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Hyperspectral and multispectral image fusion via graph Laplacian-guided coupled tensor decomposition / Yuanyang Bu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 648 - 662 Titre : Hyperspectral and multispectral image fusion via graph Laplacian-guided coupled tensor decomposition Type de document : Article/Communication Auteurs : Yuanyang Bu, Auteur ; Yong-Qiang Zhao, Auteur ; Jize Xue, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 648 - 662 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse spectrale [Termes descripteurs IGN] calcul tensoriel [Termes descripteurs IGN] équation de Laplace [Termes descripteurs IGN] fusion d'images [Termes descripteurs IGN] graphe [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] optimisation (mathématiques) [Termes descripteurs IGN] tenseur [Termes descripteurs IGN] théorie des variétés Résumé : (auteur) We propose a novel graph Laplacian-guided coupled tensor decomposition (gLGCTD) model for fusion of hyperspectral image (HSI) and multispectral image (MSI) for spatial and spectral resolution enhancements. The coupled Tucker decomposition is employed to capture the global interdependencies across the different modes to fully exploit the intrinsic global spatial–spectral information. To preserve local characteristics, the complementary submanifold structures embedded in high-resolution (HR)-HSI are encoded by the graph Laplacian regularizations. The global spatial–spectral information captured by the coupled Tucker decomposition and the local submanifold structures are incorporated into a unified framework. The gLGCTD fusion framework is solved by a hybrid framework between the proximal alternating optimization (PAO) and the alternating direction method of multipliers (ADMM). Experimental results on both synthetic and real data sets demonstrate that the gLGCTD fusion method is superior to state-of-the-art fusion methods with a more accurate reconstruction of the HR-HSI. Numéro de notice : A2021-036 Affiliation des auteurs : non IGN Thématique : IMAGERIE/MATHEMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2992788 date de publication en ligne : 18/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2992788 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96738 [article]

Learning and transferring deep joint spectral–spatial features for hyperspectral classification / Jingxiang Yang in IEEE Transactions on geoscience and remote sensing, vol 55 n° 8 (August 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 8 (August 2017) . - pp 4729 - 4742 Titre : Learning and transferring deep joint spectral–spatial features for hyperspectral classification Type de document : Article/Communication Auteurs : Jingxiang Yang, Auteur ; Yong-Qiang Zhao, Auteur ; Jonathan Cheung-Wai Chan, Auteur Année de publication : 2017 Article en page(s) : pp 4729 - 4742 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification dirigée [Termes descripteurs IGN] classification par réseau neuronal [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] filtrage numérique d'image [Termes descripteurs IGN] image AVIRIS [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] image ROSIS [Termes descripteurs IGN] réseau neuronal convolutif Résumé : (Auteur) Feature extraction is of significance for hyperspectral image (HSI) classification. Compared with conventional hand-crafted feature extraction, deep learning can automatically learn features with discriminative information. However, two issues exist in applying deep learning to HSIs. One issue is how to jointly extract spectral features and spatial features, and the other one is how to train the deep model when training samples are scarce. In this paper, a deep convolutional neural network with two-branch architecture is proposed to extract the joint spectral-spatial features from HSIs. The two branches of the proposed network are devoted to features from the spectral domain as well as the spatial domain. The learned spectral features and spatial features are then concatenated and fed to fully connected layers to extract the joint spectral-spatial features for classification. When the training samples are limited, we investigate the transfer learning to improve the performance. Low and mid-layers of the network are pretrained and transferred from other data sources; only top layers are trained with limited training samples extracted from the target scene. Experiments on Airborne Visible/Infrared Imaging Spectrometer and Reflective Optics System Imaging Spectrometer data demonstrate that the learned deep joint spectral-spatial features are discriminative, and competitive classification results can be achieved when compared with state-of-the-art methods. The experiments also reveal that the transferred features boost the classification performance. Numéro de notice : A2017-503 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2698503 En ligne : http://dx.doi.org/10.1109/TGRS.2017.2698503 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86448 [article]

Joint hyperspectral superresolution and unmixing with interactive feedback / Chen Yi in IEEE Transactions on geoscience and remote sensing, vol 55 n° 7 (July 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 7 (July 2017) . - pp 3823 - 3834 Titre : Joint hyperspectral superresolution and unmixing with interactive feedback Type de document : Article/Communication Auteurs : Chen Yi, Auteur ; Yong-Qiang Zhao, Auteur ; Jingxiang Yang, Auteur ; et al., Auteur Année de publication : 2017 Article en page(s) : pp 3823 - 3834 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] accentuation d'image [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] image hyperspectrale Résumé : (Auteur) This paper presents an interactive feedback scheme of spatial resolution enhancement and spectral unmixing in hyperspectral imaging. Traditionally spatial resolution enhancement and spectral unmixing operations have been carried out separately, often in series. In such sequential processing, spatially enhanced hyperspectral images (HSIs) may introduce distortion in spectral fidelity making spectral unmixing results unreliable, or vice versa. Since both high- and low-resolution HSIs have the same endmembers, the deviation in spectral unmixing between targets and estimated high-resolution HSIs can be used as feedback to control spatial resolution enhancement. The spatial difference before and after unmixing can also be used as feedback to enhance spectral unmixing. Therefore, spectral unmixing is utilized as a constraint to spatial resolution enhancement, while spatial resolution enhancement helps improve spectral unmixing results. The performance of spatial resolution enhancement and spectral unmixing can be improved since one behaves like a prior to the other. Experimental results on both simulated and real HSI data sets demonstrate that the proposed interactive feedback scheme simultaneously achieved spatial resolution enhancement and spectral unmixing fidelity. This paper is an extended version of the previous work. Numéro de notice : A2017-488 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1109/TGRS.2017.2681721 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=86415 [article]

Hyperspectral image denoising via sparse representation and low-rank constraint / Yong-Qiang Zhao in IEEE Transactions on geoscience and remote sensing, vol 53 n° 1 (January 2015)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 53 n° 1 (January 2015) . - pp 296 - 308 Titre : Hyperspectral image denoising via sparse representation and low-rank constraint Type de document : Article/Communication Auteurs : Yong-Qiang Zhao, Auteur ; Jingxiang Yang, Auteur Année de publication : 2015 Article en page(s) : pp 296 - 308 Note générale : Bibliogaphie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] corrélation d'images [Termes descripteurs IGN] filtrage du bruit [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] redondance de données Résumé : (Auteur) Hyperspectral image (HSI) denoising is an essential preprocess step to improve the performance of subsequent applications. For HSI, there is much global and local redundancy and correlation (RAC) in spatial/spectral dimensions. In addition, denoising performance can be improved greatly if RAC is utilized efficiently in the denoising process. In this paper, an HSI denoising method is proposed by jointly utilizing the global and local RAC in spatial/spectral domains. First, sparse coding is exploited to model the global RAC in the spatial domain and local RAC in the spectral domain. Noise can be removed by sparse approximated data with learned dictionary. At this stage, only local RAC in the spectral domain is employed. It will cause spectral distortion. To compensate the shortcoming of local spectral RAC, low-rank constraint is used to deal with the global RAC in the spectral domain. Different hyperspectral data sets are used to test the performance of the proposed method. The denoising results by the proposed method are superior to results obtained by other state-of-the-art hyperspectral denoising methods. Numéro de notice : A2015-033 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2014.2321557 En ligne : https://doi.org/10.1109/TGRS.2014.2321557 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=75115 [article]

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