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Supervised image classification by contextual adaboost based on posteriors in neighborhoods / Ryuei Nishii in IEEE Transactions on geoscience and remote sensing, vol 43 n° 11 (November 2005)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 43 n° 11 (November 2005) . - pp 2547 - 2554 Titre : Supervised image classification by contextual adaboost based on posteriors in neighborhoods Type de document : Article/Communication Auteurs : Ryuei Nishii, Auteur ; Shinto Eguchi, Auteur Année de publication : 2005 Conférence : IGARSS 2004, International Geoscience And Remote Sensing Symposium, Science for society: exploring and manging a changing planet 20/09/2004 24/09/2004 Anchorage Alaska - Etats-Unis Proceedings IEEE Article en page(s) : pp 2547 - 2554 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage automatique [Termes IGN] axiome de Bayes [Termes IGN] classification contextuelle [Termes IGN] classification dirigée [Termes IGN] géostatistique [Termes IGN] probabilités [Termes IGN] segmentation d'image Résumé : (Auteur) AdaBoost, a machine learning technique, is employed for supervised classification of land-cover categories of geostatistical data. We introduce contextual classifiers based on neighboring pixels. First, posterior probabilities are calculated at all pixels. Then averages of the log posteriors are calculated in different neighborhoods and are then used as contextual classification functions. Weights for the classification functions can be determined by minimizing the empirical risk with multiclass. Finally, a convex combination of classification functions is obtained. The classification is performed by a noniterative maximization procedure. The proposed method is applied to artificial multispectral images and benchmark datasets. The performance of the proposed method is excellent and similar to Markov-random-field-based classifier, which requires an iterative maximization procedure. Numéro de notice : A2005-495 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2005.848693 En ligne : https://doi.org/10.1109/TGRS.2005.848693 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=27631 [article]

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A Markov random field-based approach to decision-level fusion for remote sensing image classification / Ryuei Nishii in IEEE Transactions on geoscience and remote sensing, vol 41 n° 10 (October 2003)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 41 n° 10 (October 2003) . - pp 2316 - 2319 Titre : A Markov random field-based approach to decision-level fusion for remote sensing image classification Type de document : Article/Communication Auteurs : Ryuei Nishii, Auteur Année de publication : 2003 Article en page(s) : pp 2316 - 2319 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] champ aléatoire de Markov [Termes IGN] classification dirigée [Termes IGN] fusion d'images [Termes IGN] fusion de données [Termes IGN] segmentation d'image [Termes IGN] test de performance Résumé : (Auteur) A method is proposed for the enhancement of the quality of a classification result by fusing this result with remote sensing images, based on a Markov random field approach. The classification accuracy is estimated by a modified posterior probability, which is used for choosing the optimal classification result. The procedure is applied to a benchmark dataset for discrimination provided by the IEEE Geoscience and Remote Sensing Society Data Fusion Committee, and it shows an excellent performance. The classified result won the competition of the data fusion contest 2001 held by the same committee. Numéro de notice : A2003-354 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2003.816648 En ligne : https://doi.org/10.1109/TGRS.2003.816648 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=26434 [article]

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