguous. The multiples of the GPS signal phases together with the baseline lengths are therefore estimated and improved in a first step with the aid of the a priori baseline lengths information. The multiple-baseline float solution estimation method is used. However, the approach does not provide optimal results. Therefore, an alternative algorithm for the float solution is presented, which estimates the float solution by using the socalled the gradient based iterative method of the least-squares. It shows that method is able to give convergent estimate parameter. It is also shown here that the proposed method outperforms the conventional iterative least-squares in terms of iteration number and computational time. For instantaneous applications, the Least-squares AMBiguity Decorrelation Adjustment (LAMBDA) method is not optimal for fixing the integer multiples of the carrier phase differences for several baseline lengths. In addition, this method requires a high computational effort as soon as a larger number of baseline lines enter into the calculation. An improvement in this work is utilising the partial LAMBDA method, which only uses a subset of the integer multiples to be determined. This algorithm improves the determination of integer multiples and precise calculation of the baseline lengths. The advantages of this algorithm are discussed, and it is empirically demonstrated that the ambiguities are better resolved. Furthermore, the estimation of the attitude angles with the aid of quaternions is theoretically improved and analysed. Two processing strategies are investigated: the least-squares method and the Kalman Filter (KF) method. For the static case, the least-squares is applied and tested. Simulations show that the developed gradient based iterative method of the least-squares provides better estimates than the conventional adjustment methods. It is also shown that the number of iterations required is less and the computational time is reduced. This algorithm is not useful for kinematic applications where a fast sequence of results is required. A modified Extended Kalman Filter (EKF)-Like algorithm is used for kinematic applications. Experiments show that with this algorithm more stable quaternions can be calculated with fewer outliers than when they are determined by the least-squares method. All newly developed algorithms are theoretically analysed and subjected to extensive simulations and experimental kinematic tests in the field. Note de contenu : Introduction
1 - General mathematical model of GNSS positioning
2 - Multi-baseline GNSS estimation method
3 - GNSS based attitude determination
4 - Recursive attitude determination
5 - Experimental result of static and kinematic tests
6 - Summary, conclusion and future work suggestionNuméro de notice : 21574 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Thèse étrangère Note de thèse : Doctor thesis : Engineering sciences : Stuttgart : 2017 DOI : 10.18419/opus-9239 En ligne : http://dx.doi.org/10.18419/opus-9239 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90576
Titre : Introduction to Artificial Intelligence Type de document : Monographie Auteurs : Wolfgang Ertel, Auteur Editeur : Springer International Publishing Année de publication : 2017 Importance : 365 p. ISBN/ISSN/EAN : 978-3-319-58487-4 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Intelligence artificielle
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification bayesienne
[Termes IGN] entropie maximale
[Termes IGN] exploration de données
[Termes IGN] PROLOG
[Termes IGN] raisonnement sémantique
[Termes IGN] réseau neuronal artificielRésumé : (éditeur) This concise and accessible textbook supports a foundation or module course on A.I., covering a broad selection of the subdisciplines within this field. The book presents concrete algorithms and applications in the areas of agents, logic, search, reasoning under uncertainty, machine learning, neural networks and reinforcement learning. Topics and features: presents an application-focused and hands-on approach to learning the subject; provides study exercises of varying degrees of difficulty at the end of each chapter, with solutions given at the end of the book; supports the text with highlighted examples, definitions, and theorems; includes chapters on predicate logic, PROLOG, heuristic search, probabilistic reasoning, machine learning and data mining, neural networks and reinforcement learning; contains an extensive bibliography for deeper reading on further topics; supplies additional teaching resources, including lecture slides and training data for learning algorithms, at an associated website. Note de contenu : 1- Introduction
2- Propositional Logic
3- First-order Predicate Logic
4- Limitations of Logic
5- Logic Programming with PROLOG
6- Search, Games and Problem Solving
7- Reasoning with Uncertainty
8- Machine Learning and Data Mining
9- Neural Networks
10- Reinforcement Learning
11- Solutions for the ExercisesNuméro de notice : 25753 Affiliation des auteurs : non IGN Thématique : INFORMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-58487-4 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94945 Modèles géographiques avec le langage Mathematica / André Dauphiné (2017)
Titre : Modèles géographiques avec le langage Mathematica Type de document : Guide/Manuel Auteurs : André Dauphiné, Auteur Editeur : Londres : ISTE Editions Année de publication : 2017 Collection : Systèmes d'information, web et société Importance : 331 p. Format : 15 x 23 cm ISBN/ISSN/EAN : 978-1-78405-236-2 Note générale : Bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse de données
[Termes IGN] automate cellulaire
[Termes IGN] géostatistique
[Termes IGN] langage de programmation
[Termes IGN] Mathematica
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] modèle stochastique
[Termes IGN] système dynamique
