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Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation / L. Wang in ISPRS Journal of photogrammetry and remote sensing, vol 97 (November 2014)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 97 (November 2014) . - pp 123 – 137 Titre : Semi-supervised classification for hyperspectral imagery based on spatial-spectral Label Propagation Type de document : Article/Communication Auteurs : L. Wang, Auteur ; Siyuan Hao, Auteur ; Q. Wang, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 123 – 137 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification semi-dirigée [Termes IGN] filtre de Gabor [Termes IGN] graphe [Termes IGN] image hyperspectrale Résumé : (Auteur) Graph-based classification algorithms have gained increasing attention in semi-supervised classification. Nevertheless, the graph cannot fully represent the inherent spatial distribution of the data. In this paper, a new classification methodology based on the spatial-spectral Label Propagation is proposed for semi-supervised classification of hyperspectral imagery. The spatial information was used in two aspects: on the one hand, the spatial features extracted by a 2-D Gabor filter were stacked with spectral features; on the other hand, the width of the Gaussian function, which was used to construct graph, was determined with an adaptive method. Subsequently, the unlabeled samples from the spatial neighbors of the labeled samples were selected and the spatial graph was constructed based on spatial smoothness. Finally, labels were propagated from labeled samples to unlabeled samples with spatial-spectral graph to update the training set for a basic classifier (e.g., Support Vector Machine, SVM). Experiments on four hyperspectral datasets show that the proposed Spatial-Spectral Label Propagation based on the SVM (SS-LPSVM) can effectively represent the spatial information in the framework of semi-supervised learning and consistently produces greater classification accuracy than the standard SVM, the Laplacian Support Vector Machine (LapSVM), Transductive Support Vector Machine (TSVM) and the Spatial-Contextual Semi-Supervised Support Vector Machine (SCS3VM). Numéro de notice : A2014-527 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2014.08.016 En ligne : https://doi.org/10.1016/j.isprsjprs.2014.08.016 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=74140 [article]

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