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Multiview automatic target recognition for infrared imagery using collaborative sparse priors / Xuelu Li in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 6776 - 6790 Titre : Multiview automatic target recognition for infrared imagery using collaborative sparse priors Type de document : Article/Communication Auteurs : Xuelu Li, Auteur ; Vishal Monga, Auteur ; Abhijit Mahalanobis, Auteur Année de publication : 2020 Article en page(s) : pp 6776 - 6790 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] ajustement de paramètres [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection de cible [Termes descripteurs IGN] données clairsemées [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] image à basse résolution [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] reconnaissance automatique Résumé : (auteur) The low resolution of infrared (IR) images makes feature extraction for classification of a challenging work. Learning-based methods, therefore, are preferred to be used on such raw imagery. In this article, in order to avoid difficulties in feature extraction, a novel multitask extension of the widely used sparse-representation-classification (SRC) method is proposed in both single and multiview set-ups. That is, the test sample could be a single IR image or images from different views. In both single-view and multiview scenarios, we try to employ collaborative spike and slab priors. This is because the traditional sparsity-inducing measures such as the l0 -row pseudonorm makes it hard to capture the sparse structure of the coefficient matrix when expanded in terms of a training dictionary, and the priors are proved to be able to capture fairly general sparse structures. Furthermore, a joint prior and sparse coefficient estimation method (JPCEM) is proposed for the first time in this article in order to alleviate the need to handpick prior parameters required before classification. Multiple experiments are conducted on a synthetic Comanche Forward Looking IR (FLIR) Automatic Target Recognition (ATR) database collected by Army Research Lab and a challenging mid-wave IR (MWIR) image ATR database made available by the U.S. Army Night Vision and Electronic Sensors Directorate. The final results substantiate the merits of the proposed JPCEM through comparisons with other state-of-the-art methods, including both the ones based on SRC and the ones constructed using deep learning frameworks. Numéro de notice : A2020-584 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2973969 date de publication en ligne : 26/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2973969 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95908 [article]

Spatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships / Chao Song in Annals of GIS, vol 26 n° 3 (July 2020)  

Public [article]  inAnnals of GIS > vol 26 n° 3 (July 2020) . - pp 277 - 291 Titre : Spatiotemporally Varying Coefficients (STVC) model: a Bayesian local regression to detect spatial and temporal nonstationarity in variables relationships Type de document : Article/Communication Auteurs : Chao Song, Auteur ; Xun Shi, Auteur ; Jinfeng Wang, Auteur Année de publication : 2020 Article en page(s) : pp 277 - 291 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes descripteurs IGN] Chine [Termes descripteurs IGN] données socio-économiques [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] géostatistique [Termes descripteurs IGN] modélisation spatio-temporelle [Termes descripteurs IGN] régression [Termes descripteurs IGN] régression géographiquement pondérée [Termes descripteurs IGN] santé [Termes descripteurs IGN] série temporelle Résumé : (auteur) Local regression has an advantage over global regression by allowing coefficients that qualify variables relationships being heterogeneous, where such varying regression relationships are nonstationarity. Spatiotemporally Varying Coefficients (STVC) model is the first Bayesian-based local spatiotemporal regression approach, intending to simultaneously detect spatial and temporal nonstationarity for heterogeneous response-covariate variables relationships, through separately estimating posterior local-scale coefficients over different space areas and time frames. In this paper, we first presented a general Bayesian STVC modelling paradigm as a specification guide to show its commonality in broader geospatial research. Then, we employed it to solve a real-world issue concerning spatiotemporal healthcare-socioeconomic relations, for which we derived data of county-level hospital beds number per capita, as well as data of related socioeconomic factors in northeast China during 2002–2011. Results showed that the STVC model surpassed all the other comparative regressions, in terms of both Bayesian model fitness and predictive ability. Globally, resident savings, financial institutions loans, GDP, and primary industry were identified as key socioeconomic conditions affecting healthcare resources in Northeast China. Temporally, with Time-Coefficients (TC) plots, we found that after 2011, GDP and primary industry would further help improve the overall healthcare level of northeast China. Spatially, with Space-Coefficients (SC) maps, we could directly identify the relative contribution of four socioeconomic covariates’ impacts on healthcare within each administrative county. Bayesian STVC model is an essential development and extension of the local regression family for exploring the spatiotemporal heterogeneous variables relationships, especially under Bayesian statistics, as well as GIScience and spatial statistics. Numéro de notice : A2020-582 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/19475683.2020.1782469 date de publication en ligne : 08/08/2020 En ligne : https://doi.org/10.1080/19475683.2020.1782469 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95904 [article]

