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Dynamic environmental modelling in GIS: 2. Modelling error propagation / D. Karssenberg in International journal of geographical information science IJGIS, vol 19 n° 6 (july 2005)
[article]
Titre : Dynamic environmental modelling in GIS: 2. Modelling error propagation Type de document : Article/Communication Auteurs : D. Karssenberg, Auteur ; K. de Jong, Auteur Année de publication : 2005 Article en page(s) : pp 623 - 637 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] langage de modélisation
[Termes IGN] méthode de Monte-Carlo
[Termes IGN] modèle dynamique
[Termes IGN] modèle stochastique
[Termes IGN] modélisation 3D
[Termes IGN] modélisation spatio-temporelle
[Termes IGN] propagation d'erreurRésumé : (Auteur) Environmental modelling languages provide the possibility to construct models in two or three spatial dimensions. These models can be static models, without a time component, or dynamic models. Dynamic models are simulations run forward in time, where the state of the model at time t is defined as a function of its state in a period or time step preceding t. Since inputs and parameters of environmental models are associated with errors, environmental modelling languages need to provide techniques to calculate how these errors propagate to the output(s) of the model. Since these techniques are not yet available, the paper describes concepts for extending an environmental-modelling language with functionality for error-propagation modelling. The approach models errors in inputs and parameters as stochastic variables, while the error in the model outputs is approximated with a Monte Carlo simulation. A modelling language is proposed which combines standard functions in a structured script (program) for building environmental models, and calculation of error propagation in these models. A prototype implementation of the language is used in three example models to illustrate the concepts. Numéro de notice : A2005-286 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810500104799 En ligne : https://doi.org/10.1080/13658810500104799 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27422
in International journal of geographical information science IJGIS > vol 19 n° 6 (july 2005) . - pp 623 - 637[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-05061 RAB Revue Centre de documentation En réserve L003 Disponible 079-05062 RAB Revue Centre de documentation En réserve L003 Disponible Representing and reducing error in natural-resource classification using model combination / Zhi Huang in International journal of geographical information science IJGIS, vol 19 n° 5 (may 2005)
[article]
Titre : Representing and reducing error in natural-resource classification using model combination Type de document : Article/Communication Auteurs : Zhi Huang, Auteur Année de publication : 2005 Article en page(s) : pp 603 - 621 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] analyse comparative
[Termes IGN] classification de Dempster-Shafer
[Termes IGN] classification par arbre de décision
[Termes IGN] classification par réseau neuronal
[Termes IGN] erreur d'attribut
[Termes IGN] erreur d'échantillon
[Termes IGN] précision de la classification
[Termes IGN] propagation d'erreur
[Termes IGN] ressources naturellesRésumé : (Auteur) Artificial Intelligence (AI) models such as Artificial Neural Networks (ANNs), Decision Trees and Dempster-Shafer's Theory of Evidence have long claimed to be more error-tolerant than conventional statistical models, but the way error is propagated through these models is unclear. Two sources of error have been identified in this study: sampling error and attribute error. The results show that these errors propagate differently through the three AI models. The Decision Tree was the most affected by error, the Artificial Neural Network was less affected by error, and the Theory of Evidence model was not affected by the errors at all. The study indicates that AI models have very different modes of handling errors. In this case, the machine-learning models, including ANNs and Decision Trees, are more sensitive to input errors. Dempster-Shafer's Theory of Evidence has demonstrated better potential in dealing with input errors when multisource data sets are involved. The study suggests a strategy of combining AI models to improve classification accuracy. Several combination approaches have been applied, based on a 'majority voting system', a simple average, Dempster-Shafer's Theory of Evidence, and fuzzy-set theory. These approaches all increased classification accuracy to some extent. Two of them also demonstrated good performance in handling input errors. Second-stage combination approaches which use statistical evaluation of the initial combinations are able to further improve classification results. One of these second-stage combination approaches increased the overall classification accuracy on forest types to 54% from the original 46.5% of the Decision Tree model, and its visual appearance is also much closer to the ground data. By combining models, it becomes possible to calculate quantitative confidence measurements for the classification results, which can then serve as a better error representation. Final classification products include not only the predicted hard classes for individual cells, but also estimates of the probability and the confidence measurements of the prediction. Numéro de notice : A2005-239 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810500032446 En ligne : https://doi.org/10.1080/13658810500032446 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27376
