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Auteur M. Huang |
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A knowledge-based approach to urban feature classification using aerial imagery with Lidar data / M. Huang in Photogrammetric Engineering & Remote Sensing, PERS, vol 74 n° 12 (December 2008)
[article]
Titre : A knowledge-based approach to urban feature classification using aerial imagery with Lidar data Type de document : Article/Communication Auteurs : M. Huang, Auteur ; S. Shyue, Auteur ; L.H. Lee, Auteur ; et al., Auteur Année de publication : 2008 Article en page(s) : pp 1473 - 1485 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classification à base de connaissances
[Termes IGN] classification par maximum de vraisemblance
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image aérienne
[Termes IGN] image multibande
[Termes IGN] image RVB
[Termes IGN] milieu urbain
[Termes IGN] occupation du sol
[Termes IGN] seuillage d'imageRésumé : (Auteur) While the spatial resolution of remotely sensed data has improved, multispectral imagery is still not sufficient for urban classification. Problems include the difficulty in discriminating between trees and grass, the misclassification of buildings due to diverse roof compositions and shadow effects, and the misclassification of cars on roads. Recently, lidar (light detection and ranging) data have been integrated with remotely sensed data to obtain better classification results. In this study, we first conducted maximum likelihood classification (MLC) experiments, a traditional pixel-based classification method, to identify features suitable for urban classification using lidar data and aerial imagery. The addition of lidar height data improved the overall accuracy by up to 28 and 18 percent, respectively, compared to cases with only red–green–blue (RGB) and multispectral imagery. To further improve classification, we propose a knowledge-based classification system (KBCS) that includes a three-level height, “asphalt road, vegetation, and non-vegetation” (A–V–N) classification rule-based scheme and knowledge-based correction (KBC). The proposed KBCS improved overall accuracy by 12 and 7 percent compared to maximum likelihood and object-based classification, respectively. Copyright ASPRS Numéro de notice : A2008-476 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.74.12.1473 En ligne : https://doi.org/10.14358/PERS.74.12.1473 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29545
in Photogrammetric Engineering & Remote Sensing, PERS > vol 74 n° 12 (December 2008) . - pp 1473 - 1485[article]