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Object-based superresolution land-cover mapping from remotely sensed imagery / Yuehong Chen in IEEE Transactions on geoscience and remote sensing, vol 56 n° 1 (January 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 1 (January 2018) . - pp 328 - 340 Titre : Object-based superresolution land-cover mapping from remotely sensed imagery Type de document : Article/Communication Auteurs : Yuehong Chen, Auteur ; Yong Ge, Auteur ; Gerard B.M. Heuvelink, Auteur ; Ru An, Auteur ; Yu Chen, Auteur Année de publication : 2018 Article en page(s) : pp 328 - 340 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification orientée objet [Termes IGN] classification pixellaire [Termes IGN] déconvolution [Termes IGN] krigeage [Termes IGN] occupation du sol [Termes IGN] programmation linéaire [Termes IGN] variogramme Résumé : (Auteur) Superresolution mapping (SRM) is a widely used technique to address the mixed pixel problem in pixel-based classification. Advanced object-based classification will face a similar mixed phenomenon-a mixed object that contains different land-cover classes. Currently, most SRM approaches focus on estimating the spatial location of classes within mixed pixels in pixel-based classification. Little if any consideration has been given to predicting where classes spatially distribute within mixed objects. This paper, therefore, proposes a new object-based SRM strategy (OSRM) to deal with mixed objects in object-based classification. First, it uses the deconvolution technique to estimate the semivariograms at target subpixel scale from the class proportions of irregular objects. Then, an area-to-point kriging method is applied to predict the soft class values of subpixels within each object according to the estimated semivariograms and the class proportions of objects. Finally, a linear optimization model at object level is built to determine the optimal class labels of subpixels within each object. Two synthetic images and a real remote sensing image were used to evaluate the performance of OSRM. The experimental results demonstrated that OSRM generated more land-cover details within mixed objects than did the traditional object-based hard classification and performed better than an existing pixel-based SRM method. Hence, OSRM provides a valuable solution to mixed objects in object-based classification. Numéro de notice : A2018-186 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2747624 Date de publication en ligne : 20/09/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2747624 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89843 [article]