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Developing an object-based hyperspatial image classifier with a case study using WorldView-2 data / Harini Sridharan in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 11 (November 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 11 (November 2013) . - pp 1027 - 1036 Titre : Developing an object-based hyperspatial image classifier with a case study using WorldView-2 data Type de document : Article/Communication Auteurs : Harini Sridharan, Auteur ; Fang Qiu, Auteur Année de publication : 2013 Article en page(s) : pp 1027 - 1036 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement de formes [Termes IGN] classification floue [Termes IGN] Dallas (Texas) [Termes IGN] image Worldview [Termes IGN] milieu urbain Résumé : (Auteur) Recent advancements in remote sensing technology have provided a plethora of very high spatial resolution images. From pixel-based processing designed for low spatial resolution data, image processing has shifted towards object-based analysis in order to adapt to the hyperspatial nature of currently available remote sensing data. However, standard object-based classifiers work with only object-level summary statistics of the reflectance values and do not sufficiently exploit within-object reflectance pattern. In this research, a novel approach of utilizing the object-level distribution of reflectance values is presented. A fuzzy Kolmogorov-Smirnov based classifier is proposed to provide an object-to-object matching of the empirical distribution of the reflectance values of each object and derive a fuzzy membership grade to each class. This object-based classifier is tested for urban objects recognition from WorldView-2 data. Results indicate at least 10 percent increase in overall classification accuracy using the proposed classifier in comparison to various popular object- and pixel-based classifiers. Numéro de notice : A2013-597 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.11.1027 En ligne : https://doi.org/10.14358/PERS.79.11.1027 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32733 [article]