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A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 10 (October 2003) . - pp 2354 - 2363 Titre : A combined fuzzy pixel-based and object-based approach for classification of high-resolution multispectral data over urban areas Type de document : Article/Communication Auteurs : A.K. Shackelford, Auteur ; C.H. Davis, Auteur Année de publication : 2003 Article en page(s) : pp 2354 - 2363 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification floue [Termes IGN] classification orientée objet [Termes IGN] classification pixellaire [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] logique floue [Termes IGN] milieu urbain [Termes IGN] segmentation d'image Résumé : (Auteur) In this paper, we present an object-based approach for urban land cover classification from high-resolution multispectral image data that builds upon a pixel-based fuzzy classification approach. This combined pixel / object approach is demonstrated using pan-sharpened multispectral IKONOS imagery from dense urban areas. The fuzzy pixel-based classifier utilises both spectral and spatial information to discriminate between spectrally similar Road and Building urban land cover classes. After the pixel-based classification, a technique that utilises both spectral and spatial heterogeneity is used to segment the image to facilitate further object-based classification. An object-based fuzzy logic classifier is then implemented to improve upon the pixel-based classification by identifying one additional class in dense urban areas: non-road, non-building impervious surface. With the fuzzy pixel-based classification as input, the object-based classifier then uses shape, spectral. and neighbourhood features to determine the final classification of the segmented image. Using these techniques, the object-based classifier is able to identify Buildings, Impervious Surface, and Roads in dense urban areas with 76%, 81%, and 99% classification accuracies, respectively. Numéro de notice : A2003-357 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.815972 En ligne : https://doi.org/10.1109/TGRS.2003.815972 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26437 [article]

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A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas / A.K. Shackelford in IEEE Transactions on geoscience and remote sensing, vol 41 n° 9 (September 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 9 (September 2003) . - pp 1920 - 1932 Titre : A hierarchical fuzzy classification approach for high-resolution multispectral data over urban areas Type de document : Article/Communication Auteurs : A.K. Shackelford, Auteur ; C.H. Davis, Auteur Année de publication : 2003 Article en page(s) : pp 1920 - 1932 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse comparative [Termes IGN] classification floue [Termes IGN] classification par maximum de vraisemblance [Termes IGN] image à résolution métrique [Termes IGN] image Ikonos [Termes IGN] image multibande [Termes IGN] image panchromatique [Termes IGN] milieu urbain [Termes IGN] texture d'image Résumé : (Auteur) In this paper, we investigate the usefulness of high-resolution multispectral satellite imagery for classification of urban and suburban areas and present a fuzzy logic methodology to improve classification accuracy. Panchromatic and multispectral IKONOS image datasets are analyzed for two urban locations in this study. Both multispectral and pansharpened multispectral images are first classified using a traditional maximum-likelihood approach. Maximum-likelihood classification accuracies between 79 % to 87 % were achieved with significant misclassification error between the spectrally similar Road and Building urban land cover types. A number of different texture measures were investigated, and a length-width contextual measure is developed. These spatial measures were used to increase the discrimination between spectrally similar classes, thereby yielding higher accuracy urban land cover maps. Finally, a hierarchical fuzzy classification approach that makes use of both spectral and spatial information is presented. This technique is shown to increase the discrimination between spectrally similar urban land cover classes and results in classification accuracies that are 8 % to I I% larger than those from the traditional maximumlikelihood approach. Numéro de notice : A2003-249 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.814627 En ligne : https://doi.org/10.1109/TGRS.2003.814627 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=22544 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-03091	RAB	Revue	Centre de documentation	En réserve L003	Disponible