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Toward evaluating multiscale segmentations of high spatial resolution remote sensing images / Xueliang Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 7 (July 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 7 (July 2015) . - pp 3694 - 3706 Titre : Toward evaluating multiscale segmentations of high spatial resolution remote sensing images Type de document : Article/Communication Auteurs : Xueliang Zhang, Auteur ; Pengfeng Xiao, Auteur ; Xuezhi Feng, Auteur ; et al., Auteur Année de publication : 2015 Article en page(s) : pp 3694 - 3706 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse d'image numérique [Termes IGN] analyse d'image orientée objet [Termes IGN] analyse multicritère [Termes IGN] Chine [Termes IGN] image à haute résolution [Termes IGN] image Quickbird [Termes IGN] segmentation hiérarchique [Termes IGN] segmentation multi-échelle Résumé : (Auteur) Object-based analysis of high spatial resolution remote sensing images addresses the matter of multiscale segmentation. However, existing segmentation evaluation methods mainly focus on single-scale segmentation. In this paper, we examine the issue of supervised multiscale segmentation evaluation and propose two discrepancy measures to determine the manner in which geographic objects are delineated by multiscale segmentations. A QuickBird scene in Hangzhou, China, is used to conduct the evaluation. The results reveal the effectiveness of the proposed measures, in terms of method comparison and parameter optimization, for multiscale segmentation of high spatial resolution images. Moreover, meaningful indications for selecting suitable multiple segmentation scales are presented. The proposed measures are applicable to performance evaluation and parameter optimization for multiscale segmentation algorithms. Numéro de notice : A2015-320 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2381632 En ligne : https://doi.org/10.1109/TGRS.2014.2381632 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76573 [article]

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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]