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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 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]

Temporal calibration and synchronization of robotic total stations for kinematic multi-sensor-systems / Tomas Thalmann in Journal of applied geodesy, vol 15 n° 1 (January 2021)  

Public [article]  inJournal of applied geodesy > vol 15 n° 1 (January 2021) . - pp 13 - 30 Titre : Temporal calibration and synchronization of robotic total stations for kinematic multi-sensor-systems Type de document : Article/Communication Auteurs : Tomas Thalmann, Auteur ; Hans Neuner, Auteur Année de publication : 2021 Article en page(s) : pp 13 - 30 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] étalonnage [Termes IGN] positionnement cinématique [Termes IGN] référentiel [Termes IGN] réseau local sans fil [Termes IGN] synchronisation [Termes IGN] tachéomètre électronique robotisé [Termes IGN] tenseur Résumé : (auteur) Despite the increasing interest in kinematic data acquisition, Robotic Total Stations (RTSs) are still relatively seldom used. No matter if Mobile Mapping Systems or Control & Guidance, GNSS is mostly used as position sensor, which limits the application to outdoor areas. For indoor applications, a combination of relative sensors is usually employed. One reason why RTSs are not used is the challenging time referencing and synchronization. In this work we analyze the challenges of a synchronized kinematic application of RTSs and present solutions. Our approach is based on a wireless network synchronization to establish a precise temporal reference frame. The achievable synchronization quality is thoroughly examined. In addition we develop a kinematic model of spherical measurements, that incorporates timing related parameters. To estimate these parameters we propose a temporal calibration utilizing an industrial robot. Both parts of our approach are evaluated using a test setup of two total stations, proofing an overall synchronization accuracy of 0.2 ms. An overall horizontal kinematic point accuracy of 2.3 mm reveals the potential of sufficiently synchronized RTSs. Numéro de notice : A2021-043 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1515/jag-2019-0070 date de publication en ligne : 16/12/2020 En ligne : https://doi.org/10.1515/jag-2019-0070 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96770 [article]

A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data / Qingwang Wang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1568 -1586 Titre : A discriminative tensor representation model for feature extraction and classification of multispectral LiDAR data Type de document : Article/Communication Auteurs : Qingwang Wang, Auteur ; Yanfeng Gu, Auteur Année de publication : 2020 Article en page(s) : pp 1568 -1586 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] Amérique du nord [Termes IGN] analyse discriminante [Termes IGN] calcul tensoriel [Termes IGN] carte d'occupation du sol [Termes IGN] classification multibande [Termes IGN] classification par séparateurs à vaste marge [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] état de l'art [Termes IGN] extraction de traits caractéristiques [Termes IGN] image multibande [Termes IGN] modèle géométrique [Termes IGN] semis de points [Termes IGN] tenseur [Termes IGN] vectorisation [Termes IGN] voisinage (topologie) Résumé : (Auteur) Multispectral light detection and ranging (MS-LiDAR) systems open the door to the possibility in the 3-D land cover classification at a finer scale using only point cloud data. This article proposes a model based on the tensor representation for multispectral point cloud classification. The proposed method combines the 3-D local spatial structure of each multispectral point by characterizing the point with a second-order tensor. The first mode of the tensor indicates the spatial location and spectral information of each point (i.e., the row of the second-order tensor) and the second mode denotes the neighborhood geometric and spectral structures (i.e., the column of the second-order tensor). Then we develop a novel tensor manifold discriminant embedding (TMDE) algorithm to extract the geometric–spectral features for multispectral point clouds classification. TMDE solves the mapping matrices of each mode by preserving the intraclass samples’ distribution further making it more compact and maximizing the distance of different classes. Finally, the support vector machine classifier with the extracted features as input is used to implement the classification of multispectral point clouds. Experiments are conducted on two real multispectral point cloud data sets. The experimental results demonstrate that the proposed method can achieve significant improvements in classification accuracies in comparison with several state-of-the-art algorithms. Numéro de notice : A2020-086 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947081 date de publication en ligne : 30/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947081 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94660 [article]

