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Increasing robustness of postclassification change detection using time series of land cover maps / Pieter Kempeneers in IEEE Transactions on geoscience and remote sensing, vol 50 n° 9 (October 2012)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 50 n° 9 (October 2012) . - pp 3327 - 3339 Titre : Increasing robustness of postclassification change detection using time series of land cover maps Type de document : Article/Communication Auteurs : Pieter Kempeneers, Auteur ; F. Sedano, Auteur ; Peter Strobl, Auteur ; et al., Auteur Année de publication : 2012 Article en page(s) : pp 3327 - 3339 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] carte d'occupation du sol [Termes IGN] détection de changement [Termes IGN] Europe (géographie politique) [Termes IGN] image Terra-MODIS [Termes IGN] incendie de forêt [Termes IGN] méthode robuste [Termes IGN] occupation du sol [Termes IGN] risque naturel [Termes IGN] série temporelle [Termes IGN] surveillance de la végétation Résumé : (Auteur) The monitoring of land cover requires that stable land cover classes be distinguished from changes over time. Within this paper, a postclassification method is presented that provides land cover change information, based on a time series of land cover maps. The method applies a kernel filter to sequential land cover maps. Under some basic assumptions, it shows robustness against classification errors. Despite seasonality, land cover changes often occur at a low temporal frequency (e.g., maximum once every 5-10 years). If land cover maps are available more frequently, some of the information will become redundant (oversampling). The proposed method uses this redundancy for tolerating (nonsystematic) misclassifications. In order to demonstrate the benefits and limitations of the proposed method, analytical expressions have been derived. When compared to a simple postclassification comparison, one of the key strengths of the proposed approach is that it is able to improve both the overall and user's accuracy of change, while also maintaining the same level of producer's accuracy. As a case study, MODerate Resolution Imaging Spectroradiometer remote sensing data from 2006-2010 were classified into forest (F)/nonforest (NF) at pan-European scale. Promising results were obtained for detecting forest loss due to natural disasters. Quality was assessed using burnt area maps in southern Europe and a forest damage report after a windstorm in France. Results indicated a considerable reduction of change detection errors, confirming the theoretical results. Numéro de notice : A2012-448 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2011.2181854 Date de publication en ligne : 21/02/2012 En ligne : https://doi.org/10.1109/TGRS.2011.2181854 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31894 [article]

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