Descripteur
Termes IGN > mathématiques > statistique mathématique > régression
régressionSynonyme(s)analyse de régressionVoir aussi |
Documents disponibles dans cette catégorie (662)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Differential positioning based on the orthogonal transformation algorithm with GNSS multi-system / Xiao Liang in GPS solutions, vol 22 n° 3 (July 2018)
[article]
Titre : Differential positioning based on the orthogonal transformation algorithm with GNSS multi-system Type de document : Article/Communication Auteurs : Xiao Liang, Auteur ; Zhigang Huang, Auteur ; Honglei Qin, Auteur Année de publication : 2018 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] ambiguïté entière
[Termes IGN] erreur instrumentale
[Termes IGN] erreur systématique
[Termes IGN] filtre de Kalman
[Termes IGN] méthode des moindres carrés
[Termes IGN] positionnement différentiel
[Termes IGN] résolution d'ambiguïté
[Termes IGN] simple différence
[Vedettes matières IGN] Traitement de données GNSSRésumé : (Auteur) Combining global navigation satellite systems (GNSSs) will significantly increase the number of visible satellites and, thus, will improve the geometry of observed satellites, resulting in improved positioning reliability and accuracy. We focus on GNSS multi-system differential positioning based on a single-system orthogonal transformation algorithm. The orthogonal transformation algorithm using single-difference measurements is proposed to avoid the high correlation between measurements and the unnecessary prominence to the reference satellite in double-difference positioning. In addition, the algorithm uses a more straightforward recursive least squares method to avoid the effect of uncertainties of the Kalman filter. We discuss the model differences between combined system positioning and single-system positioning and verify that the combining observations of different systems should start to be used after clock biases have been reduced, respectively. Moreover, as to rising and setting of satellites in multi-system differential positioning, we propose to use matrix transform to separate the setting satellites of combined systems at an epoch. This can avoid the correlation of initial integer ambiguity vectors of different systems. The experimental results show that the proposed method can handle the change of satellites automatically and combine multiple systems for reliable and accuracy differential positioning. The method especially outperforms the basic single-system orthogonal transformation positioning and traditional multi-system double-difference positioning in a complex environment. Numéro de notice : A2018-371 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s10291-018-0754-6 Date de publication en ligne : 02/07/2018 En ligne : https://doi.org/10.1007/s10291-018-0754-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90763
in GPS solutions > vol 22 n° 3 (July 2018)[article]Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network / Ruibin Zhao in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
[article]
Titre : Classifying airborne LiDAR point clouds via deep features learned by a multi-scale convolutional neural network Type de document : Article/Communication Auteurs : Ruibin Zhao, Auteur ; Mingyong Pang, Auteur ; Jidong Wang, Auteur Année de publication : 2018 Article en page(s) : pp 960 - 979 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] apprentissage profond
[Termes IGN] classification
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] régression
[Termes IGN] réseau neuronal convolutif
[Termes IGN] semis de pointsRésumé : (Auteur) Point cloud classification plays a critical role in many applications of airborne light detection and ranging (LiDAR) data. In this paper, we present a deep feature-based method for accurately classifying multiple ground objects from airborne LiDAR point clouds. With several selected attributes of LiDAR point clouds, our method first creates a group of multi-scale contextual images for each point in the data using interpolation. Taking the contextual images as inputs, a multi-scale convolutional neural network (MCNN) is then designed and trained to learn the deep features of LiDAR points across various scales. A softmax regression classifier (SRC) is finally employed to generate classification results of the data with a combination of the deep features learned from various scales. Compared with most of traditional classification methods, which often require users to manually define a group of complex discriminant rules or extract a set of classification features, the proposed method has the ability to automatically learn the deep features and generate more accurate classification results. The performance of our method is evaluated qualitatively and quantitatively using the International Society for Photogrammetry and Remote Sensing benchmark dataset, and the experimental results indicate that our method can effectively distinguish eight types of ground objects, including low vegetation, impervious surface, car, fence/hedge, roof, facade, shrub and tree, and achieves a higher accuracy than other existing methods. Numéro de notice : A2018-196 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1431840 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1431840 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89861
