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Automated extraction of 3D vector topographic feature line from terrain point cloud / Wei Zhou in Geocarto international, vol 33 n° 10 (October 2018)
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Titre : Automated extraction of 3D vector topographic feature line from terrain point cloud Type de document : Article/Communication Auteurs : Wei Zhou, Auteur ; Rencan Peng, Auteur ; Jian Dong, Auteur ; Tao Wang, Auteur Année de publication : 2018 Article en page(s) : pp 1036 - 1047 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] arbre aléatoire minimum
[Termes IGN] détection d'objet
[Termes IGN] extraction de traits caractéristiques
[Termes IGN] ligne caractéristique
[Termes IGN] lissage de données
[Termes IGN] modèle numérique de terrain
[Termes IGN] objet géographique linéaire
[Termes IGN] repère de Laplace
[Termes IGN] segmentation en régions
[Termes IGN] semis de pointsRésumé : (auteur) This paper presents an automated topographic feature lines detection method that directly extracts 3D vector topographic feature lines from terrain point cloud. First, signed surface variation (SSV) is introduced to extract the potential feature points. Secondly, the potential feature points are segmented to different clusters by combining region growing segmentation and conditional Euclidean clustering. In order to extract feature points, the potential feature points in each cluster are iteratively thinned using a HC-Laplacian smoothing method with SSV weighted taken into account. Besides, SSV-based and elevation-based simple rules are added for accelerating this thinning process. Finally, the feature lines are obtained by constructing the minimum spanning tree of the extracted feature points. By comparing with manually digitized reference lines, the correctness and the completeness of extracted results are about 80% or even higher, which are much higher than those extracted by D8 algorithm. Numéro de notice : A2019-046 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1325521 Date de publication en ligne : 18/05/2017 En ligne : https://doi.org/10.1080/10106049.2017.1325521 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92064
in Geocarto international > vol 33 n° 10 (October 2018) . - pp 1036 - 1047[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-2018041 RAB Revue Centre de documentation En réserve L003 Disponible Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images / Michele Volpi in ISPRS Journal of photogrammetry and remote sensing, vol 144 (October 2018)
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Titre : Deep multi-task learning for a geographically-regularized semantic segmentation of aerial images Type de document : Article/Communication Auteurs : Michele Volpi, Auteur ; Devis Tuia, Auteur Année de publication : 2018 Article en page(s) : pp 48 - 60 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage profond
[Termes IGN] champ aléatoire conditionnel
[Termes IGN] image aérienne
[Termes IGN] orthoimage
[Termes IGN] réseau neuronal convolutif
[Termes IGN] segmentation sémantiqueRésumé : (Auteur) When approaching the semantic segmentation of overhead imagery in the decimeter spatial resolution range, successful strategies usually combine powerful methods to learn the visual appearance of the semantic classes (e.g. convolutional neural networks) with strategies for spatial regularization (e.g. graphical models such as conditional random fields). In this paper, we propose a method to learn evidence in the form of semantic class likelihoods, semantic boundaries across classes and shallow-to-deep visual features, each one modeled by a multi-task convolutional neural network architecture. We combine this bottom-up information with top-down spatial regularization encoded by a conditional random field model optimizing the label space across a hierarchy of segments with constraints related to structural, spatial and data-dependent pairwise relationships between regions. Our results show that such strategy provide better regularization than a series of strong baselines reflecting state-of-the-art technologies. The proposed strategy offers a flexible and principled framework to include several sources of visual and structural information, while allowing for different degrees of spatial regularization accounting for priors about the expected output structures. Numéro de notice : A2018-392 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.06.007 Date de publication en ligne : 05/07/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.06.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90826
