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Domain adaptation for land use classification: A spatio-temporal knowledge reusing method / Yilun Liu in ISPRS Journal of photogrammetry and remote sensing, vol 98 (December 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 98 (December 2014) . - pp 133 - 144 Titre : Domain adaptation for land use classification: A spatio-temporal knowledge reusing method Type de document : Article/Communication Auteurs : Yilun Liu, Auteur ; Xia Li, Auteur Année de publication : 2014 Article en page(s) : pp 133 - 144 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] classificateur [Termes IGN] classification [Termes IGN] classification dirigée [Termes IGN] connaissance thématique [Termes IGN] données anciennes [Termes IGN] utilisation du sol Résumé : (Auteur) Land use classification requires a significant amount of labeled data, which may be difficult and time consuming to obtain. On the other hand, without a sufficient number of training samples, conventional classifiers are unable to produce satisfactory classification results. This paper aims to overcome this issue by proposing a new model, TrCbrBoost, which uses old domain data to successfully train a classifier for mapping the land use types of target domain when new labeled data are unavailable. TrCbrBoost adopts a fuzzy CBR (Case Based Reasoning) model to estimate the land use probabilities for the target (new) domain, which are subsequently used to estimate the classifier performance. Source (old) domain samples are used to train the classifiers of a revised TrAdaBoost algorithm in which the weight of each sample is adjusted according to the classifier’s performance. This method is tested using time-series SPOT images for land use classification. Our experimental results indicate that TrCbrBoost is more effective than traditional classification models, provided that sufficient amount of old domain data is available. Under these conditions, the proposed method is 9.19% more accurate. Numéro de notice : A2014-632 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.09.013 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.09.013 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75048 [article]

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