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Identifying geospatial services across heterogeneous taxonomies / Anand Mehta in Geocarto international, Vol 31 n° 9 - 10 (October - November 2016)  

Public [article]  inGeocarto international > Vol 31 n° 9 - 10 (October - November 2016) . - pp 1058 - 1077 Titre : Identifying geospatial services across heterogeneous taxonomies Type de document : Article/Communication Auteurs : Anand Mehta, Auteur ; Akash Ashapure, Auteur ; Onkar Dikshit, Auteur Année de publication : 2016 Article en page(s) : pp 1058 - 1077 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web [Termes IGN] données localisées [Termes IGN] identification automatique [Termes IGN] informatique [Termes IGN] service fondé sur la position [Termes IGN] taxinomie Résumé : (auteur) Geospatial services with different functions are assembled together to solve complex problems. Different taxonomies are developed to categorize these services into classes. As differences in granularity and semantics exist among these taxonomies, the identification of services across different taxonomies has become a challenge. In this paper, an approach to identify geospatial services across heterogeneous taxonomies is proposed. Using formal concept analysis, existing heterogeneous taxonomies are decomposed into semantic factors and their various combinations. With these semantic factors, a super taxonomy is established to integrate the original heterogeneous taxonomies. Finally, with the super taxonomy as a cross-referencing system, geospatial services with classes in original taxonomies are identifiable across taxonomies. Experiments in service registries and a social media-based spatial-temporal analysis project are presented to illustrate the effectiveness of this approach. Numéro de notice : A2016-674 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1110208 Date de publication en ligne : 02/12/2015 En ligne : http://dx.doi.org/10.1080/10106049.2015.1110208 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=81924 [article]

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059-2016051	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Comparative study on projected clustering methods for hyperspectral imagery classification / Anand Mehta in Geocarto international, vol 31 n° 3 - 4 (March - April 2016)  

Public [article]  inGeocarto international > vol 31 n° 3 - 4 (March - April 2016) . - pp 296 - 307 Titre : Comparative study on projected clustering methods for hyperspectral imagery classification Type de document : Article/Communication Auteurs : Anand Mehta, Auteur ; Onkar Dikshit, Auteur Année de publication : 2016 Article en page(s) : pp 296 - 307 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification semi-dirigée [Termes IGN] image hyperspectrale [Termes IGN] Orclus Résumé : (Auteur) In this study, projected clustering is introduced to hyperspectral imagery for unsupervised classification. The main advantage of projected clustering lies in its ability to simultaneously perform feature selection and clustering. This framework also allows selection of different sets of dimensions (features/bands) for different clusters. This framework provides an effective way to address the issues associated with the high dimensionality of the data. Experiments are conducted on both synthetic and real hyperspectral imagery. For this purpose, projected clustering algorithms are implemented and compared with k-means and k-means preceded by principal component analysis. Preliminary analyses of studied algorithms on synthetic hyperspectral imagery demonstrate good results. For real hyperspectral imagery, only ORCLUS is able to produce acceptable results as compared to other unsupervised methods. The main concern lies with identification of right parameter settings. More experiments are required in this direction. Numéro de notice : A2016-152 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2015.1047416 Date de publication en ligne : 26/05/2015 En ligne : http://www.tandfonline.com/doi/full/10.1080/10106049.2015.1047416 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=80385 [article]

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Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2016021	RAB	Revue	Centre de documentation	En réserve L003	Disponible