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 960 - 979[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018031 RAB Revue Centre de documentation En réserve L003 Disponible Gen*: a generic toolkit to generate spatially explicit synthetic populations / Kevin Chapuis in International journal of geographical information science IJGIS, vol 32 n° 5-6 (May - June 2018)
[article]
Titre : Gen*: a generic toolkit to generate spatially explicit synthetic populations Type de document : Article/Communication Auteurs : Kevin Chapuis, Auteur ; Patrick Taillandier , Auteur ; Misslin Renaud, Auteur ; Alexis Drogoul, Auteur Année de publication : 2018 Article en page(s) : pp 1194 - 1210 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications SIG
[Termes IGN] distribution spatiale
[Termes IGN] figuration de la densité
[Termes IGN] modèle orienté agent
[Termes IGN] population urbaine
[Termes IGN] programmation par contraintes
[Termes IGN] recensement démographique
[Termes IGN] régression
[Termes IGN] Rouen
[Termes IGN] système d'information géographiqueRésumé : (Auteur) Agent-based models tend to integrate more and more data that can deeply impact their outcomes. Among these data, the ones that deal with agent attributes and localization are particularly important, but are very difficult to collect. In order to tackle this issue, we propose a complete generic toolkit called Gen* dedicated to generating spatially explicit synthetic populations from global (census and GIS) data. This article focuses on the localization methods provided by Gen* that are based on regression, geometrical constraints and spatial distributions. The toolkit is applied for a case study concerning the generation of the population of Rouen (France) and shows the capabilities of Gen* regarding population spatialization. Numéro de notice : A2018-204 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2018.1440563 Date de publication en ligne : 26/02/2018 En ligne : https://doi.org/10.1080/13658816.2018.1440563 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89875
in International journal of geographical information science IJGIS > vol 32 n° 5-6 (May - June 2018) . - pp 1194 - 1210[article]Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 079-2018031 RAB Revue Centre de documentation En réserve L003 Disponible Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes / Hans Ole Ørka in Forestry, an international journal of forest research, vol 91 n° 2 (April 2018)
[article]
Titre : Effects of terrain slope and aspect on the error of ALS-based predictions of forest attributes Type de document : Article/Communication Auteurs : Hans Ole Ørka, Auteur ; Ole Martin Bollandsås, Auteur ; Endre H. Hansen, Auteur ; Erik Naesset, Auteur ; Terje Gobakken, Auteur Année de publication : 2018 Article en page(s) : pp 225 - 237 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] analyse de variance
[Termes IGN] données dendrométriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] inventaire forestier (techniques et méthodes)
[Termes IGN] inventaire forestier étranger (données)
[Termes IGN] modèle de simulation
[Termes IGN] Norvège
[Termes IGN] pente
[Termes IGN] régression non linéaire
[Vedettes matières IGN] Inventaire forestierRésumé : (Auteur) Wall-to-wall forest management inventories with the area-based method using airborne laser scanner (ALS) data are operational in many countries. With this method, empirical relationships are established between ALS metrics and ground reference observations of forest attributes, and wall-to-wall predictions can be made over large areas. However, the prediction errors may be influenced by terrain slope and aspect because the properties of the ALS point cloud are dependent on these factors. Two datasets covering wide ranges of terrain slope and aspect, collected in the western part of Norway, were analysed. The first dataset represented sample plots from an ordinary operational forest management inventory and the second dataset were collected as an experimental dataset where clusters of sample plots were distributed on slopes with different inclinations. Six forest attributes were predicted using non-linear regression and the prediction errors were analysed using univariate- and multivariate analysis of variance. The results showed that slope and aspect affected the prediction errors, but that the effects were small in magnitude. Thus, the current study concludes that terrain effects seem to be negligible in operational forest inventories. Numéro de notice : A2018-652 Affiliation des auteurs : non IGN Thématique : FORET Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1093/forestry/cpx058 Date de publication en ligne : 30/01/2018 En ligne : https://doi.org/10.1093/forestry/cpx058 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93238
in Forestry, an international journal of forest research > vol 91 n° 2 (April 2018) . - pp 225 - 237[article]Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records / Zhang Liu in Transactions in GIS, vol 22 n° 2 (April 2018)
