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Vegetation height estimation precision with compact PolInSAR and homogeneous random volume over ground model / Aurélien Arnaubec in IEEE Transactions on geoscience and remote sensing, vol 52 n° 3 (March 2014)
[article]
Titre : Vegetation height estimation precision with compact PolInSAR and homogeneous random volume over ground model Type de document : Article/Communication Auteurs : Aurélien Arnaubec, Auteur ; Antoine Roueff, Auteur ; Pascale C. Dubois-Fernandez, Auteur ; Philippe Réfrégier, Auteur Année de publication : 2014 Article en page(s) : pp 1879 - 1891 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] bande P
[Termes IGN] données polarimétriques
[Termes IGN] estimation statistique
[Termes IGN] hauteur de la végétation
[Termes IGN] image radar moirée
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] polarimétrie radarRésumé : (Auteur) Analyzing the precision of vegetation height estimation with compact (i.e., single transmit instead of dual transmit) polarimetric interferometric synthetic aperture radar (PolInSAR) with the homogeneous random volume over ground model can help justify the use of this type of radar rather than using the full PolInSAR. However, since compact PolInSAR provides less information than full PolInSAR, a loss of precision in the vegetation height estimation is expected, which can depend on the single transmit polarization. The adaptation of the Cramer-Rao bound (CRB) derived for full PolInSAR in our earlier work to compact PolInSAR measurement provides a general methodology to characterize this loss of precision. Indeed, the CRB is a lower bound of the variance of unbiased estimators that does not depend on the choice of a particular estimation method. We illustrate this methodology for P-band measurements with three synthetic examples chosen for their variability of polarimetric responses. For these examples, it is shown that there can exist a large set of transmit polarizations for which the loss of precision described by the CRB is small (smaller than a factor 2) although there also exist transmit polarizations for which the loss can be large (about a factor 100). This loss of precision is compared with the large dependency of the precision to the vegetation height estimation that can be observed with the vegetation height (more than a factor 100 in the precision described by the CRB) when all the other parameters of the vegetation, ground, and radar system are constant. Numéro de notice : A2014-114 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2256362 En ligne : https://doi.org/10.1109/TGRS.2013.2256362 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=33019
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 3 (March 2014) . - pp 1879 - 1891[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014031 RAB Revue Centre de documentation En réserve L003 Disponible A fully constrained linear spectral unmixing algorithm based on distance geometry / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)
[article]
Titre : A fully constrained linear spectral unmixing algorithm based on distance geometry Type de document : Article/Communication Auteurs : Hanye Pu, Auteur ; Wei Xia, Auteur ; Bin Wang, Auteur ; Geng-Ming Jiang, Auteur Année de publication : 2014 Article en page(s) : pp 1157 - 176 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des mélanges spectraux
[Termes IGN] contrainte géométrique
[Termes IGN] distance euclidienne
[Termes IGN] estimation statistique
[Termes IGN] méthode de Monte-CarloRésumé : (Auteur) Under the linear spectral mixture model, hyperspectral unmixing can be considered as a convex geometry problem, in which the endmembers are located in the vertices of simplex enclosing the hyperspectral data set and the barycentric coordinates of observation pixels with respect to the simplex correspond to the abundances of endmembers. Based on distance geometry theory, in this paper we propose a new approach for abundance estimation of mixed pixels in hyperspectral images. With the endmember signatures, which is known a priori or can be obtained from the endmember extraction algorithms, the proposed method automatically estimates the abundances of endmembers at each pixel using convex geometry concepts and distance geometry constraints. In the algorithm, denoting the pairwise distances with Cayley-Menger matrix makes it easy to calculate the barycentric coordinates of the observation pixels. Another characteristic of this algorithm is that the optimal estimated points of observation pixels as well as the least distortion in geometric structure of original data set can be obtained with the distance geometry constraint. Simultaneously, the use of barycenter of simplex builds an accurate and efficient method to estimate endmembers with zero abundance and, as a result, the subsimplex containing the estimated points is obtained. A comparative study and analysis based on Monte Carlo simulations and real data experiments is conducted among the proposed algorithm and three state-of-the-art algorithms: fully constrained least squares (FCLS), FCLS computed using constrained sparse unmixing by variable splitting and augmented Lagrangian, and simplex-projection unmixing (SPU). The experimental results show that the proposed algorithm always provides the best unmixing accuracy and when the number of endmembers is not very large the algorithm has a lower computational complexity. Numéro de notice : A2014-074 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2248013 En ligne : https://doi.org/10.1109/TGRS.2013.2248013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32979
