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Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method / Xuefei Hu in Geocarto international, vol 26 n° 1 (February 2011)  

Public [article]  inGeocarto international > vol 26 n° 1 (February 2011) . - pp 3 - 20 Titre : Impervious surface area extraction from IKONOS imagery using an object-based fuzzy method Type de document : Article/Communication Auteurs : Xuefei Hu, Auteur ; Q. Weng, Auteur Année de publication : 2011 Article en page(s) : pp 3 - 20 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification floue [Termes IGN] détection d'objet [Termes IGN] extraction automatique [Termes IGN] image Ikonos [Termes IGN] image multibande [Termes IGN] Indianapolis [Termes IGN] logique floue [Termes IGN] milieu urbain [Termes IGN] pansharpening (fusion d'images) [Termes IGN] surface imperméable [Termes IGN] ville Résumé : (Auteur) The study of impervious surfaces is crucial to the sustainable development of urban areas due to its strong impact on urban environments. Remotely sensed high-resolution imagery has the advantage of providing more spatial details; however, digital image processing algorithms have not been well developed to accommodate this advantage and other characteristics of such imagery. In this article, an object-based fuzzy classification approach for impervious surface extraction was developed and applied to two pan-sharpened multi-spectral IKONOS images covering the residential and central business district (CBD) areas of Indianapolis, Indiana, USA. Fuzzy rules based on spectral, spatial and texture attributes, were developed to extract impervious surfaces. An accuracy assessment was performed for the final maps. The results indicated that the spatial patterns of extracted features were in accordance with those in the original images and the boundaries of features were appropriately delineated. Impervious surfaces were extracted with an accuracy of 95% in the residential area and 92% in the CBD area. Road extraction achieved accuracy a bit lower, with 93% and 90% from the residential and CBD area, respectively. Buildings were extracted with an accuracy of 94% from the residential area while 89% from the CBD area. It is suggested that the CBD area had a higher spectral complexity, building displacement and the shadow problem, leading to a more difficult estimation and mapping of impervious surfaces. Numéro de notice : A2011-034 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2010.535616 Date de publication en ligne : 01/10/2010 En ligne : https://doi.org/10.1080/10106049.2010.535616 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=30815 [article]

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Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery / Dong Lu in Photogrammetric Engineering & Remote Sensing, PERS, vol 70 n° 9 (September 2004)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 70 n° 9 (September 2004) . - pp 1053 - 1062 Titre : Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM+ imagery Type de document : Article/Communication Auteurs : Dong Lu, Auteur ; Q. Weng, Auteur Année de publication : 2004 Article en page(s) : pp 1053 - 1062 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse des mélanges spectraux [Termes IGN] analyse en composantes principales [Termes IGN] arbre (mathématique) [Termes IGN] classification hybride [Termes IGN] classification par maximum de vraisemblance [Termes IGN] image Landsat-ETM+ [Termes IGN] Indianapolis [Termes IGN] paysage urbain Résumé : (Auteur) This paper examines characteristics of urban land-use and land-cover (LULC) classes using spectral mixture analysis (SMA), and develops a conceptual model for characterizing urban LULC patterns. A Landsat Enhanced Thematic Mapper Plus (ETM+) image of Indianapolis City was used in this research and a minimum noise fraction (MNF) transform was employed to convert the ETM+ image into principal components. Five image endmembers (shade, green vegetation, impervious surface, dry soil, and dark soil) were selected, and an unconstrained least-squares solution was used to unmix the MNF components into fraction images. Different combinations of three or four endmembers were evaluated. The best fraction images were chosen to classify LULC classes based on a hybrid procedure that combined maximum-likelihood and decision-tree algorithms. The results indicate that the SMA-based approach significantly improved classification accuracy as compared to the maximum-likelihood classifier. The fraction images were found to be effective for characterizing the urban landscape patterns. Numéro de notice : A2004-347 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.70.9.1053 En ligne : https://doi.org/10.14358/PERS.70.9.1053 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26874 [article]