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Superpixel-based graphical model for remote sensing image mapping / Guangyun Zhang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 11 (November 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 11 (November 2015) . - pp 5861 - 5871 Titre : Superpixel-based graphical model for remote sensing image mapping Type de document : Article/Communication Auteurs : Guangyun Zhang, Auteur ; Xiuping Jia, Auteur ; Jiankun Hu, Auteur Année de publication : 2015 Article en page(s) : pp 5861 - 5871 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] classification contextuelle [Termes IGN] classification pixellaire [Termes IGN] décomposition du pixel [Termes IGN] image multibande [Termes IGN] modèle sémantique de données [Termes IGN] segmentation d'image Résumé : (Auteur) Object-oriented remote sensing image classification is becoming more and more popular because it can integrate spatial information from neighboring regions of different shapes and sizes into the classification procedure to improve the mapping accuracy. However, object identification itself is difficult and challenging. Superpixels, which are groups of spatially connected similar pixels, have the scale between the pixel level and the object level and can be generated from oversegmentation. In this paper, we establish a new classification framework using a superpixel-based graphical model. Superpixels instead of pixels are applied as the basic unit to the graphical model to capture the contextual information and the spatial dependence between the superpixels. The advantage of this treatment is that it makes the classification less sensitive to noise and segmentation scale. The contribution of this paper is the application of a graphical model to remote sensing image semantic segmentation. It is threefold. 1) Gradient fusion is applied to multispectral images before the watershed segmentation algorithm is used for superpixel generation. 2) A probabilistic fusion method is designed to derive node potential in the superpixel-based graphical model to address the problem of insufficient training samples at the superpixel level. 3) A boundary penalty between the superpixels is introduced in the edge potential evaluation. Experiments on three real data sets were conducted. The results show that the proposed method performs better than the related state-of-the-art methods tested. Numéro de notice : A2015-770 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2423688 Date de publication en ligne : 08/06/2015 En ligne : https://doi.org/10.1109/TGRS.2015.2423688 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=78826 [article]

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