Camera orientation, calibration and inverse perspective with uncertainties: a Bayesian method applied to area estimation from diverse photographs / Grégoire Guillet in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 159 (January 2020) . - pp 237 - 255 Titre : Camera orientation, calibration and inverse perspective with uncertainties: a Bayesian method applied to area estimation from diverse photographs Type de document : Article/Communication Auteurs : Grégoire Guillet, Auteur ; Thomas Guillet, Auteur ; Ludovic Ravanel, Auteur Année de publication : 2020 Article en page(s) : pp 237 - 255 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] ajustement de paramètres [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] autocorrélation spatiale [Termes descripteurs IGN] distorsion d'image [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] étalonnage de chambre métrique [Termes descripteurs IGN] figuration de la densité [Termes descripteurs IGN] fonction inverse [Termes descripteurs IGN] image 2D [Termes descripteurs IGN] image aérienne [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] longueur focale [Termes descripteurs IGN] méthode de Monte-Carlo par chaînes de Markov [Termes descripteurs IGN] modèle numérique de surface [Termes descripteurs IGN] orientation externe [Termes descripteurs IGN] photographie numérique [Termes descripteurs IGN] vue 3D [Termes descripteurs IGN] vue perspective Résumé : (Auteur) Large collections of images have become readily available through modern digital catalogs, from sources as diverse as historical photographs, aerial surveys, or user-contributed pictures. Exploiting the quantitative information present in such wide-ranging collections can greatly benefit studies that follow the evolution of landscape features over decades, such as measuring areas of glaciers to study their shrinking under climate change. However, many available images were taken with low-quality lenses and unknown camera parameters. Useful quantitative data may still be extracted, but it becomes important to both account for imperfect optics, and estimate the uncertainty of the derived quantities. In this paper, we present a method to address both these goals, and apply it to the estimation of the area of a landscape feature traced as a polygon on the image of interest. The technique is based on a Bayesian formulation of the camera calibration problem. First, the probability density function (PDF) of the unknown camera parameters is determined for the image, based on matches between 2D (image) and 3D (world) points together with any available prior information. In a second step, the posterior distribution of the feature area of interest is derived from the PDF of camera parameters. In this step, we also model systematic errors arising in the polygon tracing process, as well as uncertainties in the digital elevation model. The resulting area PDF therefore accounts for most sources of uncertainty. We present validation experiments, and show that the model produces accurate and consistent results. We also demonstrate that in some cases, accounting for optical lens distortions is crucial for accurate area determination with consumer-grade lenses. The technique can be applied to many other types of quantitative features to be extracted from photographs when careful error estimation is important. Numéro de notice : A2020-015 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.11.013 date de publication en ligne : 02/12/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.11.013 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94404 [article]

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Sentinel-2 sharpening using a reduced-rank method / Magnus Orn Ulfarsson in IEEE Transactions on geoscience and remote sensing, vol 57 n° 9 (September 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 9 (September 2019) . - pp 6408 - 6420 Titre : Sentinel-2 sharpening using a reduced-rank method Type de document : Article/Communication Auteurs : Magnus Orn Ulfarsson, Auteur ; Frosti Palsson, Auteur ; Mauro Dalla Mura, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2019 Article en page(s) : pp 6408 - 6420 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] affinage d'image [Termes descripteurs IGN] ajustement de paramètres [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] fusion de données [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] largeur de bande Résumé : (auteur) Recently, the Sentinel-2 (S2) satellite constellation was deployed for mapping and monitoring the Earth environment. Images acquired by the sensors mounted on the S2 platforms have three levels of spatial resolution: 10, 20, and 60 m. In many remote sensing applications, the availability of images at the highest spatial resolution (i.e., 10 m for S2) is often desirable. This can be achieved by generating a synthetic high-resolution image through data fusion. To this end, researchers have proposed techniques exploiting the spectral/spatial correlation inherent in multispectral data to sharpen the lower resolution S2 bands to 10 m. In this paper, we propose a novel method that formulates the sharpening process as a solution to an inverse problem. We develop a cyclic descent algorithm called S2Sharp and an associated tuning parameter selection algorithm based on generalized cross validation and Bayesian optimization. The tuning parameter selection method is evaluated on a simulated data set. The effectiveness of S2Sharp is assessed experimentally by comparisons to state-of-the-art methods using both simulated and real data sets. Numéro de notice : A2019-340 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2906048 date de publication en ligne : 22/04/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2906048 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93377 [article]

