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Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization / Zhangyang Wang in IEEE Transactions on geoscience and remote sensing, vol 53 n° 3 (March 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 3 (March 2015) . - pp 1161 - 1173 Titre : Semisupervised hyperspectral classification using task-driven dictionary learning with Laplacian regularization Type de document : Article/Communication Auteurs : Zhangyang Wang, Auteur ; Nasser M. Nasrabadi, Auteur ; Thomas S. Huang, Auteur Année de publication : 2015 Article en page(s) : pp 1161 - 1173 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage semi-dirigé [Termes IGN] classification pixellaire [Termes IGN] classification semi-dirigée [Termes IGN] image hyperspectrale Résumé : (Auteur) We present a semisupervised method for single-pixel classification of hyperspectral images. The proposed method is designed to address the special problematic characteristics of hyperspectral images, namely, high dimensionality of hyperspectral pixels, lack of labeled samples, and spatial variability of spectral signatures. To alleviate these problems, the proposed method features the following components. First, being a semisupervised approach, it exploits the wealth of unlabeled samples in the image by evaluating the confidence probability of the predicted labels, for each unlabeled sample. Second, we propose to jointly optimize the classifier parameters and the dictionary atoms by a task-driven formulation, to ensure that the learned features (sparse codes) are optimal for the trained classifier. Finally, it incorporates spatial information through adding a Laplacian smoothness regularization to the output of the classifier, rather than the sparse codes, making the spatial constraint more flexible. The proposed method is compared with a few comparable methods for classification of several popular data sets, and it produces significantly better classification results. Numéro de notice : A2015-129 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2335177 Date de publication en ligne : 30/07/2014 En ligne : https://doi.org/10.1109/TGRS.2014.2335177 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75792 [article]

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