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A neural network-based method for solving "nested hierarchy" areal interpolation problems / D. Merwin in Cartography and Geographic Information Science, vol 36 n° 4 (October 2009)
[article]
Titre : A neural network-based method for solving "nested hierarchy" areal interpolation problems Type de document : Article/Communication Auteurs : D. Merwin, Auteur ; R. Cromley, Auteur ; Daniel L. Civco, Auteur Année de publication : 2009 Article en page(s) : pp 347 - 365 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Statistiques
[Termes IGN] analyse comparative
[Termes IGN] Connecticut (Etats-Unis)
[Termes IGN] figuration de la densité
[Termes IGN] interpolation par pondération de zones
[Termes IGN] interpolation spatiale
[Termes IGN] prévision
[Termes IGN] recensement démographique
[Termes IGN] régression linéaire
[Termes IGN] réseau neuronal artificiel
[Termes IGN] structure hiérarchique de donnéesRésumé : (Auteur) This study proposes a neural network approach to solving areal interpolation scenarios, specifically the “nested hierarchy” problem. The neural network method presented adopts the approach taken by intelligent interpolation methods where ancillary spatial information is presented to assist in achieving more accurate results. For this study, the data to be estimated are total populations for census tracts and block groups in Hartford County, Connecticut. A number of neural network models are generated containing various combinations of ancillary spatial information. The neural-network-derived predictions are compared with the predicted populations derived from three existing interpolation methods: areal weighting, a dasymetric areal weighting approach using remote sensing data, and ordinary least squares (OLS) regression. For each scenario presented, the proposed neural network approach outperforms each of the existing methods. Numéro de notice : A2009-441 Affiliation des auteurs : non IGN Thématique : MATHEMATIQUE Nature : Article DOI : 10.1559/152304009789786335 En ligne : https://doi.org/10.1559/152304009789786335 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30072
in Cartography and Geographic Information Science > vol 36 n° 4 (October 2009) . - pp 347 - 365[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 032-09041 RAB Revue Centre de documentation En réserve L003 Disponible Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes / Konstantinos Topouzelis in Geocarto international, vol 24 n° 3 (June - July 2009)
[article]
Titre : Potentiality of feed-forward neural networks for classifying dark formations to oil spills and look-alikes Type de document : Article/Communication Auteurs : Konstantinos Topouzelis, Auteur ; V. Karathanassi, Auteur ; P. Pavlaskis, Auteur ; D. Rokos, Auteur Année de publication : 2009 Article en page(s) : pp 179 - 191 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] classification par réseau neuronal
[Termes IGN] détection
[Termes IGN] fonction de base radiale
[Termes IGN] hydrocarbure
[Termes IGN] image radar
[Termes IGN] marée noire
[Termes IGN] Perceptron multicouche
[Termes IGN] pollution des mers
[Termes IGN] rétrodiffusionRésumé : (Auteur) Radar backscatter values from oil spills are very similar to backscatter values from very calm sea areas and other ocean phenomena. Several studies aiming at oil spill detection have been conducted. Most of these studies rely on the detection of dark areas, which have high Bayesian probability of being oil spills. The drawback of these methods is a complex process, mainly because non-linearly separable datasets are introduced in statistically based decisions. The use of neural networks (NNs) in remote sensing has increased significantly, as NNs can simultaneously handle non-linear data of a multidimensional input space. In this article, we investigate the ability of two commonly used feed-forward NN models: multilayer perceptron (MLP) and radial basis function (RBF) networks, to classify dark formations in oil spills and look-alike phenomena. The appropriate training algorithm, type and architecture of the optimum network are subjects of research. Inputs to the networks are the original synthetic aperture radar image and other images derived from it. MLP networks are recognized as more suitable for oil spill detection. Numéro de notice : A2009-186 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106040802488526 Date de publication en ligne : 19/05/2009 En ligne : https://doi.org/10.1080/10106040802488526 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29816
in Geocarto international > vol 24 n° 3 (June - July 2009) . - pp 179 - 191[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 059-09031 RAB Revue Centre de documentation En réserve L003 Disponible Representing geographical objects with scale-induced indeterminate boundaries: a neural network-based data model / José L. Silvan-Cardenas in International journal of geographical information science IJGIS, vol 23 n°3-4 (march - april 2009)
[article]
Titre : Representing geographical objects with scale-induced indeterminate boundaries: a neural network-based data model Type de document : Article/Communication Auteurs : José L. Silvan-Cardenas, Auteur ; L. Wang, Auteur ; F.B. Zhan, Auteur Année de publication : 2009 Article en page(s) : pp 295 - 318 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées
[Termes IGN] limite indéterminée
[Termes IGN] modèle conceptuel de données localisées
[Termes IGN] objet géographique
[Termes IGN] réseau neuronal artificiel
[Termes IGN] sous ensemble flouRésumé : (Auteur) The degree of uncertainty of many geographical objects has long been known to be in intimate relation with the scale of its observation and representation. Yet, the explicit consideration of scaling operations when modeling uncertainty is rarely found. In this study, a neural network-based data model was investigated for representing geographical objects with scale-induced indeterminate boundaries. Two types of neural units, combined with two types of activation function, comprise the processing core of the model, where the activation function can model either hard or soft transition zones. The construction of complex fuzzy regions, as well as lines and points, is discussed and illustrated with examples. It is shown how the level of detail that is apparent in the boundary at a given scale can be controlled through the degree of smoothness of each activation function. Several issues about the practical implementation of the model are discussed and indications on how to perform complex overlay operations of fuzzy maps provided. The model was illustrated through an example of representing multi-resolution, sub-pixel maps that are typically derived from remote sensing techniques. Copyright Taylor & Francis Numéro de notice : A2009-152 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article DOI : 10.1080/13658810801932021 En ligne : https://doi.org/10.1080/13658810801932021 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29782
