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Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data / Razieh Shojanoori in Geocarto international, vol 33 n° 4 (April 2018)  

Public [article]  inGeocarto international > vol 33 n° 4 (April 2018) . - pp 357 - 374 Titre : Generic rule-sets for automated detection of urban tree species from very high-resolution satellite data Type de document : Article/Communication Auteurs : Razieh Shojanoori, Auteur ; Helmi Zulhaidi Mohd Shafri, Auteur ; Shattri Bin Mansor, Auteur ; et al., Auteur Année de publication : 2018 Article en page(s) : pp 357 - 374 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse d'image orientée objet [Termes IGN] arbre (flore) [Termes IGN] arbre urbain [Termes IGN] base de règles [Termes IGN] détection d'arbres [Termes IGN] image Worldview [Termes IGN] Malaisie [Termes IGN] traitement d'image [Termes IGN] zone urbaine Résumé : (Auteur) The sustainable management and monitoring of urban forests is an important activity in the urbanized world, and operational approaches require information about the status of urban trees to determine the best strategy. One limitation in urban forest studies is the detection and discrimination of tree species using limited training data. Thus, this study focuses on developing generic rule sets from high-resolution WorldView-2 imagery in conjunction with spectral, spatial, colour and textural information for automated urban tree species detection. The object-based image analysis and its combination with statistical analysis of object features is utilized for this purpose. Results of attribute selection indicated that from 55 attributes, only 26 were useful to discriminate urban tree species, namely Messua ferrea L., Samanea saman and Casuarina sumatrana. Finally, the high overall accuracy, approximately 86.87% with kappa of 0.75 confirmed the transferability of the generic model. Numéro de notice : A2018-046 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2016.1265593 En ligne : https://doi.org/10.1080/10106049.2016.1265593 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89268 [article]

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