in ISPRS Journal of photogrammetry and remote sensing > vol 144 (October 2018) . - pp 48 - 60[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 081-2018101 RAB Revue Centre de documentation En réserve L003 Disponible 081-2018103 DEP-EXM Revue LASTIG Dépôt en unité Exclu du prêt 081-2018102 DEP-EAF Revue Nancy Dépôt en unité Exclu du prêt Developing allometric equations for estimating shrub biomass in a Boreal Fen / Annie He in Forests, vol 9 n° 9 (September 2018)
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Titre : Developing allometric equations for estimating shrub biomass in a Boreal Fen Type de document : Article/Communication Auteurs : Annie He, Auteur ; Gregory J. McDermid, Auteur ; Mir Mustafizur Rahman, Auteur ; Maria Strack, Auteur ; Saraswati Saraswati, Auteur ; Bin Xu, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Végétation
[Termes IGN] allométrie
[Termes IGN] Alnus (genre)
[Termes IGN] biomasse aérienne
[Termes IGN] estimation statistique
[Termes IGN] marais
[Termes IGN] Salix (genre)
[Termes IGN] tourbeRésumé : (Auteur) Allometric equations for estimating aboveground biomass (AGB) from easily measured plant attributes are unavailable for most species common to mid-continental boreal peatlands, where shrubs comprise a large component of the vegetation community. Our study develops allometric equations for three dominant genera found in boreal fens: Alnus spp. (alder), Salix spp. (willow) and Betula pumila (bog birch). Two different types of local equations were developed: (1) individual equations based on genus/phylogeny, and (2) a general equation that pooled all individuals regardless of genera. The general equation had a R2 = 0.97 (n = 82), and was not significantly different (p > 0.05) than any of the phylogenetic equations. This indicated that a single generalized equation is sufficient in estimating AGB for all three genera occurring in our study area. A closer look at the performance of the general equation revealed that smaller stems were predicted less accurately than larger stems because of the higher variability of leafy biomass found in small individuals. Previously published equations developed in other ecoregions did not perform as well as our local equations. Numéro de notice : A2018-502 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/f9090569 Date de publication en ligne : 15/09/2018 En ligne : https://doi.org/10.3390/f9090569 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91216
in Forests > vol 9 n° 9 (September 2018)[article]Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data / P. Kumar in Geocarto international, vol 33 n° 9 (September 2018)
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Titre : Estimation of winter wheat crop growth parameters using time series Sentinel-1A SAR data Type de document : Article/Communication Auteurs : P. Kumar, Auteur ; R. Prasad, Auteur ; D. K. Gupta, Auteur ; V. N. Mishra, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 942 - 956 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] bande C
[Termes IGN] blé (céréale)
[Termes IGN] classification par forêts d'arbres décisionnels
[Termes IGN] croissance végétale
[Termes IGN] cultures
[Termes IGN] données polarimétriques
[Termes IGN] estimation statistique
[Termes IGN] hiver
[Termes IGN] image Sentinel-SAR
[Termes IGN] Leaf Area Index
[Termes IGN] régression
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste marge
[Termes IGN] teneur en eau de la végétationRésumé : (Auteur) In the present study, Sentinel-1A Synthetic Aperture Radar analysis of time series data at C-band was carried out to estimate the winter wheat crop growth parameters. Five different date images were acquired during January 2015–April 2015 at different growth stages from tillering to ripening in Varanasi district, India. The winter wheat crop parameters, i.e. leaf area index, vegetation water content (VWC), fresh biomass (FB), dry biomass (DB) and plant height (PH) were estimated using random forest regression (RFR), support vector regression (SVR), artificial neural network regression (ANNR) and linear regression (LR) algorithms. The Ground Range Detected products of Interferometric Wide (IW) Swath were used at VV polarization. The three different subplots of 1 m2 area were taken for the measurement of crop parameters at every growth stage. In total, 73 samples were taken as the training data-sets and 39 samples were taken as testing data-sets. The highest sensitivity (adj. R2 = 0.95579) of backscattering with VWC was found using RFR algorithm, whereas the lowest sensitivity (adj. R2 = 0.66201) was found for the PH using LR algorithm. Overall results indicate more accurate estimation of winter wheat parameters by the RFR algorithm followed by SVR, ANNR and LR algorithms. Numéro de notice : A2018-337 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2017.1316781 Date de publication en ligne : 18/04/2017 En ligne : https://doi.org/10.1080/10106049.2017.1316781 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90551
