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A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships / Ick-Hoi Kim in International journal of geographical information science IJGIS, vol 31 n° 5-6 (May-June 2017)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 31 n° 5-6 (May-June 2017) . - pp 1042 - 1060 Titre : A simplified linear feature matching method using decision tree analysis, weighted linear directional mean, and topological relationships Type de document : Article/Communication Auteurs : Ick-Hoi Kim, Auteur ; Chen-Chieh Feng, Auteur ; Yi-Chen Wang, Auteur Année de publication : 2017 Article en page(s) : pp 1042 - 1060 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] appariement de données localisées [Termes IGN] axe médian [Termes IGN] base de données historiques [Termes IGN] classification par arbre de décision [Termes IGN] conflation [Termes IGN] distance de Hausdorff [Termes IGN] données anciennes [Termes IGN] objet géographique linéaire [Termes IGN] relation topologique [Termes IGN] réseau routier [Termes IGN] similitude [Termes IGN] valeur moyenne Résumé : (auteur) Linear feature matching is one of the crucial components for data conflation that sees its usefulness in updating existing data through the integration of newer data and in evaluating data accuracy. This article presents a simplified linear feature matching method to conflate historical and current road data. To measure the similarity, the shorter line median Hausdorff distance (SMHD), the absolute value of cosine similarity (aCS) of the weighted linear directional mean values, and topological relationships are adopted. The decision tree analysis is employed to derive thresholds for the SMHD and the aCS. To demonstrate the usefulness of the simple linear feature matching method, four models with incremental configurations are designed and tested: (1) Model 1: one-to-one matching based on the SMHD; (2) Model 2: matching with only the SMHD threshold; (3) Model 3: matching with the SMHD and the aCS thresholds; and (4) Model 4: matching with the SMHD, the aCS, and topological relationships. These experiments suggest that Model 2, which considers only distance, does not provide stable results, while Models 3 and 4, which consider direction and topological relationships, produce stable results with levels of accuracy around 90% and 95%, respectively. The results suggest that the proposed method is simple yet robust for linear feature matching. Numéro de notice : A2017-241 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2016.1267736 En ligne : http://dx.doi.org/10.1080/13658816.2016.1267736 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85177 [article]

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