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GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings / Bin Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 10 (October 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 10 (October 2021) . - pp 8305 - 8325 Titre : GPRInvNet: Deep learning-based ground-penetrating radar data inversion for tunnel linings Type de document : Article/Communication Auteurs : Bin Liu, Auteur ; Yuxiao Ren, Auteur ; Hanchi Liu, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 8305 - 8325 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage profond [Termes IGN] cible cachée [Termes IGN] extraction de traits caractéristiques [Termes IGN] géolocalisation par radar pénétrant GPR [Termes IGN] reconstruction d'image [Termes IGN] revêtement [Termes IGN] tunnel Résumé : (auteur) A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhance each trace, and then further condense the features of each trace separately. As a result, the sensitive zones on the permittivity maps spatially aligned to the enhanced trace could be reconstructed accurately. The GPRInvNet has been utilized to reconstruct the permittivity map of tunnel linings. A diverse range of dielectric models of tunnel linings containing complex defects has been reconstructed using GPRInvNet. The results have demonstrated that the GPRInvNet is capable of effectively reconstructing complex tunnel lining defects with clear boundaries. Comparative results with existing baseline methods also demonstrated the superiority of the GPRInvNet. For the purpose of generalizing the GPRInvNet to real GPR data, some background noise patches recorded from practical model testing were integrated into the synthetic GPR data to retrain the GPRInvNet. The model testing has been conducted for validation, and experimental results revealed that the GPRInvNet had also achieved satisfactory results with regard to the real data. Numéro de notice : A2021-710 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3046454 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1109/TGRS.2020.3046454 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98610 [article]

Fusion of sparse model based on randomly erased image for SAR occluded target recognition / Zhiqiang He in IEEE Transactions on geoscience and remote sensing, vol 58 n° 11 (November 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 11 (November 2020) . - pp 7829 - 7844 Titre : Fusion of sparse model based on randomly erased image for SAR occluded target recognition Type de document : Article/Communication Auteurs : Zhiqiang He, Auteur ; Huaitie Xiao, Auteur ; Chao Gao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7829 - 7844 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] cible cachée [Termes IGN] détection de cible [Termes IGN] détection de partie cachée [Termes IGN] image radar moirée [Termes IGN] reconstruction d'image [Termes IGN] représentation parcimonieuse Résumé : (auteur) The recognition of partially occluded targets is a difficult problem in the field of synthetic aperture radar (SAR) target recognition. To eliminate the effect of occlusion, the intuitive idea is to determine the exact location and the size of the occluded area. However, this is very difficult, even impossible in practice. In order to avoid this difficulty and to improve the recognition performance for the partially occluded target, a fusion strategy of the sparse representation (SR) model based on randomly erased images is proposed to recognize the partially occluded target. The proposed method randomly erases some areas many times in both the test samples and the training samples. The erased training samples in each erasure are used to sparsely represent the corresponding erased test sample. Finally, all the SR results are fused to recognize the test sample. The proposed method utilizes random erasure to eliminate the possible occluded region. In addition, this method uses the fusion strategy to overcome under-erasing of the occluded region and erroneous erasure of the unoccluded region. The key parameter of the proposed method is the erasure ratio only. Although the erasure is random, the recognition performance of the method is relatively stable. Therefore, the method can eliminate the influence of occlusion without determining the details of occlusion. The experimental results show that the proposed method is significantly better than the state-of-the-art methods in the case of occlusion. Additionally, the recognition performance of the proposed method is similar to some comparison methods in the case of no occlusion. Numéro de notice : A2020-680 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2984577 Date de publication en ligne : 14/04/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2984577 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96204 [article]