[Termes IGN] système multi-agentsIndex. décimale : 37.20 Analyse spatiale et ses outils Résumé : (Editeur) Les géographes construisent des modèles afin de comprendre et d’expliquer les relations sociétés-nature, les localisations d’objets, de personnes et d’activités, et les organisations territoriales. Ce livre dresse un panorama complet des types de modèles nécessaires à la mise au point de nouvelles connaissances géographiques : modèles stochastiques, de chroniques, d’analyses de données, de géostatistiques, de réseaux, de systèmes dynamiques, d’automates cellulaires et de systèmes multi-agents. Cet ouvrage didactique replace ces modèles dans leur contexte théorique. Il propose plus de 65 programmes écrits en langage Mathematica formalisant ces modèles. Des études de cas permettent de montrer leur pertinence. Le lecteur pourra appliquer immédiatement ces programmes à ses propres questionnements et données. Note de contenu : 1. Paradoxes théoriques de la géographie classique
2. Modèles statistiques et probabilistes des relations sociétés-nature
3. Modèles de systèmes dynamiques ordinaires
4. Théories des localisations géographiques
5. Modèles des localisations géographiques
6. Théoriser les structures et les dynamiques territoriales
7. Modèles de points et de champs
8. Modèles de réseaux
9. Modèles de l’espace géographique
10. Macro et micro-modèles de la morphogénieNuméro de notice : 22708 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Manuel de cours Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85164 Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 22708-01 37.20 Livre Centre de documentation Géomatique Disponible Modeling spatial and temporal variabilities in hyperspectral image unmixing / Pierre-Antoine Thouvenin (2017)
Titre : Modeling spatial and temporal variabilities in hyperspectral image unmixing Type de document : Thèse/HDR Auteurs : Pierre-Antoine Thouvenin, Auteur ; Nicolas Dobigeon, Directeur de thèse ; Jean-Yves Tourneret, Directeur de thèse Editeur : Toulouse : Université de Toulouse Année de publication : 2017 Importance : 191 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
[Termes IGN] amplitude
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] données multitemporelles
[Termes IGN] image hyperspectrale
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] optimisation (mathématiques)
[Termes IGN] processus stochastique
[Termes IGN] séparation aveugle de source
[Termes IGN] signature spectrale
[Termes IGN] variabilitéIndex. décimale : THESE Thèses et HDR Résumé : (auteur) Acquired in hundreds of contiguous spectral bands, hyperspectral (HS) images have received an increasing interest due to the significant spectral information they convey about the materials present in a given scene. However, the limited spatial resolution of hyperspectral sensors implies that the observations are mixtures of multiple signatures corresponding to distinct materials. Hyperspectral unmixing is aimed at identifying the reference spectral signatures composing the data – referred to as endmembers – and their relative proportion in each pixel according to a predefined mixture model. In this context, a given material is commonly assumed to be represented by a single spectral signature. This assumption shows a first limitation, since endmembers may vary locally within a single image, or from an image to another due to varying acquisition conditions, such as declivity and possibly complex interactions between the incident light and the observed materials. Unless properly accounted for, spectral variability can have a significant impact on the shape
and the amplitude of the acquired signatures, thus inducing possibly significant estimation errors during the unmixing process. A second limitation results from the significant size of HS data, which may preclude the use of batch estimation procedures commonly used in the literature, i.e., techniques exploiting all the available data at once. Such computational considerations notably become prominent to characterize endmember variability in multi-temporal HS (MTHS) images, i.e., sequences of HS images acquired over the same area at different time instants. The main objective of this thesis consists in introducing new models and unmixing procedures to account for spatial and temporal endmember variability. Endmember variability is addressed by considering an explicit variability model reminiscent of the total least squares problem, and later extended to account for time-varying signatures. The variability is first estimated using an unsupervised deterministic optimization procedure based on the Alternating Direction Method of Multipliers (ADMM). Given the sensitivity of this approach to abrupt spectral variations, a robust model formulated within a Bayesian framework is introduced. This formulation enables smooth spectral variations to be described in terms of spectral variability, and abrupt changes in terms of outliers. Finally, the computational restrictions induced by the size of the data is tackled by an online estimation algorithm. This work further investigates an asynchronous distributed estimation procedure to estimate the parameters of the proposed models.Note de contenu : Introduction
1- Hyperspectral unmixing with spectral variability using a perturbed linear mixing model
2- A Bayesian model accounting for endmember variability and abrupt spectral changes to unmix multitemporal hyperspectral images
3- Online unmixing of multitemporal hyperspectral images
4- A partially asynchronous distributed unmixing algorithm
Conclusions et perspectivesNuméro de notice : 25812 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Signal, Image, Acoustique et Optimisation : Toulouse : 2017 Organisme de stage : Institut de Recherche en Informatique de Toulouse (I.R.I.T.) nature-HAL : Thèse DOI : sans En ligne : http://www.theses.fr/2017INPT0068 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95075 New iterative learning strategy to improve classification systems by using outlier detection techniques / Charlotte Pelletier (2017)PermalinkOndelettes et processus stochastiques / Abdourrahmane M. Atto (2017)PermalinkPré-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)PermalinkPermalinkPermalinkTélédétection pour l'observation des surfaces continentales, Volume 3. Observation des surfaces continentales par télédétection 1 / Nicolas Baghdadi (2017)PermalinkUtilisation de données satellites dans le combat contre l'esclavage moderne / Florent Negrel-Teodori (2017)PermalinkUtilisation d’un modèle numérique de hauteur en stratification des données de l’Inventaire Forestier National / Sophie Georges (2017)PermalinkVision stéréoscopique temps-réel pour la navigation autonome d'un robot en environnement dynamique / Maxime Derome (2017)Permalink