Centre de documentation
scientifique

Classified road detection from satellite images based on perceptual organization / J. Yang in International Journal of Remote Sensing IJRS, vol 28 n°19-20 (October 2007)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 28 n°19-20 (October 2007) . - pp 4653 - 4669 Titre : Classified road detection from satellite images based on perceptual organization Type de document : Article/Communication Auteurs : J. Yang, Auteur ; R.S. Wang, Auteur Année de publication : 2007 Article en page(s) : pp 4653 - 4669 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] algorithme génétique [Termes IGN] axe médian [Termes IGN] classification automatique [Termes IGN] extraction du réseau routier [Termes IGN] image satellite [Termes IGN] lissage de courbe [Termes IGN] méthode heuristique [Termes IGN] objet géographique [Termes IGN] primitive géométrique Résumé : (Auteur) Extracting roads from satellite images is an important task in both research and practice. This work presents an improved model for road detection based on the principles of perceptual organization and classification fusion in human vision system (HVS). The model consists of four levels: pixels, primitives, structures and objects, and two additional sub-processes: automatic classification of road scenes and global integration of multiform roads. Based on the model, a novel algorithm for detecting roads from satellite images is also proposed, in which two types of road primitives, namely blob-like primitive and line-like primitive are defined, measured, extracted and linked using different methods for dissimilar road scenes. A hierarchical search strategy driven by saliency measurement is adopted in both linking processes. The blob primitives are linked using heuristic grouping and the line primitives are connected through genetic algorithm (GA) evolution. Finally, all of the linked road segments are normalized with centre-main lines and integrated into global smooth road curves through tensor voting. Experimental results show that the algorithm is capable of detecting multiform roads from real satellite images with high adaptability and reliability. Copyright Taylor & Francis Numéro de notice : A2007-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160701250382 En ligne : https://doi.org/10.1080/01431160701250382 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28819 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
080-07111	RAB	Revue	Centre de documentation	En réserve L003	Disponible