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An assessment of geometric activity features for per-pixel classification of urban man-made objects using Very High Resolution satellite Imagery / J. Chan in Photogrammetric Engineering & Remote Sensing, PERS, vol 75 n° 4 (April 2009)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 75 n° 4 (April 2009) . - pp 397 - 411 Titre : An assessment of geometric activity features for per-pixel classification of urban man-made objects using Very High Resolution satellite Imagery Type de document : Article/Communication Auteurs : J. Chan, Auteur ; R. Bellens, Auteur ; et al., Auteur Année de publication : 2009 Article en page(s) : pp 397 - 411 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classification pixellaire [Termes IGN] détail topographique artificiel [Termes IGN] eCognition [Termes IGN] image à très haute résolution [Termes IGN] milieu urbain [Termes IGN] objet géographique urbain [Termes IGN] précision de la classification [Termes IGN] traitement géométrique de données Résumé : (Auteur) In this paper, we propose the use of Geometric Activity (GA) features for detecting man-made objects in urban areas using VHR satellite imagery. These features describe the geometric context of a pixel without the necessity of segmentation and can be integrated as extra bands in a per-pixel classification. Two main types of GA features were investigated: ridge features based on the well-known facet model and morphological features obtained by applying closing transforms with structuring elements of different size and shape. Our findings show a substantial increase in classification accuracy for the man-made object classes “roads and buildings with dark roof” after inclusion of GA features. Next to GA features, the use of object-based features derived from eCognition®, containing both geometric and textural information, was also investigated for per-pixel classification. Accuracies obtained with object-based features are comparable to the accuracies obtained with GA features. The inclusion of both GA features and object-based features further improves the overall accuracy. GA features and object-based features thus contain complementary information. Copyright ASPRS Numéro de notice : A2009-106 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.75.4.397 En ligne : https://doi.org/10.14358/PERS.75.4.397 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29736 [article]