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CSTST 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)
Titre : CSTST 2008, the 5th International conference on soft computing as transdisciplinary science and technology, October 28th - October 31st 2008, University of Cergy-Pontoise, France : Proceedings Type de document : Actes de congrès Auteurs : Richard Chbeir, Éditeur scientifique ; Youakim Badr, Éditeur scientifique ; Ajith Abraham, Éditeur scientifique ; Dominique Laurent , Éditeur scientifique ; Fernando Ferri, Éditeur scientifique Editeur : New York [Etats-Unis] : Association for computing machinery ACM Année de publication : 2008 Conférence : CSTST 2008, 5th International conference on soft computing as transdisciplinary science and technology 28/10/2008 31/10/2008 Cergy-Pontoise France Proceedings ACM Importance : 693 p. Format : 17 x 24 cm ISBN/ISSN/EAN : 978-1-60558-046-3 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Informatique
[Termes IGN] algorithmique
[Termes IGN] architecture logicielle
[Termes IGN] classification
[Termes IGN] découverte de connaissances
[Termes IGN] exploration de données
[Termes IGN] ingénierie des connaissances
[Termes IGN] logique floue
[Termes IGN] optimisation (mathématiques)
[Termes IGN] réseau neuronal artificielNuméro de notice : 19702 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Actes DOI : sans En ligne : https://dl.acm.org/doi/proceedings/10.1145/1456223 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=82642 ContientRéservation
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Code-barres Cote Support Localisation Section Disponibilité 19702-01 CG2008 Livre Centre de documentation Congrès Disponible A 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)
[article]
Titre : A supervised artificial immune classifier for remote-sensing imagery Type de document : Article/Communication Auteurs : Y. Zhong, Auteur ; L. Zhang, Auteur ; J. Gong, Auteur ; P. Li, Auteur Année de publication : 2007 Article en page(s) : pp 3957 - 3966 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] classificateur
[Termes IGN] classification dirigée
[Termes IGN] image
[Termes IGN] réseau neuronal artificiel
[Termes IGN] système immunitaire artificielRésumé : (Auteur) The artificial immune network (AIN), which is a new computational intelligence model based on artificial immune systems inspired by the vertebrate immune system, has been widely utilized for pattern recognition and data analysis. However, due to the inherent complexity of current AIN models, their application to remote-sensing image classification has been rather limited. This paper presents a novel supervised classification algorithm based on a multiple-valued immune network, which is a novel AIN model, to perform remote-sensing image classification. The proposed method trains the immune network using the samples of regions of interest and obtains an immune network with memory to classify the remote-sensing imagery. Two experiments with different types of images are performed to evaluate the performance of the proposed algorithm in comparison with other traditional image classification algorithms: Parallelepiped, Minimum Distance, Maximum Likelihood, and Back-Propagation Neural Network. The results evince that the proposed algorithm consistently outperforms the traditional algorithms in all the experiments and, hence, provides an effective option for processing remote-sensing imagery. Copyright IEEE Numéro de notice : A2007-585 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2007.907739 En ligne : https://doi.org/10.1109/TGRS.2007.907739 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28948
in IEEE Transactions on geoscience and remote sensing > vol 45 n° 12 Tome 1 (December 2007) . - pp 3957 - 3966[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 065-07121A RAB Revue Centre de documentation En réserve L003 Disponible Visibility prediction based on artificial neural networks used in automatic network design / M. Saadatseresht in Photogrammetric record, vol 22 n° 120 (December 2007 - February 2008)
[article]
Titre : Visibility prediction based on artificial neural networks used in automatic network design Type de document : Article/Communication Auteurs : M. Saadatseresht, Auteur ; Masood Varshosaz, Auteur Année de publication : 2007 Article en page(s) : pp 336 - 355 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] apprentissage automatique
[Termes IGN] incertitude des données
[Termes IGN] photogrammétrie terrestre
[Termes IGN] réseau neuronal artificiel
[Termes IGN] visibilitéRésumé : (Auteur) Automatic design of photogrammetric networks is a complex task for which the visibility and quality constraints need to be both modelled and satisfied simultaneously. The task becomes even more complex when measurements are carried out for the first time on a large and/or complex object surrounded by multiple obstructions in a confined workspace. In this situation, automatic visibility prediction of a target point becomes an extremely difficult task. The visibility information inherent within the initial photogrammetric network can be used to solve this problem. However, this introduces some uncertainty into the prediction result because of the incompleteness of the visibility information. In a previous study, the authors developed an analytical deterministic method, visibility uncertainty prediction (VUP), that used ‘‘visibility spheres’’ to predict the visibility of target points. This paper investigates the use of artificial neural networks (ANNs) in visibility prediction, and presents a new technique, ANN-based visibility uncertainty prediction (AVUP), that works by training a feed-forward multi-layer ANN. The visibility data for this network is extracted from the initial photogrammetric network. Once trained, the network can be used to predict the visibility of any target point from a potential camera station. Various experiments were carried out to evaluate the proposed technique. The results showed that, compared to the previous deterministic method, it is more accurate and has a lower computational cost. Copyright RS&PS + Blackwell Publishing Numéro de notice : A2007-569 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/j.1477-9730.2007.00454.x En ligne : https://doi.org/10.1111/j.1477-9730.2007.00454.x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28932
