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Evidence of climate effects on the height-diameter relationships of tree species / Mathieu Fortin in Annals of Forest Science, vol 76 n° 1 (March 2019)  

Public [article]  inAnnals of Forest Science > vol 76 n° 1 (March 2019) Titre : Evidence of climate effects on the height-diameter relationships of tree species Type de document : Article/Communication Auteurs : Mathieu Fortin, Auteur ; Rosalinde van Couwenberghe, Auteur ; Vincent Perez, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] allométrie [Termes IGN] changement climatique [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] diamètre des arbres [Termes IGN] France (végétation) [Termes IGN] gestion forestière [Termes IGN] hauteur des arbres [Termes IGN] modèle de mélange multilinéaire [Termes IGN] précipitation [Termes IGN] température [Termes IGN] variable régionalisée [Vedettes matières IGN] Végétation et changement climatique Résumé : (auteur) Key message : The mean temperature from March to September affects the height-diameter relationship of many tree species in France. For most of these species, the temperature effect is nonlinear, which makes the identification of an optimal temperature possible. Increases in mean temperature could impact the volume supply of commercial species by the end of the twenty-first century. Context : Height-diameter (HD) relationships are central in forestry since they are essential to estimate tree volume and biomass. Since the late 1960s, efforts have been made to generalize models of HD relationships through the inclusion of plot- and tree-level explanatory variables. In some recent studies, climate variables such as mean annual temperature and precipitation have been found to have a significant effect on HD allometry. However, in these studies, the effects were all considered to be linear or almost linear, which supposes that there is no optimal temperature and no optimal precipitation. Aims : In this study, we tested the hypothesis that an optimum effect of temperature and precipitation exists on tree heights. Methods : We fitted generalized models of HD relationships to 44 tree species distributed across France. To make sure that the climate variables would not hide some differences in terms of the local environment, the models included explanatory variables accounting for competition, tree social status and other plot-level factors such as slope inclination and the occurrence of harvesting in the last five years. Results : It turned out that the temperature effect was significant for 33 out of 44 species and an optimum was found in 26 cases. The precipitation effect was linear and was found to be significant for only seven species. Although the two climate variables did not contribute as much as the competition and the social status indices to the model fit, they were still important contributors. Under the representative concentration pathway (RCP) 2.6 and the assumptions of constant form factors and forest conditions in terms of competition and social statuses, it is expected that approximately two thirds of the species with climate-sensitive HD relationships will generally be shorter. This would induce a decrease in volume ranging from 1 to 5% for most of these species. Conclusion : Forest practitioners should be aware that the volume supply of some commercial species could decrease by the end of the twenty-first century. However, these losses could be partly compensated for by changes in the form factors and the species distributions. Numéro de notice : A2019-045 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-018-0784-9 Date de publication en ligne : 19/12/2018 En ligne : https://doi.org/10.1007/s13595-018-0784-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92051 [article]

Uncertainty in radar emitter classification and clustering / Guillaume Revillon (2019)  

Public Titre : Uncertainty in radar emitter classification and clustering Titre original : Gestion des incertitudes en identification des modes radar Type de document : Thèse/HDR Auteurs : Guillaume Revillon, Auteur ; Charles Soussen, Directeur de thèse ; A. Mohammad-Djafari, Directeur de thèse Editeur : Paris-Orsay : Université de Paris 11 Paris-Sud Centre d'Orsay Année de publication : 2019 Importance : 181 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de Doctorat de l’Université Paris-Saclay préparée à l’Université Paris-Sud Sciences et Technologies de l’Information et de la Communication (STIC) Spécialité : Traitement du signal et des images Langues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] approximation [Termes IGN] détection du signal [Termes IGN] écho radar [Termes IGN] émetteur [Termes IGN] estimation bayesienne [Termes IGN] inférence statistique [Termes IGN] modèle de mélange multilinéaire [Termes IGN] modulation du signal [Termes IGN] probabilités [Termes IGN] valeur aberrante Index. décimale : THESE Thèses et HDR Résumé : (auteur) In Electronic Warfare, radar signals identification is a supreme asset for decision making in military tactical situations. By providing information about the presence of threats, classification and clustering of radar signals have a significant role ensuring that countermeasures against enemies are well-chosen and enabling detection of unknown radar signals to update databases. Most of the time, Electronic Support Measures systems receive mixtures of signals from different radar emitters in the electromagnetic environment. Hence a radar signal, described by a pulse-to-pulse modulation pattern, is often partially observed due to missing measurements and measurement errors. The identification process relies on statistical analysis of basic measurable parameters of a radar signal which constitute both quantitative and qualitative data. Many general and practical approaches based on data fusion and machine learning have been developed and traditionally proceed to feature extraction, dimensionality reduction and classification or clustering. However, these algorithms cannot handle missing data and imputation methods are required to generate data to use them. Hence, the main objective of this work is to define a classification/clustering framework that handles both outliers and missing values for any types of data. Here, an approach based on mixture models is developed since mixture models provide a mathematically based, flexible and meaningful framework for the wide variety of classification and clustering requirements. The proposed approach focuses on the introduction of latent variables that give us the possibility to handle sensitivity of the model to outliers and to allow a less restrictive modelling of missing data. A Bayesian treatment is adopted for model learning, supervised classification and clustering and inference is processed through a variational Bayesian approximation since the joint posterior distribution of latent variables and parameters is untractable. Some numerical experiments on synthetic and real data show that the proposed method provides more accurate results than standard algorithms. Note de contenu : Introduction 1- State of the art and the selected approach 2- Continuous data 3- Mixed data 4- Temporal evolution data 5- Conclusion and perspectives Numéro de notice : 25703 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Traitement du signal et des images : Paris 11 : 2019 Organisme de stage : Thales, GPI nature-HAL : Thèse DOI : sans Date de publication en ligne : 02/09/2019 En ligne : https://hal.science/tel-02275817 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94829