in International journal of geographical information science IJGIS > vol 19 n° 5 (may 2005) . - pp 603 - 621[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-05051 RAB Revue Centre de documentation En réserve L003 Disponible 079-05052 RAB Revue Centre de documentation En réserve L003 Disponible Landsat-7 ETM+ radiometric normalization comparison for northern mapping application / I. Olthof in Remote sensing of environment, vol 95 n° 3 (15/04/2005)
[article]
Titre : Landsat-7 ETM+ radiometric normalization comparison for northern mapping application Type de document : Article/Communication Auteurs : I. Olthof, Auteur ; D. Pouliot, Auteur ; R. Fernandes, Auteur ; R. Latifovic, Auteur Année de publication : 2005 Article en page(s) : pp 388 - 398 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse comparative
[Termes IGN] cartographie numérique
[Termes IGN] correction radiométrique
[Termes IGN] image Landsat-ETM+
[Termes IGN] image SPOT-Végétation
[Termes IGN] méthode robuste
[Termes IGN] mosaïque d'images
[Termes IGN] propagation d'erreur
[Termes IGN] régressionRésumé : (Auteur) Relative radiometric normalization has long been performed to generate consistency among individual Landsat scenes for production of composites containing multiple scenes. Normalization methods have relied on matching identical and assumed invariant features in both images of an overlapping pair, or on invariant targets that are not necessarily the same features. Problems with overlap normalization methods include sensitivity to outliers in overlap data caused by atmospheric or land cover change between scenes, which can lead to radiometric error propagation across a mosaic caused by a normalized scene becoming a reference for the subsequent scene entered into the mosaic. Solutions to such problems include interactive outlier removal to generate a normalization function using a 'no change' data set and methods that are robust against outliers to automatically generate normalization functions with minimal user input. This paper compares two normalization methods that use a robust regression technique called Theil-Sen with an established overlap normalization method. The first method uses Theil-Sen regression to generate a normalization function between overlap regions, while the second uses Theil-Sen to normalize to coarse-resolution composite reflectance data from the SPOT VEGETATION (VGT) sensor. The results of the normalizations were evaluated in two ways: (1) using statistics generated between overlap regions; and (2) separately using coarse-resolution data as a reference. Both overlap normalization methods performed almost identically; however, Theil-Sen was faster and easier to implement than its traditional counterpart due to its insensitivity to outliers and capability for full automation. While overlap and coarse-resolution normalizations each outperformed the other when evaluated against its calibration set, error propagation caused by outliers in overlap samples was avoided in the normalization to coarse-resolution imagery. Advantages offered by normalization to coarse-resolution data using robust regression, including full automation, make this method particularly attractive for generation of large area mosaics containing 100 Landsat scenes or more. Numéro de notice : A2005-171 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.rse.2004.06.024 En ligne : https://doi.org/10.1016/j.rse.2004.06.024 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27309
in Remote sensing of environment > vol 95 n° 3 (15/04/2005) . - pp 388 - 398[article]Pedestrian dead reckoning : a solution to navigation in GPS signal degraded areas? / O. Mezentsev in Geomatica, vol 59 n° 2 (April 2005)
[article]
Titre : Pedestrian dead reckoning : a solution to navigation in GPS signal degraded areas? Type de document : Article/Communication Auteurs : O. Mezentsev, Auteur ; Gérard Lachapelle, Auteur ; et al., Auteur Année de publication : 2005 Article en page(s) : pp 175 - 182 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement
[Termes IGN] atténuation du signal
[Termes IGN] erreur de positionnement
[Termes IGN] estimation statistique
[Termes IGN] modèle stochastique
[Termes IGN] navigation à l'estime
[Termes IGN] navigation pédestre
[Termes IGN] positionnement par GPS
[Termes IGN] propagation d'erreurRésumé : (Auteur) Cet article présente une analyse du rendement des systèmes de navigation piétonnière à l'estime (NPE) à exactitude moyenne. De tels systèmes de NPE, basés sur des détecteurs autonomes conviennent bien pour une utilisation à l'intérieur et dans les canyons urbains où les signaux GPS sont dégradés ou ne peuvent fournir une géométrie adéquate. Une analyse des principaux facteurs ayant un apport dans les erreurs de position du système de NPE, notamment l'erreur de la longueur du pas et l'erreur de la direction est présentée. L'analyse discute aussi de l'importance de l'initialisation adéquate du NPE. Les auteurs proposent une nouvelle méthode pour évaluer l'exactitude de la position en 2D de la navigation autonome du système de NPE. L'article présente et analyse aussi plusieurs modèles stochastiques pour représenter les sources principales d'erreurs des NPE pour un système de NPE de qualité moyenne, notamment l'erreur de l'estimation de la longueur du pas. Une analyse quantitative de la limite supérieure de l'erreur de la position horizontale pour un système de NPE d'une qualité donnée est effectué pour le cas d'une marche en droite ligne ce qui représente le pire cas de propagation de l'erreur horizontale du NPE. À l'aide de cette analyse, les exigences d'un système de NPE et de l'exactitude de l'initialisation peuvent être estimés pour obtenir l'exactitude désirée de la navigation en fonction du temps. L'analyse de faisabilité est mise à l'essai à l'aide d'une expérience sur le terrain. Copyright Geomatica Numéro de notice : A2005-318 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : 10.5623/geomat-2005-0023 En ligne : https://doi.org/10.5623/geomat-2005-0023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27454