Genetic diversity and structure of Silver fir (Abies alba Mill.) at the south-eastern limit of its distribution range / Maria Teodosiu in Annals of forest research, vol 62 n° 2 (June - December 2019)  

Public [article]  inAnnals of forest research > vol 62 n° 2 (June - December 2019) Titre : Genetic diversity and structure of Silver fir (Abies alba Mill.) at the south-eastern limit of its distribution range Type de document : Article/Communication Auteurs : Maria Teodosiu, Auteur ; Georgeta Mihai, Auteur ; Barbara Fussi, Auteur ; Elena Ciocîrlan, Auteur Année de publication : 2019 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] Abies alba [Termes descripteurs IGN] aire de répartition [Termes descripteurs IGN] analyse de groupement [Termes descripteurs IGN] analyse de variance [Termes descripteurs IGN] Carpates [Termes descripteurs IGN] changement climatique [Termes descripteurs IGN] composition d'un peuplement forestier [Termes descripteurs IGN] conservation des ressources forestières [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] estimation bayesienne [Termes descripteurs IGN] génétique forestière [Termes descripteurs IGN] indice de diversité [Termes descripteurs IGN] Roumanie [Vedettes matières IGN] Sylviculture Résumé : (auteur) In the Romanian Carpathians, Silver fir covers about 5% of the forest area and is the second most important conifer species. Although there are a number of genetic studies concerning the distribution of genetic diversity of Abies alba in Europe, populations from the south-eastern limit of the distribution range have been studied less. The aim of the present study was to assess the genetic diversity and differentiation in 36 silver fir populations along the Carpathian Mountains in Romania, using seven microsatellites loci. High levels of genetic diversity (He = 0.779 to 0.834 and AR = 11.61 to 14.93) were found in all populations. Eastern Carpathians populations show higher levels of diversity, both in allelic richness and expected heterozygosity and higher degrees of genetic differentiation compared to southern populations. Bayesian clustering analysis revealed the existence of two genetically distinct groups for silver fir populations, one larger cluster which comprises the Inner Eastern Carpathians, Curvature Carpathians, South Carpathians and the Banat Mountains and the second cluster contained most of the North and Outer Eastern Carpathians population. Both AMOVA and Barrier analysis supported genetic differentiation among geographical provenance regions. The high genetic diversity of silver fir populations from the eastern limit of its distribution provide high potential to mitigate the negative effects of climate warming being valuable genetic resources in the context of global change. The distribution pattern of genetic variation at local, regional and country scale could and should be considered for the preservation of the forest genetic resources. Numéro de notice : A2019-613 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article DOI : 10.15287/afr.2019.1436 date de publication en ligne : 26/11/2019 En ligne : https://doi.org/10.15287/afr.2019.1436 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94846 [article]

Bayesian calibration of a carbon balance model PREBAS using data from permanent growth experiments and national forest inventory / Francesco Minunno in Forest ecology and management, vol 440 (15 May 2019)  

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Estimation parcimonieuse de biais multitrajets pour les systèmes GNSS / Julien Lesouple (2019)  

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Exploitation de séries temporelles d'images multi-sources pour la cartographie des surfaces en eau / Filsa Bioresita (2019)  

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Simultaneous characterization of objects temperature and radiative properties through multispectral infrared thermography / Thibaud Toullier (2019)  

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

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Amélioration de la vitesse et de la qualité d'image du rendu basé image / Rodrigo Ortiz Cayón (2017)  

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Space–time adaptive processing and motion parameter estimation in multistatic passive radar using sparse Bayesian learning / Qisong Wu in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

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Multifractal analysis for multivariate data with application to remote sensing / Sébastien Combrexelle (2016)  

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Object detection in optical remote sensing images based on weakly supervised learning and high-level feature learning / Junwei Han in IEEE Transactions on geoscience and remote sensing, vol 53 n° 6 (June 2015)  

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Bayesian approach to tree detection based on airborne laser scanning data / Timo Lähivaara in IEEE Transactions on geoscience and remote sensing, vol 52 n° 5 tome 1 (May 2014)  

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