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Bi-temporal texton forest for land cover transition detection on remotely sensed imagery / Zhen Lei in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 52 n° 2 (February 2014) . - pp 1227 - 1237 Titre : Bi-temporal texton forest for land cover transition detection on remotely sensed imagery Type de document : Article/Communication Auteurs : Zhen Lei, Auteur ; Tao Fang, Auteur ; Hong Huo, Auteur ; Deren Li, Auteur Année de publication : 2014 Article en page(s) : pp 1227 - 1237 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse diachronique [Termes IGN] arbre de décision [Termes IGN] détection de changement [Termes IGN] gradient [Termes IGN] occupation du sol Résumé : (Auteur) With the advancement of machine learning, classification methods have been increasingly used in change (or transition) detection. The texton forest (TF)-based method has received increasing research attention because of its speed, good generalization characteristics, stability, and especially its ability to capture spatial contextual information. In this paper, we propose a TF-based method for transition detection in remotely sensed imagery. We investigate a maximal joint-information gain criterion for random forests to better capture combined information in the bi-temporal images in transition detection, which is implemented by a natural extension of binary-trees in traditional methods into a quad-decision tree structure. We also utilize color-invariant gradient as a feature to help alleviate the impact of difference in imaging conditions on bi-temporal transition detection. The experimental results for transition detection show that our bi-temporal TF classifier achieves better performance than a post-classification comparison method and several other alternative methods. Numéro de notice : A2014-075 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2248738 En ligne : https://doi.org/10.1109/TGRS.2013.2248738 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32980 [article]

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