in Geocarto international > vol 33 n° 9 (September 2018) . - pp 942 - 956[article]A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models / Dengkui Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)
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Titre : A two-stage estimation method with bootstrap inference for semi-parametric geographically weighted generalized linear models Type de document : Article/Communication Auteurs : Dengkui Li, Auteur ; Chang-Lin Mei, Auteur Année de publication : 2018 Article en page(s) : pp 1860 - 1883 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] estimation statistique
[Termes IGN] inférence statistique
[Termes IGN] méthode du maximum de vraisemblance (estimation)
[Termes IGN] modèle linéaire
[Termes IGN] population urbaine
[Termes IGN] régression géographiquement pondérée
[Termes IGN] simulation
[Termes IGN] Tokyo (Japon)Résumé : (Auteur) Semi-parametric geographically weighted generalized linear models (S-GWGLMs) are a useful tool in modeling a regression relationship where the impact of certain explanatory variables on a non-Gaussian distributed response variable is global while that of others is spatially varying. In this article, we propose for S-GWGLMs a new estimation method, called two-stage geographically weighted maximum likelihood estimation, and further develop a likelihood ratio statistic-based bootstrap test to determine constant coefficients in the models. The performance of the estimation and test methods is then evaluated by simulations. The results show that the proposed estimation method performs as well as the existing method in estimating both constant and spatially varying coefficients but it is more efficient in terms of computation time; the bootstrap test is of accurate size under the null hypothesis and satisfactory power in identifying spatially varying coefficients. A real-world data set is finally analyzed to demonstrate the application of the proposed estimation and test methods. Numéro de notice : A2018-306 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1463443 Date de publication en ligne : 03/05/2018 En ligne : https://doi.org/10.1080/13658816.2018.1463443 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90449
in International journal of geographical information science IJGIS > vol 32 n° 9-10 (September - October 2018) . - pp 1860 - 1883[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2018051 RAB Revue Centre de documentation En réserve L003 Disponible Uncertainty modeling and analysis of surface area calculation based on a regular grid digital elevation model (DEM) / Chang Li in International journal of geographical information science IJGIS, vol 32 n° 9-10 (September - October 2018)
PermalinkA deep neural network with spatial pooling (DNNSP) for 3-D point cloud classification / Zhen Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 8 (August 2018)
PermalinkEstimating storm damage with the help of low-altitude photographs and different sampling designs and estimators / Pekka Hyvönen in Silva fennica, vol 52 n° 3 ([01/08/2018])
PermalinkSpectral-spatial classification of hyperspectral images using wavelet transform and hidden Markov random fields / Elham Kordi Ghasrodashti in Geocarto international, vol 33 n° 8 (August 2018)
PermalinkParametric bootstrap estimators for hybrid inference in forest inventories / Mathieu Fortin in Forestry, an international journal of forest research, vol 91 n° 3 (July 2018)
PermalinkStochastic models in the DORIS position time series : estimates for IDS contribution to ITRF2014 / Anna Klos in Journal of geodesy, vol 92 n° 7 (July 2018)
PermalinkGeometric reasoning with uncertain polygonal faces / Jochen Meidow in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)
PermalinkExploring the sensitivity of coastal inundation modelling to DEM vertical error / Harry West in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
PermalinkGeodetic VLBI with an artificial radio source on the Moon : a simulation study / Grzegorz Klopotek in Journal of geodesy, vol 92 n° 5 (May 2018)
PermalinkSeed dispersal, microsites or competition : what drives gap regeneration in an old-growth forest? An application of spatial point process modelling / Georg Gratzer in Forests, vol 9 n° 5 (May 2018)
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