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Extracting building patterns with multilevel graph partition and building grouping / Shihong Du in ISPRS Journal of photogrammetry and remote sensing, vol 122 (December 2016)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 122 (December 2016) . - pp 81 – 96 Titre : Extracting building patterns with multilevel graph partition and building grouping Type de document : Article/Communication Auteurs : Shihong Du, Auteur ; Liqun Luo, Auteur ; Kai Cao, Auteur ; Mi Shu, Auteur Année de publication : 2016 Article en page(s) : pp 81 – 96 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes descripteurs IGN] extraction automatique [Termes descripteurs IGN] figure géométrique [Termes descripteurs IGN] graphe [Termes descripteurs IGN] partition des données [Termes descripteurs IGN] paysage urbain [Termes descripteurs IGN] reconstruction 2D du bâti [Termes descripteurs IGN] reconstruction automatique Résumé : (Auteur) Building patterns are crucial for urban landscape evaluation, social analyses and multiscale spatial data automatic production. Although many studies have been conducted, there is still lack of satisfying results due to the incomplete typology of building patterns and the ineffective extraction methods. This study aims at providing a typology with four types of building patterns (e.g., collinear patterns, curvilinear patterns, parallel and perpendicular groups, and grid patterns) and presenting four integrated strategies for extracting these patterns effectively and efficiently. First, the multilevel graph partition method is utilized to generate globally optimal building clusters considering area, shape and visual distance similarities. In this step, the weights of similarity measurements are automatically estimated using Relief-F algorithm instead of manual selection, thus building clusters with high quality can be obtained. Second, based on the clusters produced in the first step, the extraction strategies group the buildings from each cluster into patterns according to the criteria of proximity, continuity and directionality. The proposed methods are tested using three datasets. The experimental results indicate that the proposed methods can produce satisfying results, and demonstrate that the F-Histogram model is better than the two widely used models (i.e., centroid model and the Voronoi graph) to represent relative directions for building patterns extraction. Numéro de notice : A2016--022 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.10.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83885 [article]