in Geomatica > vol 59 n° 2 (April 2005) . - pp 175 - 182[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 035-05021 RAB Revue Centre de documentation En réserve L003 Disponible The choice of window size in approximating topographic surfaces from digital elevation models / M. Albani in International journal of geographical information science IJGIS, vol 18 n° 6 (october 2004)
[article]
Titre : The choice of window size in approximating topographic surfaces from digital elevation models Type de document : Article/Communication Auteurs : M. Albani, Auteur ; Brian Klinkenberg, Auteur ; et al., Auteur Année de publication : 2004 Article en page(s) : pp 577 - 593 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] autocorrélation spatiale
[Termes IGN] Colombie-Britannique (Canada)
[Termes IGN] erreur de mesure
[Termes IGN] méthode des moindres carrés
[Termes IGN] modèle numérique de surface
[Termes IGN] profil topographique
[Termes IGN] propagation d'erreur
[Termes IGN] topographie localeRésumé : (Auteur) Quantitative surface analysis through quadratic modelling of Digital Elevation Models (DEMs) is a promising tool for automatically describing the physical environment in ecological studies of terrestrial landscapes. Fundamental topographic variables such as slope, aspect, plan and profile curvature can be simply calculated from the parameters of a conic equation fitted to a DEM window through the least-squares method. The scale of the analysis, defined by the size of the DEM window used to fit the conic equation, affects both the estimated value of the topographic variables and the propagation of elevation errors to derived topographic variables. The least-squares method is amenable to the analytical treatment of the propagation of elevation errors to the derived topographical variables. A general analytical model of error propagation is presented that accounts for the effects of window size and of spatial autocorrelation in elevation errors. The method is based on the Taylor approximation of the least-square fitting equation and allows for the presence of stationary autocorrelation in the elevation errors. In numerical simulations with DEMs from British Columbia, Canada, it is shown that increasing the size of evaluation windows effectively reduces the propagation of elevation errors to the derived topographic variables. However, this was obtained at the expense of topographic detail. A methodology is proposed to evaluate quantitatively the loss of topographic detail through a X2-test of the corrected residuals in the immediate neighbourhood of the evaluation point. This methodology, in combination with the analytical model of error propagation, can be used to select the scale or range of scales at which to calculate topographic variables from a DEM. Numéro de notice : A2004-351 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810410001701987 En ligne : https://doi.org/10.1080/13658810410001701987 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26878
in International journal of geographical information science IJGIS > vol 18 n° 6 (october 2004) . - pp 577 - 593[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-04061 RAB Revue Centre de documentation En réserve L003 Disponible 079-04062 RAB Revue Centre de documentation En réserve L003 Disponible Modelling error propagation in vector-based overlay analysis / Wei Shi in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 1-2 (August 2004 - April 2005)PermalinkReview article: geometric processing of remote sensing images: models, algorithms and methods / Thierry Toutin in International Journal of Remote Sensing IJRS, vol 25 n° 10 (May 2004)PermalinkA probability-based uncertainty model for point-in-polygon analysis in GIS / C.K. Cheung in Geoinformatica, vol 8 n° 1 (March - May 2004)PermalinkPath processing and block adjustment with RadarSat-1 SAR images / Thierry Toutin in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)PermalinkModelling error propagation in vector-based buffer analysis / Wei Shi in International journal of geographical information science IJGIS, vol 17 n° 3 (may 2003)PermalinkAccuracy prediction for ortho-image generation / Amnon Krupnik in Photogrammetric record, vol 18 n° 101 (March - May 2003)PermalinkAnalyse und Optimierung geodätischer Messanordnungen unter besonderer Berücksichtigung des Intervallansatzes / S. Schön (2003)PermalinkError tracking in Ikonos geometric processing using a 3D parametric model / Thierry Toutin in Photogrammetric Engineering & Remote Sensing, PERS, vol 69 n° 1 (January 2003)PermalinkExperimental evaluation of positional accuracy estimates from linear network using point- and line-based testing methods / T.G. Van Niel in International journal of geographical information science IJGIS, vol 16 n° 5 (july 2002)PermalinkLarge deviation theorems for weighted sums applied to a geographical problem / Olivier Bonin in Journal of Applied Probability, vol 39 n° 2 (01/06/2002)Permalink