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MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images / Ilias Grinias 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 145 - 166 Titre : MRF-based segmentation and unsupervised classification for building and road detection in peri-urban areas of high-resolution satellite images Type de document : Article/Communication Auteurs : Ilias Grinias, Auteur ; Costas Panagiotakis, Auteur ; Georgios Tziritas, Auteur Année de publication : 2016 Article en page(s) : pp 145 - 166 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] analyse de données [Termes IGN] classification non dirigée [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification par maximum de vraisemblance [Termes IGN] discrétisation [Termes IGN] données vectorielles [Termes IGN] image satellite [Termes IGN] réseau routier [Termes IGN] segmentation d'image Résumé : (Auteur) We present in this article a new method on unsupervised semantic parsing and structure recognition in peri-urban areas using satellite images. The automatic “building” and “road” detection is based on regions extracted by an unsupervised segmentation method. We propose a novel segmentation algorithm based on a Markov random field model and we give an extensive data analysis for determining relevant features for the classification problem. The novelty of the segmentation algorithm lies on the class-driven vector data quantization and clustering and the estimation of the likelihoods given the resulting clusters. We have evaluated the reachability of a good classification rate using the Random Forest method. We found that, with a limited number of features, among them some new defined in this article, we can obtain good classification performance. Our main contribution lies again on the data analysis and the estimation of likelihoods. Finally, we propose a new method for completing the road network exploiting its connectivity, and the local and global properties of the road network. Numéro de notice : A2016--024 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2016.10.010 En ligne : http://dx.doi.org/10.1016/j.isprsjprs.2016.10.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=83887 [article]