Centre dedocumentation
scientifique

Histograms of oriented mosaic gradients for snapshot spectral image description / Lulu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 183 (January 2022)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 183 (January 2022) . - pp 79 - 93 Titre : Histograms of oriented mosaic gradients for snapshot spectral image description Type de document : Article/Communication Auteurs : Lulu Chen, Auteur ; Yong-Qiang Zhao, Auteur ; Jonathan Cheung-Wai Chan, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 79 - 93 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] capteur multibande [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtre spectral [Termes IGN] histogramme [Termes IGN] image proche infrarouge [Termes IGN] image spectrale [Termes IGN] mosaïque d'images [Termes IGN] poursuite de cible [Termes IGN] temps instantané Résumé : (auteur) This paper presents a feature descriptor using Histogram of Oriented Mosaic Gradient (HOMG) that extracts spatial-spectral features directly from mosaic spectral images. Spectral imaging utilizes unique spectral signatures to distinguish objects of interest in the scene more discriminatively. Snapshot spectral cameras equipped with spectral filter arrays (SFAs) capture spectral videos in real time, making it possible to detect/track fast moving targets based on spectral imaging. How to effectively extract the spatial-spectral feature directly from the mosaic spectral images acquired by snapshot spectral cameras is a core issue for detection/tracking. So far, there is a lack of comprehensive and in-depth research on this issue. To this end, this paper proposed a new spatial-spectral feature extractor for mosaic spectral images. The proposed scheme finds two forms of SFA neighborhood (SFAN) to construct a feature extractor suitable for any SFA structure. Exploiting the spatial-spectral correlation in two SFANs, we design six mosaic spatial-spectral gradient operators to compute spatial-spectral gradient maps (SGMs). HOMG descriptors are constructed using the magnitude and orientation of SGMs. The effectiveness and generalizability of the proposed method have been verified with object tracking experiments. Compared to the state-of-the-art feature descriptors, HOMG ranked first on two datasets captured with snapshot spectral camera with different SFAs, achieving a gain of 3.9% and 5.9% in average success rate over the second-ranked feature. Numéro de notice : A2022-010 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.10.018 Date de publication en ligne : 12/11/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.10.018 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99058 [article]

Réservation

Réserver ce document

Exemplaires (3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2022011	SL	Revue	Centre de documentation	Revues en salle	Disponible
081-2022013	DEP-RECP	Revue	LaSTIG	Dépôt en unité	Exclu du prêt
081-2022012	DEP-RECF	Revue	Nancy	Dépôt en unité	Exclu du prêt

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 IGN] analyse spectrale [Termes IGN] calcul tensoriel [Termes IGN] équation de Laplace [Termes IGN] fusion d'images [Termes IGN] graphe [Termes IGN] image hyperspectrale [Termes IGN] image multibande [Termes IGN] optimisation (mathématiques) [Termes IGN] tenseur [Termes 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 IGN] apprentissage profond [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal [Termes IGN] extraction de traits caractéristiques [Termes IGN] filtrage numérique d'image [Termes IGN] image AVIRIS [Termes IGN] image hyperspectrale [Termes IGN] image ROSIS [Termes 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 IGN] accentuation d'image [Termes IGN] analyse des mélanges spectraux [Termes IGN] image à haute résolution [Termes 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 DOI : 10.1109/TGRS.2017.2681721 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 IGN] appariement d'images [Termes IGN] filtrage du bruit [Termes IGN] image hyperspectrale [Termes 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]

Réservation

Réserver ce document

Exemplaires (1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-2015011	RAB	Revue	Centre de documentation	En réserve 3L	Disponible