in International journal of geographical information science IJGIS > vol 23 n°3-4 (march - april 2009) . - pp 295 - 318[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-09021 RAB Revue Centre de documentation En réserve L003 Disponible 079-09022 RAB Revue Centre de documentation En réserve L003 Disponible vol 74 n° 10 - October 2008 - Artificial intelligence in remote sensing (Bulletin de Photogrammetric Engineering & Remote Sensing, PERS) / American society for photogrammetry and remote sensing
[n° ou bulletin]
est un bulletin de Photogrammetric Engineering & Remote Sensing, PERS / American society for photogrammetry and remote sensing (1975 -)
Titre : vol 74 n° 10 - October 2008 - Artificial intelligence in remote sensing Type de document : Périodique Auteurs : American society for photogrammetry and remote sensing, Auteur Année de publication : 2008 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] algorithme génétique
[Termes IGN] automate cellulaire
[Termes IGN] intelligence artificielle
[Termes IGN] réseau neuronal artificiel
[Termes IGN] séparateur à vaste margeNuméro de notice : 105-0810 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=10346 [n° ou bulletin]Contient
- Subpixel urban land cover estimation: comparing cubist, random forests, and support vector regression / J. Walton in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)
- Neuro-fuzzy based analysis of hyperspectral imagery / F. Qiu in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)
- Genetic algorithms for the calibration of cellular automata urban growth modeling / J. Shan in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 10 (October 2008)
Using neural networks and cellular automata for modelling intra-urban land-use dynamics / C.M. Almeida in International journal of geographical information science IJGIS, vol 22 n° 8-9 (august 2008)
[article]
Titre : Using neural networks and cellular automata for modelling intra-urban land-use dynamics Type de document : Article/Communication Auteurs : C.M. Almeida, Auteur ; J.M. Gleriani, Auteur ; E.F. Castejon, Auteur ; B.S. Soares-Filho, Auteur Année de publication : 2008 Article en page(s) : pp 943 - 963 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] automate cellulaire
[Termes IGN] milieu urbain
[Termes IGN] modèle de simulation
[Termes IGN] planification urbaine
[Termes IGN] réseau neuronal artificiel
[Termes IGN] Sao Paulo
[Termes IGN] utilisation du sol
[Termes IGN] villeRésumé : (Auteur) Empirical models designed to simulate and predict urban land-use change in real situations are generally based on the utilization of statistical techniques to compute the land-use change probabilities. In contrast to these methods, artificial neural networks arise as an alternative to assess such probabilities by means of non-parametric approaches. This work introduces a simulation experiment on intra-urban land-use change in which a supervised back-propagation neural network has been employed in the parameterization of several biophysical and infrastructure variables considered in the simulation model. The spatial land-use transition probabilities estimated thereof feed a cellular automaton (CA) simulation model, based on stochastic transition rules. The model has been tested in a medium-sized town in the Midwest of Sao Paulo State, Piracicaba. A series of simulation outputs for the case study town in the period 1985-1999 were generated, and statistical validation tests were then conducted for the best results, based on fuzzy similarity measures. Copyright Taylor & Francis Numéro de notice : A2008-310 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/13658810701731168 En ligne : https://doi.org/10.1080/13658810701731168 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29303
in International journal of geographical information science IJGIS > vol 22 n° 8-9 (august 2008) . - pp 943 - 963[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-08051 RAB Revue Centre de documentation En réserve L003 Disponible 079-08052 RAB Revue Centre de documentation En réserve L003 Disponible The application of artificial neural networks to the analysis of remotely sensed data / J.F. Mas in International Journal of Remote Sensing IJRS, vol 29 n°3-4 (February 2008)PermalinkCSTST 2008, the 5th International conference on soft computing as transdisciplinary science and technology, October 28th - October 31st 2008, University of Cergy-Pontoise, France / Richard Chbeir (2008)PermalinkA supervised artificial immune classifier for remote-sensing imagery / Y. Zhong in IEEE Transactions on geoscience and remote sensing, vol 45 n° 12 Tome 1 (December 2007)PermalinkVisibility prediction based on artificial neural networks used in automatic network design / M. Saadatseresht in Photogrammetric record, vol 22 n° 120 (December 2007 - February 2008)PermalinkArtificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland / S. Chattopadhyay in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkMultispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation / A. Agrawal in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)PermalinkBrainy positioning: processing GPS data with neural networks / Rodrigo Figueiredo Leandro in GPS world, vol 18 n° 9 (September 2007)PermalinkMapping of environmental data using kernel-based methods / Mikhail Kanevski in Revue internationale de géomatique, vol 17 n° 3-4 (septembre 2007 – février 2008)PermalinkArtificial neural networks for mapping regional-scale upland vegetation from high spatial resolution imagery / H. Mills in International Journal of Remote Sensing IJRS, vol 27 n° 11 (June 2006)PermalinkExamining the use of stored navigation knowledge for neural network based INS/GPS integration / Kai-Wei Chiang in Geomatica, vol 60 n° 1 (March 2006)Permalink