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 1157 - 176[article]Exemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 065-2014021 RAB Revue Centre de documentation En réserve L003 Disponible
Titre : Bayesian essentials with R Type de document : Monographie Auteurs : Jean-Michel Marin, Auteur ; Christian P. Robert, Auteur Editeur : Berlin, Heidelberg, Vienne, New York, ... : Springer Année de publication : 2014 Importance : 296 p. Format : 16 x 24 cm ISBN/ISSN/EAN : 978-1-4614-8687-9 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] algorithme de Métropolis-Hastings
[Termes IGN] classification bayesienne
[Termes IGN] échantillonnage de Gibbs
[Termes IGN] méthode de Monte-Carlo par chaînes de Markov
[Termes IGN] modèle linéaire
[Termes IGN] problème de Dirichlet
[Termes IGN] R (langage)
[Termes IGN] régression linéaire
[Termes IGN] série temporelle
[Termes IGN] théorème de BayesRésumé : (éditeur) This Bayesian modeling book provides a self-contained entry to computational Bayesian statistics. Focusing on the most standard statistical models and backed up by real datasets and an all-inclusive R (CRAN) package called bayess, the book provides an operational methodology for conducting Bayesian inference, rather than focusing on its theoretical and philosophical justifications. Readers are empowered to participate in the real-life data analysis situations depicted here from the beginning. The stakes are high and the reader determines the outcome. Special attention is paid to the derivation of prior distributions in each case and specific reference solutions are given for each of the models. Similarly, computational details are worked out to lead the reader towards an effective programming of the methods given in the book. In particular, all R codes are discussed with enough detail to make them readily understandable and expandable. This works in conjunction with the bayess package. Bayesian Essentials with R can be used as a textbook at both undergraduate and graduate levels, as exemplified by courses given at Université Paris Dauphine (France), University of Canterbury (New Zealand), and University of British Columbia (Canada). It is particularly useful with students in professional degree programs and scientists to analyze data the Bayesian way. The text will also enhance introductory courses on Bayesian statistics. Prerequisites for the book are an undergraduate background in probability and statistics, if not in Bayesian statistics. A strength of the text is the noteworthy emphasis on the role of models in statistical analysis. Note de contenu : 1- User’s Manual
2- Normal Models
3- Regression and Variable Selection
4- Generalized Linear Models
5- Capture–Recapture Experiments
6- Mixture Models
7- Time Series
8- Image AnalysisNuméro de notice : 25759 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://link.springer.com/book/10.1007%2F978-1-4614-8687-9#toc Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94954
Titre : Data Analysis : Statistical and Computational Methods for Scientists and Engineers Type de document : Monographie Auteurs : Siegmund Brandt, Auteur Editeur : Springer International Publishing Année de publication : 2014 Importance : 532 p. ISBN/ISSN/EAN : 978-3-319-03762-2 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse de variance
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] méthode des moindres carrés
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] probabilités
[Termes IGN] régression
[Termes IGN] série temporelle
[Termes IGN] statistiques
[Termes IGN] variable aléatoireRésumé : (éditeur) The fourth edition of this successful textbook presents a comprehensive introduction to statistical and numerical methods for the evaluation of empirical and experimental data. Equal weight is given to statistical theory and practical problems. The concise mathematical treatment of the subject matter is illustrated by many examples, and for the present edition a library of Java programs has been developed. It comprises methods of numerical data analysis and graphical representation as well as many example programs and solutions to programming problems. The programs (source code, Java classes, and documentation) and extensive appendices to the main text are available for free download from the book’s page at www.springer.com.
Contents: Probabilities. Random variables. Random numbers and the Monte Carlo Method. Statistical distributions (binomial, Gauss, Poisson). Samples. Statistical tests. Maximum Likelihood. Least Squares. Regression. Minimization. Analysis of Variance. Time series analysis.