[article]
Titre : Mapping hourly dynamics of urban population using trajectories reconstructed from mobile phone records Type de document : Article/Communication Auteurs : Zhang Liu, Auteur ; Ting Ma, Auteur ; Yunyan Du, Auteur ; Tao Pei, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 494 - 513 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse spatio-temporelle
[Termes IGN] carte thématique
[Termes IGN] cartographie des flux
[Termes IGN] classification par réseau neuronal
[Termes IGN] mobilité urbaine
[Termes IGN] population urbaine
[Termes IGN] régression
[Termes IGN] téléphone intelligent
[Termes IGN] trace numérique
[Termes IGN] trajet (mobilité)Résumé : (Auteur) Understanding the spatiotemporal dynamics of urban population is crucial for addressing a wide range of urban planning and management issues. Aggregated geospatial big data have been widely used to quantitatively estimate population distribution at fine spatial scales over a given time period. However, it is still a challenge to estimate population density at a fine temporal resolution over a large geographical space, mainly due to the temporal asynchrony of population movement and the challenges to acquiring a complete individual movement record. In this article, we propose a method to estimate hourly population density by examining the time‐series individual trajectories, which were reconstructed from call detail records using BP neural networks. We first used BP neural networks to predict the positions of mobile phone users at an hourly interval and then estimated the hourly population density using log‐linear regression at the cell tower level. The estimated population density is linearly correlated with population census data at the sub‐district level. Trajectory clustering results show five distinct diurnal dynamic patterns of population movement in the study area, revealing spatially explicit characteristics of the diurnal commuting flows, though the driving forces of the flows need further investigation. Numéro de notice : A2018-215 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12323 Date de publication en ligne : 26/02/2018 En ligne : https://doi.org/10.1111/tgis.12323 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90006
in Transactions in GIS > vol 22 n° 2 (April 2018) . - pp 494 - 513[article]A methodology for least-squares local quasi-geoid modelling using a noisy satellite-only gravity field model / R. Klees in Journal of geodesy, vol 92 n° 4 (April 2018)PermalinkHarmonic regression of Landsat time series for modeling attributes from national forest inventory data / Barry T. Wilson in ISPRS Journal of photogrammetry and remote sensing, vol 137 (March 2018)PermalinkRegional geoid computation by least squares modified Hotine’s formula with additive corrections / Silja Märdla in Journal of geodesy, vol 92 n° 3 (March 2018)PermalinkRemote estimation of canopy leaf area index and chlorophyll content in Moso bamboo (Phyllostachys edulis (Carrière) J. Houz.) forest using MODIS reflectance data / Xiaojun Xu in Annals of Forest Science, vol 75 n° 1 (March 2018)PermalinkLRAGE : learning latent relationships with adaptive graph embedding for aerial scene classification / Yuebin Wang in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)PermalinkAn accurate Kriging-based regional ionospheric model using combined GPS/BeiDou observations / Mohamed Abdelazeem in Journal of applied geodesy, vol 12 n° 1 (January 2018)PermalinkA hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning / Rasmus M. Houborg in ISPRS Journal of photogrammetry and remote sensing, vol 135 (January 2018)PermalinkPermalinkIntroduction to multiple regression equations in datum transformations and their reversibility / Andrew Carey Ruffhead in Survey review, vol 50 n° 358 (January 2018)PermalinkMixed integer–real least squares estimation for precise GNSS positioning using a modified ambiguity function approach / Krzysztof Nowel in GPS solutions, vol 22 n° 1 (January 2018)PermalinkPermalinkPermalinkRéseaux de neurones convolutionnels profonds pour la détection de petits véhicules en imagerie aérienne / Jean Ogier du Terrail (2018)PermalinkPermalinkPermalinkPerformance analysis of BDS/GPS precise point positioning with undifferenced ambiguity resolution / Min Wang in Advances in space research, vol 60 n° 12 (15 December 2017)PermalinkAbove-bottom biomass retrieval of aquatic plants with regression models and SfM data acquired by a UAV platform – A case study in Wild Duck Lake Wetland, Beijing, China / Ran Jing in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)PermalinkArea-based estimation of growing stock volume in Scots pine stands using ALS and airborne image-based point clouds / Paweł Hawryło in Forestry, an international journal of forest research, vol 90 n° 5 (December 2017)PermalinkEstimation and mapping of above-ground biomass of mangrove forests and their replacement land uses in the Philippines using Sentinel imagery / Jose Alan A. Castillo in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)PermalinkEfficient weighted total least-squares solution for partial errors-in-variables model / J. Zhao in Survey review, vol 49 n° 356 (November 2017)Permalink