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Auteur Z. He |
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Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features / Z. He in IEEE Transactions on geoscience and remote sensing, vol 52 n° 8 Tome 2 (August 2014)
[article]
Titre : Kernel sparse multitask learning for hyperspectral image classification with empirical mode decomposition and morphological wavelet-based features Type de document : Article/Communication Auteurs : Z. He, Auteur ; Qiang Wang, Auteur ; Y. Shen, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 5150 -5163 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] classificateur
[Termes IGN] décomposition en fonctions orthogonales empiriques
[Termes IGN] image hyperspectrale
[Termes IGN] pouvoir de résolution géométrique
[Termes IGN] pouvoir de résolution spectrale
[Termes IGN] précision de la classification
[Termes IGN] transformation en ondelettesRésumé : (Auteur) Recently, many researchers have attempted to exploit spectral-spatial features and sparsity-based hyperspectral image classifiers for higher classification accuracy. However, challenges remain for efficient spectral-spatial feature generation and combination in the sparsity-based classifiers. This paper utilizes the empirical mode decomposition (EMD) and morphological wavelet transform (MWT) to gain spectral-spatial features, which can be significantly integrated by the sparse multitask learning (MTL). In the feature extraction step, the sum of the intrinsic mode functions extracted by an optimized EMD is taken as spectral features, whereas the spatial features are formed by the low-frequency components of one-level MWT. In the classification step, a kernel-based sparse MTL solved by the accelerated proximal gradient is applied to analyze both the spectral and spatial features simultaneously. Experiments are conducted on two benchmark data sets with different spectral and spatial resolutions. It is found that the proposed methods provide more accurate classification results compared to the state-of-the-art techniques with various ratio of training samples. Numéro de notice : A2014-436 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2013.2287022 En ligne : https://doi.org/10.1109/TGRS.2013.2287022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=73973
in IEEE Transactions on geoscience and remote sensing > vol 52 n° 8 Tome 2 (August 2014) . - pp 5150 -5163[article]Réservation
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Titre : Detecting water bodies on Radarsat imagery Type de document : Article/Communication Auteurs : G. Kuang, Auteur ; J. Li, Auteur ; Z. He, Auteur Année de publication : 2011 Article en page(s) : pp 15 - 25 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] détection de contours
[Termes IGN] image radar moirée
[Termes IGN] image Radarsat
[Termes IGN] plan d'eau
[Termes IGN] précision des donnéesRésumé : (Auteur) This paper presents a novel geodesic active contour (GAC) model based on an edge detector for rapid detection of water bodies from spaceborne synthetic aperture radar (SAR) imagery with high speckle noise. The original edge indicator function based on gradients is replaced by an edge indicator function based on the ratio of exponentially weighted averages (ROEWA) operator. Thus, the capability of edge detection and the accuracy of locating edges are greatly improved, which makes the model more appropriate for SAR images. In addition, an enhancing term is added to the original model’s energy function in order to boost the strength for the contour’s evolution. An unconditionally stable additive operator splitting (AOS) scheme and a fast algorithm for re-initialization of the level set function are adopted, which not only enhances the model’s stability, but also speeds up the model’s convergence remarkably. The experimental results on simulated and real RADARSAT-1/-2 images show its efficiency and accuracy. Numéro de notice : A2011-537 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5623/cig2011-005 En ligne : https://cdnsciencepub.com/doi/pdf/10.5623/cig2011-005 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=31431
in Geomatica > vol 65 n° 1 (March 2011) . - pp 15 - 25[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 035-2011011 RAB Revue Centre de documentation En réserve L003 Disponible