Generalized tensor regression for hyperspectral image classification / Jianjun Liu in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 2 (February 2020) . - pp 1244 - 1258 Titre : Generalized tensor regression for hyperspectral image classification Type de document : Article/Communication Auteurs : Jianjun Liu, Auteur ; Zebin Wu, Auteur ; Liang Xiao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1244 - 1258 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] bande spectrale [Termes IGN] calcul tensoriel [Termes IGN] classification dirigée [Termes IGN] image hyperspectrale [Termes IGN] image infrarouge [Termes IGN] méthode fondée sur le noyau [Termes IGN] régression [Termes IGN] spectromètre imageur [Termes IGN] tenseur Résumé : (auteur) In this article, we propose a novel tensorial approach, namely, generalized tensor regression, for hyperspectral image classification. First, a simple and effective classifier, i.e., the ridge regression for multivariate labels, is extended to its tensorial version by taking advantages of tensorial representation. Then, the discrimination information of different modes is exploited to further strengthen the capacity of the model. Moreover, the model can be simplified and solved easily. Different from traditional tensorial methods, the proposed model can be utilized to capture not only the intrinsic structure of data in a physical sense but also the generalized relationship of data in a logical sense. Our proposed approach is shown to be effective for different classification purposes on a series of instantiations. Specifically, our experiment results with hyperspectral images collected by the airborne visible/infrared imaging spectrometer, the reflective optics spectrographic imaging system and the ITRES CASI-1500 demonstrate the effectiveness of the proposed approach as compared to other tensor-based classifiers and multiple kernel learning methods. Numéro de notice : A2020-097 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2944989 date de publication en ligne : 21/10/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2944989 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94670 [article]

Unmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition / Sheng Wang in ISPRS Journal of photogrammetry and remote sensing, vol 155 (September 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 155 (September 2019) . - pp 58 - 71 Titre : Unmanned aerial system multispectral mapping for low and variable solar irradiance conditions: Potential of tensor decomposition Type de document : Article/Communication Auteurs : Sheng Wang, Auteur ; Andreas Baum, Auteur ; Pablo J. Zarco-Tejada, Auteur ; Carsten Dam-Hansen, Auteur ; Anders Thorseth, Auteur ; Peter Bauer-Gottwein, Auteur ; Filippo Bandini, Auteur ; Monica Garcia, Auteur Année de publication : 2019 Article en page(s) : pp 58 - 71 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] éclairement énergétique [Termes IGN] étalonnage de capteur (imagerie) [Termes IGN] image captée par drone [Termes IGN] image multibande [Termes IGN] nébulosité [Termes IGN] réflectance spectrale [Termes IGN] réflectance végétale [Termes IGN] tenseur Résumé : (Auteur) Unlike satellite earth observation, multispectral images acquired by Unmanned Aerial Systems (UAS) provide great opportunities to monitor land surface conditions also in cloudy or overcast weather conditions. This is especially relevant for high latitudes where overcast and cloudy days are common. However, multispectral imagery acquired by miniaturized UAS sensors under such conditions tend to present low brightness and dynamic ranges, and high noise levels. Additionally, cloud shadows over space (within one image) and time (across images) are frequent in UAS imagery collected under variable irradiance and result in sensor radiance changes unrelated to the biophysical dynamics at the surface. To exploit the potential of UAS for vegetation mapping, this study proposes methods to obtain robust and repeatable reflectance time series under variable and low irradiance conditions. To improve sensor sensitivity to low irradiance, a radiometric pixel-wise calibration was conducted with a six-channel multispectral camera (mini-MCA6, Tetracam) using an integrating sphere simulating the varying low illumination typical of outdoor conditions at 55oN latitude. The sensor sensitivity was increased by using individual settings for independent channels, obtaining higher signal-to-noise ratios compared to the uniform setting for all image channels. To remove cloud shadows, a multivariate statistical procedure, Tucker tensor decomposition, was applied to reconstruct images using a four-way factorization scheme that takes advantage of spatial, spectral and temporal information simultaneously. The comparison between reconstructed (with Tucker) and original images showed an improvement in cloud shadow removal. Outdoor vicarious reflectance validation showed that with these methods, the multispectral imagery can provide reliable reflectance at sunny conditions with root mean square deviations of around 3%. The proposed methods could be useful for operational multispectral mapping with UAS under low and variable irradiance weather conditions as those prevalent in northern latitudes. Numéro de notice : A2019-311 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2019.06.017 date de publication en ligne : 04/07/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.06.017 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93336 [article]

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