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Incorporating domain knowledge and spatial relationships into land cover classifications: a rule-based approach / A.E. Daniels in International Journal of Remote Sensing IJRS, vol 27 n°12-13-14 (July 2006)  

Public [article]  inInternational Journal of Remote Sensing IJRS > vol 27 n°12-13-14 (July 2006) . - pp 2949 - 2975 Titre : Incorporating domain knowledge and spatial relationships into land cover classifications: a rule-based approach Type de document : Article/Communication Auteurs : A.E. Daniels, Auteur Année de publication : 2006 Article en page(s) : pp 2949 - 2975 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] classe d'objets [Termes IGN] classification à base de connaissances [Termes IGN] classification par maximum de vraisemblance [Termes IGN] données auxiliaires [Termes IGN] feuillu [Termes IGN] forêt tropicale [Termes IGN] interprétation automatique [Termes IGN] occupation du sol [Termes IGN] précision de la classification Résumé : (Auteur) For some tropical regions, remote sensing of land cover yields unacceptable results, particularly as the number of land cover classes increases. This research explores the utility of incorporating domain knowledge and multiple algorithms into land cover classifications via a rule-based algorithm for a series of satellite images. The proposed technique integrates the fundamental, knowledge-based interpretation elements of remote sensing without sacrificing the ease and consistency of automated, algorithm-based processing. Compared with results from a traditional maximum likelihood algorithm, classification accuracy was improved substantially for each of the six land cover classes and all three years in the image series. Use of domain knowledge proved effective in accurately classifying problematic tropical land covers, such as tropical deciduous forest and seasonal wetlands. Results also suggest that ancillary data may be most useful in the classification of historic images, where the greatest improvement was observed relative to results from maximum likelihood. The cost of incorporating contextual knowledge and extensive spatial data sets may be justified, since results from the proposed technique suggest a considerable improvement in accuracy may be achieved. Copyright Taylor & Francis Numéro de notice : A2006-310 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431160600567753 En ligne : https://doi.org/10.1080/01431160600567753 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28034 [article]

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