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Main problems in building European environmental spatial data / A. Annoni in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 24 n° 20 (October 2003) . - pp 3887 - 3902 Titre : Main problems in building European environmental spatial data Type de document : Article/Communication Auteurs : A. Annoni, Auteur ; P. Smits, Auteur Année de publication : 2003 Article en page(s) : pp 3887 - 3902 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Infrastructure de données [Termes IGN] infrastructure européenne de données localisées [Termes IGN] métadonnées [Termes IGN] métadonnées géographiques [Termes IGN] projection [Termes IGN] système d'information géographique [Termes IGN] système de référence géodésique [Termes IGN] Union Européenne Résumé : (Auteur) The Geographical Information & Geographical Information Systems (GI&GIS) project of the Joint Research Centre is actively involved in assisting in the creation of the European Spatial Data Infrastructure (ESDI), as well as in activities related to the conception, creation and harmonization of pan-European spatial databases relevant for the policies of the European Union (EU), mainly through the support and co-ordination of networks in various thematic fields. Remotely sensed datasets play an important role in the ESDI and it is argued that the creation of an ESDI will greatly impact the GI and the remote sensing communities alike. Hence the main objective of this paper is to make the remote sensing community familiar with the current developments at EU level. This paper also illustrates major issues related to the harmonization of existing national datasets or the creation of new ones having a European or pan-European extent. In particular, the following technical aspects are discussed : terrestrial reference systems, projection systems, core data definition and geospatial metadata. Numéro de notice : A2003-283 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000103763 En ligne : https://doi.org/10.1080/0143116031000103763 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22578 [article]

Exemplaires(1)

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080-03201	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Data fusion and feature extraction in the wavelet domain / Magnus Orn Ulfarsson in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 24 n° 20 (October 2003) . - pp 3933 - 3945 Titre : Data fusion and feature extraction in the wavelet domain Type de document : Article/Communication Auteurs : Magnus Orn Ulfarsson, Auteur ; Jon Atli Benediktsson, Auteur ; Johannes R. Sveinsson, Auteur Année de publication : 2003 Article en page(s) : pp 3933 - 3945 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification par réseau neuronal [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] transformation en ondelettes Résumé : (Auteur) This paper concentrates on data fusion, feature extraction, feature selection and neural network classification for multi-source remote sensing and geographical data. The considered feature extraction method is based on the discrete wavelet transformation (DWT). The original data are transformed using DWT and then a feature selection mechanism is applied to select features from the full feature set in the wavelet domain. The feature selection mechanism is a binary genetic algorithm which selects the best features to be used in a neural network classification. In experiments on two datasets, the proposed data fusion and feature extraction method performed well in terms of overall accuracies as compared to results obtained with other wellknown feature extraction methods. Numéro de notice : A2003-284 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000103790 En ligne : https://doi.org/10.1080/0143116031000103790 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22579 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-03201	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

A neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery / E. Binaghi in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 24 n° 20 (October 2003) . - pp 3947 - 3959 Titre : A neural adaptive model for feature extraction and recognition in high resolution remote sensing imagery Type de document : Article/Communication Auteurs : E. Binaghi, Auteur ; I. Gallo, Auteur ; M. Pepe, Auteur Année de publication : 2003 Article en page(s) : pp 3947 - 3959 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement de formes [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage numérique d'image [Termes IGN] image à haute résolution [Termes IGN] Perceptron multicouche [Termes IGN] reconnaissance de formes [Termes IGN] système expert [Termes IGN] variation d'échelle Résumé : (Auteur) Contextual classification methods, which require the extraction of complex spatial information over a range of scales, from fine details in local areas to large features that extend across the image, are necessary in many remote sensing image classification studies. This work presents a supervised adaptive object recognition model which integrates scale-space filtering techniques for feature extraction within a Multilayer Perceptron neural network and the back-propagation learning task of the search of the most adequate filter parameters. The experimental evaluation of the method has been conducted in an easily controlled domain using synthetic imagery, and in the real domain coping with object recognition in high-resolution remote sensing imagery. To investigate whether the strategy can be considered an alternative to conventional procedures the results were compared with those obtained by a well known contextual classification scheme. Numéro de notice : A2003-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000103808 En ligne : https://doi.org/10.1080/0143116031000103808 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22580 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-03201	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt

Bayesian classification by data augmentation / B. Regguzoni in International Journal of Remote Sensing IJRS, vol 24 n° 20 (October 2003)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 24 n° 20 (October 2003) . - pp 3961 - 3981 Titre : Bayesian classification by data augmentation Type de document : Article/Communication Auteurs : B. Regguzoni, Auteur ; Fernando Sanso, Auteur ; Giovanna Venuti, Auteur ; P.A. Brivio, Auteur Année de publication : 2003 Article en page(s) : pp 3961 - 3981 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] axiome de Bayes [Termes IGN] classification automatique d'objets [Termes IGN] classification bayesienne [Termes IGN] classification par maximum de vraisemblance Résumé : (Auteur) A typical remote sensing data clustering is the maximum likelihood supervised procedure. It consists of the estimation of a suitable mixture of distributions, based on training samples only, and in the subsequent pixelbypixel classification, performed by maximizing the likelihood ratio. In this way all the information on the parameters of the distributions, contained in the unsurveyed samples, is lost. In the paper it is proposed to apply a suitable Bayesian method, known as a data augmentation algorithm, to fully exploit the information contained in the data. The method is presented in detail and applied to an elementary simulated example proving its capability of achieving almost the theoretical limit for the classification error. Comparisons with current classification methods as well as an application to a real dataset are reported. Numéro de notice : A2003-286 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/0143116031000103817 En ligne : https://doi.org/10.1080/0143116031000103817 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22581 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-03201	RAB	Revue	Centre de documentation	En réserve L003	Exclu du prêt