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vol 27 n° 9-10 - september - october 2013 - Land change modelling: moving beyond projections (Bulletin de International journal of geographical information science IJGIS) / Martin Paegelow

Public [n° ou bulletin]  est un bulletin de International journal of geographical information science IJGIS (1997 - 2014)   Titre : vol 27 n° 9-10 - september - october 2013 - Land change modelling: moving beyond projections Type de document : Périodique Auteurs : Martin Paegelow, Éditeur scientifique ; M.T. Camacho Olmedo, Éditeur scientifique ; J.F. Mas, Éditeur scientifique ; et al., Auteur Année de publication : 2013 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Analyse spatiale [Termes IGN] changement d'utilisation du sol [Termes IGN] modélisation spatiale [Termes IGN] représentation du changement [Termes IGN] temps Numéro de notice : 079-201305 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Numéro de périodique Permalink : https://documentation.ensg.eu/index.php?lvl=bulletin_display&id=26386 [n° ou bulletin]

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079-2013051	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)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 29 n°3-4 (February 2008) . - pp 617 - 663 Titre : The application of artificial neural networks to the analysis of remotely sensed data Type de document : Article/Communication Auteurs : J.F. Mas, Auteur ; J.J. Flores, Auteur Année de publication : 2008 Article en page(s) : pp 617 - 663 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] classification par réseau neuronal [Termes IGN] image aérienne [Termes IGN] image satellite [Termes IGN] réseau neuronal artificiel Résumé : (Auteur) Artificial neural networks (ANNs) have become a popular tool in the analysis of remotely sensed data. Although significant progress has been made in image classification based upon neural networks, a number of issues remain to be resolved. This paper reviews remotely sensed data analysis with neural networks. First, we present an overview of the main concepts underlying ANNs, including the main architectures and learning algorithms. Then, the main tasks that involve ANNs in remote sensing are described. The limitations and crucial issues relating to the application of the neural network approach are discussed. A brief review of the implementation of ANNs in some of the most popular image processing software packages is presented. Finally, we discuss the application perspectives of neural networks in remote sensing image analysis. Copyright Taylor & Francis Numéro de notice : A2008-004 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701352154 En ligne : https://doi.org/10.1080/01431160701352154 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28999 [article]

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080-08021	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Comparison of pixel-based and object-oriented image classification approaches: a case study in a coal fire area, Wuda, Inner Mongolia, China / G. Yan in International Journal of Remote Sensing IJRS, vol 27 n°18 - 19 - 20 (October 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n°18 - 19 - 20 (October 2006) . - pp 4039 - 4055 Titre : Comparison of pixel-based and object-oriented image classification approaches: a case study in a coal fire area, Wuda, Inner Mongolia, China Type de document : Article/Communication Auteurs : G. Yan, Auteur ; J.F. Mas, Auteur ; B.H. Maathuis, Auteur ; et al., Auteur Année de publication : 2006 Article en page(s) : pp 4039 - 4055 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] charbon [Termes IGN] classification orientée objet [Termes IGN] classification par maximum de vraisemblance [Termes IGN] image Terra-ASTER [Termes IGN] incendie [Termes IGN] précision de la classification Résumé : (Auteur) Pixel-based and object-oriented classifications were tested for land-cover mapping in a coal fire area. In pixel-based classification a supervised Maximum Likelihood Classification (MLC) algorithm was utilized; in object-oriented classification, a region-growing multi-resolution segmentation and a soft nearest neighbour classifier were used. The classification data was an ASTER image and the typical area extent of most land-cover classes was greater than the image pixels (15 m). Classification results were compared in order to evaluate the suitability of the two classification techniques. The comparison was undertaken in a statistically rigorous way to provide an objective basis for comment and interpretation. Considering consistency, the same set of ground data was used for both classification results for accuracy assessment. Using the object-oriented classification, the overall accuracy was higher than the accuracy obtained using the pixel-based classification by 36.77%, and the user’s and producer’s accuracy of almost all the classes were also improved. In particular, the accuracy of (potential) surface coal fire areas mapping showed a marked increase. The potential surface coal fire areas were defined as areas covered by coal piles and coal wastes (dust), which are prone to be on fire, and in this context, indicated by the two land-cover types ‘coal’ and ‘coal dust’. Taking into account the same test sites utilized, McNemar’s test was used to evaluate the statistical significance of the difference between the two methods. The differences in accuracy expressed in terms of proportions of correctly allocated pixels were statistically significant at the 0.1% level, which means that the thematic mapping result using object-oriented image analysis approach gave a much higher accuracy than that obtained using the pixel-based approach. Copyright Taylor & Francis Numéro de notice : A2006-461 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600702632 En ligne : https://doi.org/10.1080/01431160600702632 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28185 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-06101	RAB	Revue	Centre de documentation	En réserve L003	Disponible