Robust collaborative nonnegative matrix factorization for hyperspectral unmixing / Jun Li in IEEE Transactions on geoscience and remote sensing, vol 54 n° 10 (October 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 10 (October 2016) . - pp 6076 - 6090 Titre : Robust collaborative nonnegative matrix factorization for hyperspectral unmixing Type de document : Article/Communication Auteurs : Jun Li, Auteur ; José M. Bioucas-Dias, Auteur ; Antonio J. Plaza, Auteur ; Lin Liu, Auteur Année de publication : 2016 Article en page(s) : pp 6076 - 6090 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] factorisation de matrice non-négative [Termes IGN] modèle de mélange multilinéaire [Termes IGN] signature spectrale Résumé : (auteur) Spectral unmixing is an important technique for remotely sensed hyperspectral data exploitation. It amounts to identifying a set of pure spectral signatures, which are called endmembers, and their corresponding fractional, draftrulesabun-dances in each pixel of the hyperspectral image. Over the last years, different algorithms have been developed for each of the three main steps of the spectral unmixing chain: 1) estimation of the number of endmembers in a scene; 2) identification of the spectral signatures of the endmembers; and 3) estimation of the fractional abundance of each endmember in each pixel of the scene. However, few algorithms can perform all the stages involved in the hyperspectral unmixing process. Such algorithms are highly desirable to avoid the propagation of errors within the chain. In this paper, we develop a new algorithm, which is termed robust collaborative nonnegative matrix factorization (R-CoNMF), that can perform the three steps of the hyperspectral unmixing chain. In comparison with other conventional methods, R-CoNMF starts with an overestimated number of endmembers and removes the redundant endmembers by means of collaborative regularization. Our experimental results indicate that the proposed method provides better or competitive performance when compared with other widely used methods. Numéro de notice : A2016-868 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2580702 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2580702 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83025 [article]

Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach / Abderrahim Halimi in IEEE Transactions on geoscience and remote sensing, vol 54 n° 7 (July 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 7 (July 2016) . - pp 3811 - 3821 Titre : Estimating the intrinsic dimension of hyperspectral images using a noise-whitened eigengap approach Type de document : Article/Communication Auteurs : Abderrahim Halimi, Auteur ; Paul Honeine, Auteur ; Malika Kharouf, Auteur ; Cédric Richard, Auteur ; Jean-Yves Tourneret, Auteur Année de publication : 2016 Article en page(s) : pp 3811 - 3821 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bruit blanc [Termes IGN] image hyperspectrale [Termes IGN] modèle de mélange multilinéaire [Termes IGN] valeur propre Résumé : (Auteur) Linear mixture models are commonly used to represent a hyperspectral data cube as linear combinations of endmember spectra. However, determining the number of endmembers for images embedded in noise is a crucial task. This paper proposes a fully automatic approach for estimating the number of endmembers in hyperspectral images. The estimation is based on recent results of random matrix theory related to the so-called spiked population model. More precisely, we study the gap between successive eigenvalues of the sample covariance matrix constructed from high-dimensional noisy samples. The resulting estimation strategy is fully automatic and robust to correlated noise owing to the consideration of a noise-whitening step. This strategy is validated on both synthetic and real images. The experimental results are very promising and show the accuracy of this algorithm with respect to state-of-the-art algorithms. Numéro de notice : A2016-873 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2016.2528298 En ligne : http://dx.doi.org/10.1109/TGRS.2016.2528298 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83032 [article]

A multilinear mixing model for nonlinear spectral unmixing / Rob Heylen in IEEE Transactions on geoscience and remote sensing, vol 54 n° 1 (January 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 1 (January 2016) . - pp 240 - 251 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 linéaire Résumé : (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 [article]

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