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Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images / S. Al-Kheder in International journal of geographical information science IJGIS, vol 22 n°11-12 (november 2008)  

Public [article]  inInternational journal of geographical information science IJGIS > vol 22 n°11-12 (november 2008) . - pp 1271 - 1293 Titre : Fuzzy inference guided cellular automata urban-growth modelling using multi-temporal satellite images Type de document : Article/Communication Auteurs : S. Al-Kheder, Auteur ; Jing Wang, Auteur ; J. Shan, Auteur Année de publication : 2008 Article en page(s) : pp 1271 - 1293 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] analyse diachronique [Termes IGN] automate cellulaire [Termes IGN] croissance urbaine [Termes IGN] étalonnage des données [Termes IGN] image multitemporelle [Termes IGN] image satellite [Termes IGN] Indianapolis [Termes IGN] Inférence floue [Termes IGN] modélisation spatio-temporelle [Termes IGN] moteur d'inférence [Termes IGN] urbanisation Résumé : (Auteur) This paper presents a fuzzy inference guided cellular automata approach. Semantic or linguistic knowledge on urban development is expressed as fuzzy rules, based on which fuzzy inference is applied to determine the urban development potential for each pixel. A defuzzification process converts the development potential to the required neighbourhood development level, which is taken by cellular automata as initial approximation for its transition rules. Such approximations are updated through spatial calibration over townships and temporal calibration with multi-temporal satellite images. Assessment of the modelling results is based on three evaluation measures: fitness and Type I and Type II errors. The approach is applied to model the growth of the city of Indianapolis, Indiana over a period of 30 years from 1973 to 2003. A fitness level of 100 + 20% with 30% average errors can be achieved for 80% of the townships in urban-growth prediction. Copyright Taylor & Francis Numéro de notice : A2008-403 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/13658810701617292 En ligne : https://doi.org/10.1080/13658810701617292 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=29395 [article]

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