Audience: The book is conceived both as an introduction and as a work of reference. In particular it addresses itself to students, scientists and practitioners in science and engineering as a help in the analysis of their data in laboratory courses, working for bachelor or master degrees, in thesis work, and in research and professional work.Note de contenu : 1- Introduction
2- Probabilities
3- Random Variables: Distributions
4- Computer Generated Random Numbers: The Monte Carlo Method
5- Some Important Distributions and Theorems
6- Samples
7- The Method of Maximum Likelihood
8- Testing Statistical Hypotheses
9- The Method of Least Squares
10- Function Minimization
11- Analysis of Variance
12- Linear and Polynomial Regression
13- Time Series AnalysisNuméro de notice : 25778 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Monographie En ligne : https://doi.org/10.1007/978-3-319-03762-2 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94973 Method comparisons of forest attribute estimations based on different remote sensing sources, including Lidar. The Vosges case study / Nicolas Py (2014)
Titre : Method comparisons of forest attribute estimations based on different remote sensing sources, including Lidar. The Vosges case study Titre original : Comparaison de méthodes d'estimation de paramètres forestiers par télédétection, Lidar inclus. Cas d'étude sur les Vosges Type de document : Article/Communication Auteurs : Nicolas Py , Auteur ; Thierry Bélouard , Auteur ; Jean-Matthieu Monnet, Auteur ; Jérôme Bock, Auteur ; Alain Munoz, Auteur ; Marine Bouvier, Auteur ; Sylvie Durrieu, Auteur ; Jean-Pierre Renaud , Auteur Editeur : [s.l.] : [s.n.] Année de publication : 2014 Projets : FORESEE / Bigot-de-Morogues, Francis Conférence : FORESEE Workshop 2014, Forestry applications of remote sensing technologies 08/10/2014 10/10/2014 Champenoux France Langues : Anglais (eng) Descripteur : [Termes IGN] analyse comparative
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] estimation statistique
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] Vosges, massif des
[Vedettes matières IGN] Inventaire forestierRésumé : (auteur) Cette étude compare différentes méthodes pour l'estimation de paramètres forestiers à partir de données de télédétection, incluant le lidar, sur une vaste zone situées dans les Vosges haut-rhinoises. Numéro de notice : C2014-035 Affiliation des auteurs : IGN+Ext (2012-2019) Thématique : FORET Nature : Communication nature-HAL : ComSansActesPubliés-Unpublished DOI : sans En ligne : https://hal.archives-ouvertes.fr/hal-02600322 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98380 Single tree biomass modelling using airborne laser scanning / Ville Kankare in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)PermalinkMapping social activities and concepts with social media (Twitter) and web search engines (Yahoo and Bing): a case study in 2012 US Presidential Election / Ming-Hsiang Tsou in Cartography and Geographic Information Science, vol 40 n° 4 (September 2013)PermalinkEffects of national forest inventory plot location error on forest carbon stock estimation using k-nearest neighbor algorithm / Jaehoon Jung in ISPRS Journal of photogrammetry and remote sensing, vol 81 (July 2013)PermalinkMapping GPS positional errors using spatial linear mixed models / A.F. Militino in Journal of geodesy, vol 87 n° 7 (July 2013)PermalinkLe projet EMERGE pour des tarifs cohérents de volumes et biomasses des essences forestières françaises métropolitaines / Christine Deleuze in Rendez-vous techniques, n° 39-40 (Hiver-printemps 2013)PermalinkEstimation of glacier ice extinction using long-wavelength airborne Pol-InSAR / Jayanti J. Sharma in IEEE Transactions on geoscience and remote sensing, vol 51 n° 6 Tome 2 (June 2013)PermalinkModeling ambiguity in census microdata allocations to improve demographic small area estimates / Stefan Leyk in Transactions in GIS, vol 17 n° 3 (June 2013)PermalinkWhen tree rings behave like foam : moderate historical decrease in the mean ring density of common beech paralleling a strong historical growth increase / Jean-Daniel Bontemps in Annals of Forest Science, Vol 70 n° 4 (June 2013)PermalinkGrowth-competition-based stem diameter and volume modeling for tree-level forest inventory using airborne LiDAR data / Chien-Shun Lo in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 2 (April 2013)PermalinkStem volume and above-ground biomass estimation of individual pine trees from LiDAR data: contribution of full-waveform signals / Tristan Allouis in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 6 n° 2 part 3 (April 2013)Permalink