in Photogrammetric record > vol 22 n° 120 (December 2007 - February 2008) . - pp 336 - 355[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 106-07041 Revue Centre de documentation Revues en salle Disponible Artificial 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)
[article]
Titre : Artificial neural network with backpropagation learning to predict mean monthly total ozone in Arosa, Switzerland Type de document : Article/Communication Auteurs : S. Chattopadhyay, Auteur ; G. Bandyopadhyay, Auteur Année de publication : 2007 Article en page(s) : pp 4471 - 4482 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale
[Termes IGN] analyse diachronique
[Termes IGN] apprentissage automatique
[Termes IGN] ozone
[Termes IGN] Perceptron multicouche
[Termes IGN] pollution atmosphérique
[Termes IGN] prédiction
[Termes IGN] réseau neuronal artificiel
[Termes IGN] SuisseRésumé : (Auteur) The present study deals with the mean monthly total ozone time series over Arosa, Switzerland. The study period is 1932-1971. First of all, the total ozone time series has been identified as a complex system and then Artificial Neural Network models in the form of Multilayer Perceptron with back propagation learning have been developed. The models are Single-hidden-layer and Two-hidden-layer Perceptrons with sigmoid activation function. After sequential learning with a learning rate of 0.9 the peak total ozone period (February-May) concentrations of mean monthly total ozone have been predicted by the two neural net models. After training and validation, both of the models are found to be skillful. But the Two-hidden-layer Perceptron is found to be more adroit in predicting the mean monthly total ozone concentrations over the aforesaid period. Copyright Taylor & Francis Numéro de notice : A2007-448 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701250440 En ligne : https://doi.org/10.1080/01431160701250440 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28811
in International Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4471 - 4482[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-07111 RAB Revue Centre de documentation En réserve L003 Disponible Multispectral 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)
[article]
Titre : Multispectral image classification: a supervised neural computation approach based on rough-fuzzy membership function and weak fuzzy similarity relation Type de document : Article/Communication Auteurs : A. Agrawal, Auteur ; N. Kumar, Auteur ; M. Radhakrishna, Auteur Année de publication : 2007 Article en page(s) : pp 4597 - 4608 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification dirigée
[Termes IGN] classification par réseau neuronal
[Termes IGN] ERDAS Imagine
[Termes IGN] image IRS-LISS
[Termes IGN] image multibande
[Termes IGN] incertitude des données
[Termes IGN] Inde
[Termes IGN] Kappa de Cohen
[Termes IGN] Perceptron multicouche
[Termes IGN] sous ensemble flouRésumé : (Auteur) A supervised neural network classification model based on rough-fuzzy membership function, weak fuzzy similarity relation, multilayer perceptron, and back-propagation algorithm is proposed. The described model is capable of dealing with rough uncertainty as well as fuzzy uncertainty associated with the classification of multispectral images. The concept of weak fuzzy similarity relation is used for generation of fuzzy equivalence classes during the calculation of rough-fuzzy membership function. The model allows efficient modelling of indiscernibility and fuzziness between patterns by appropriate weights being assigned using the back-propagated errors depending upon the rough-fuzzy membership values at the corresponding outputs. The effectiveness of the proposed model is demonstrated on classification problem of IRS-P6 LISS IV image of Allahabad area. The results are compared with statistical (minimum distance to means), conventional Multi-Layer Perceptron (MLP) and Fuzzy Multi-Layer Perceptron (FMLP) models. The better overall accuracy, user's and producer's accuracies and kappa coefficient of the proposed classifier in comparison to other considered models demonstrate the effectiveness of this model in multispectral image classification. Copyright Taylor & Francis Numéro de notice : A2007-449 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701244898 En ligne : https://doi.org/10.1080/01431160701244898 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28812
in International Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4597 - 4608[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 080-07111 RAB Revue Centre de documentation En réserve L003 Disponible Brainy 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)PermalinkIntegrating LIDAR elevation data, multi-spectral imagery and neural network modelling for marsh characterization / J.T. Morris in International Journal of Remote Sensing IJRS, vol 26 n° 23 (December 2005)PermalinkAn artificial-neural-network-based, constrained CA model for simulating urban growth / Q. Guan in Cartography and Geographic Information Science, vol 32 n° 4 (October 2005)PermalinkAutomatic 3D object recognition and reconstruction based on neuro-fuzzy modelling / F. Samadzadegan in ISPRS Journal of photogrammetry and remote sensing, vol 59 n° 5 (August - October 2005)PermalinkAssessment of simulated cognitive maps: the influence of prior knowledge from cartographic maps / R.E. Lloyd in Cartography and Geographic Information Science, vol 32 n° 3 (July 2005)PermalinkVisualizing demographic trajectories with self-organizing maps / A. Skupin in Geoinformatica, vol 9 n° 2 (June - August 2005)PermalinkNeural network model for standard PCA and its variants applied to remote sensing / S. Chitroub in International Journal of Remote Sensing IJRS, vol 26 n° 10 (May 2005)Permalink