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Automatically conflating road vector data with orthoimagery / C.C. Chen in Geoinformatica, vol 10 n° 4 (December 2006)  

Public [article]  inGeoinformatica > vol 10 n° 4 (December 2006) . - pp 495 - 530 Titre : Automatically conflating road vector data with orthoimagery Type de document : Article/Communication Auteurs : C.C. Chen, Auteur ; Craig A. Knoblock, Auteur ; C. Shabani, Auteur Année de publication : 2006 Article en page(s) : pp 495 - 530 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] appariement de modèles conceptuels de données [Termes IGN] automatisation [Termes IGN] Californie (Etats-Unis) [Termes IGN] cohérence des données [Termes IGN] conflation [Termes IGN] données multisources [Termes IGN] données vectorielles [Termes IGN] fusion de données multisource [Termes IGN] intégration de données [Termes IGN] Montana (Etats-Unis) [Termes IGN] orthoimage [Termes IGN] point d'appui [Termes IGN] réseau routier Résumé : (Auteur) Recent growth of the geospatial information on the web has made it possible to easily access a wide variety of spatial data. The ability to combine various sets of geospatial data into a single composite dataset has been one of central issues of modern geographic information processing. By conflating diverse spatial datasets, one can support a rich set of queries that could have not been answered given any of these sets in isolation. However, automatically conflating geospatial data from different data sources remains a challenging task. This is because geospatial data obtained from various data sources may have different projections, different accuracy levels and different formats (e.g., raster or vector format), thus resulting in various positional inconsistencies. Most of the existing algorithms only deal with vector to vector data conflation or require human intervention to accomplish vector data to imagery conflation. In this paper, we describe a novel geospatial data fusion approach, named AMS-Conflation, which achieves automatic vector to imagery conflation. We describe an efficient technique to automatically generate control point pairs from the orthoimagery and vector data by exploiting the information from the vector data to perform localized image processing on the orthoimagery. We also evaluate a filtering technique to automatically eliminate inaccurate pairs from the generated control points. We show that these conflation techniques can automatically align the roads in orthoimagery, such that 75% of the conflated roads are within 3.6 meters from the real road axes compared to 35% for the original vector data for partial areas of the county of St. Louis, MO. Copyright Springer Numéro de notice : A2006-549 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s10707-006-0344-6 En ligne : https://doi.org/10.1007/s10707-006-0344-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28272 [article]

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