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Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion / Renlong Hang in IEEE Transactions on geoscience and remote sensing, vol 54 n° 2 (February 2016)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 54 n° 2 (February 2016) . - pp 783 - 794 Titre : Matrix-based discriminant subspace ensemble for hyperspectral image spatial–spectral feature fusion Type de document : Article/Communication Auteurs : Renlong Hang, Auteur ; Qingshan Liu, Auteur ; Huihui Song, Auteur ; Yubao Sun, Auteur Année de publication : 2016 Article en page(s) : pp 783 - 794 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse discriminante [Termes IGN] classification multibande [Termes IGN] extraction de traits caractéristiques [Termes IGN] fusion de données [Termes IGN] image hyperspectrale [Termes IGN] matrice Résumé : (Auteur) Spatial-spectral feature fusion is well acknowledged as an effective method for hyperspectral (HS) image classification. Many previous studies have been devoted to this subject. However, these methods often regard the spatial-spectral high-dimensional data as 1-D vector and then extract informative features for classification. In this paper, we propose a new HS image classification method. Specifically, matrix-based spatial-spectral feature representation is designed for each pixel to capture the local spatial contextual and the spectral information of all the bands, which can well preserve the spatial-spectral correlation. Then, matrix-based discriminant analysis is adopted to learn the discriminative feature subspace for classification. To further improve the performance of discriminative subspace, a random sampling technique is used to produce a subspace ensemble for final HS image classification. Experiments are conducted on three HS remote sensing data sets acquired by different sensors, and experimental results demonstrate the efficiency of the proposed method. Numéro de notice : A2016-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2015.2465899 En ligne : http://dx.doi.org/10.1109/TGRS.2015.2465899 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=79996 [article]

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