Centre de documentation
scientifique

Fast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty / Xuellang Zhang in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 1 (January 2014)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 80 n° 1 (January 2014) . - pp 71 - 80 Titre : Fast hierarchical segmentation of high-resolution remote sensing images with adaptative edge penalty Type de document : Article/Communication Auteurs : Xuellang Zhang, Auteur ; Pending Xiao, Auteur ; Xuezhi Feng, Auteur Année de publication : 2014 Article en page(s) : pp 71 - 80 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] image à haute résolution [Termes IGN] image aérienne [Termes IGN] image Quickbird [Termes IGN] image Worldview [Termes IGN] segmentation d'image [Termes IGN] segmentation hiérarchique [Termes IGN] segmentation multi-échelle Résumé : (Auteur) A fast hierarchical segmentation method (FHS) for high-resolution remote sensing (HR) image is proposed in the paper. FHS is completely unsupervised. It is characterized by two aspects. First, the hierarchical segmentation process is accelerated by the improved linear nearest neighbor graph (LNNG) model and the segment tree model. It runs faster than other existing hierarchical segmentation methods, and can produce multi-resolution segmentations in time linear to the image size. Second, an adaptive edge penalty function is introduced to formulate the merging criterion, serving as a semantic factor. A set of QuickBird, WorldView, and aerial images is used to test the proposed method. The experiments show that the multi-resolution segmentations produced by FHS can represent objects at different scales very well. Moreover, the adaptive edge penalty function helps to remove meaningless weak edges within objects, enclosing the relation between segments and real-world objects. Numéro de notice : A2014-093 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.80.1.71 En ligne : https://doi.org/10.14358/PERS.80.1.71 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32998 [article]