Application of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data / Mahdi Moalla in IEEE Transactions on geoscience and remote sensing, vol 58 n° 10 (October 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 10 (October 2020) . - pp 7022 - 7034 Titre : Application of convolutional and recurrent neural networks for buried threat detection using ground penetrating radar data Type de document : Article/Communication Auteurs : Mahdi Moalla, Auteur ; Hichem Frigui, Auteur ; Andrew Karem, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 7022 - 7034 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] cible cachée [Termes IGN] classification barycentrique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données radar [Termes IGN] image radar moirée [Termes IGN] mine antipersonnel [Termes IGN] radar pénétrant GPR [Termes IGN] réseau neuronal récurrent [Termes IGN] sous-sol Résumé : (auteur) We propose discrimination algorithms for buried threat detection (BTD) that exploit deep convolutional neural networks (CNNs) and recurrent neural networks (RNN) to analyze 2-D GPR B-scans in the down-track (DT) and cross-track (CT) directions as well as 3-D GPR volumes. Instead of imposing a specific model or handcrafted features, as in most existing detectors, we use large real GPR data collections and data-driven approaches that learn: 1) features characterizing buried explosive objects (BEOs) in 2-D B-scans, both in the DT and CT directions; 2) the variation of the CNN features learned in a fixed 2-D view across the third dimension; and 3) features characterizing BEOs in the original 3-D space. The proposed algorithms were trained and evaluated using large experimental GPR data covering a surface area of 120 000 m 2 from 13 different lanes across two U.S. test sites. These data include a diverse set of BEOs consisting of varying shapes, metal content, and underground burial depths. We provide some qualitative analysis of the proposed algorithms by visually comparing their performance and consistency along different dimensions and visualizing typical features learned by some nodes of the network. We also provide quantitative analysis that compares the receiver operating characteristics (ROCs) obtained using the proposed algorithms with those obtained using existing approaches based on CNN as well as traditional learning. Numéro de notice : A2020-586 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2978763 Date de publication en ligne : 25/03/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2978763 Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95914 [article]

Novel communication channel model for signal propagation and loss through layered earth / David O. LeVan in IEEE Transactions on geoscience and remote sensing, vol 58 n° 8 (August 2020)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 58 n° 8 (August 2020) . - pp 5393 - 5399 Titre : Novel communication channel model for signal propagation and loss through layered earth Type de document : Article/Communication Auteurs : David O. LeVan, Auteur Année de publication : 2020 Article en page(s) : pp 5393 - 5399 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement du signal [Termes IGN] atténuation du signal [Termes IGN] champ électromagnétique [Termes IGN] cible cachée [Termes IGN] géophysique [Termes IGN] mine de charbon [Termes IGN] modèle de simulation [Termes IGN] modèle mathématique [Termes IGN] modélisation [Termes IGN] propagation du signal [Termes IGN] Terre (planète) Résumé : (auteur) Signal propagation through-the-Earth (TTE) is of great importance to geophysicists searching for underground resources such as oil and gas, homeland defense searching for tunnels and underground structures, and mining operations. The Earth is a conductive medium, unlike air or space, which tends to “short-out” electromagnetic fields traditionally used for wireless communications. The magnitude of this effect depends on many factors, such as frequency and the type of Earth-material. Mathematical models of energy propagation have been developed to help us understand the signal propagation issues, and some models can be used to predict the performance of the specific electromagnetic energies being used. There are numerous ways of modeling the Earth to study energy propagation. Some early literature presented models of signal propagation through a homogeneous Earth. These were fairly accurate for signals traveling from one point in the Earth to another point. However, signals traveling from below the ground to the surface of the Earth encounter many different layers of the Earth. This realization led to the development of models of a layered Earth. A novel layered-Earth communication model is presented and evaluated as to its accuracy by using measured data gathered during TTE communication tests from 2007 to 2012. Evaluations show that the new layered-EARTH model provides improved accuracy for the prediction of signal propagation performance from within a subterranean space, such as a mine, to and from the surface of the Earth. Numéro de notice : A2020-472 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2965398 Date de publication en ligne : 28/01/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2965398 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95575 [article]

Quelle limite pour les mesures angulaires ? Vidéotachéomètrie en milieu industriel / Vincent Barras in Géomatique suisse, vol 111 n° 10 (01/10/2013)  

Public [article]  inGéomatique suisse > vol 111 n° 10 (01/10/2013) . - pp 556 - 559 Titre : Quelle limite pour les mesures angulaires ? Vidéotachéomètrie en milieu industriel Type de document : Article/Communication Auteurs : Vincent Barras, Auteur ; Martin Jeanneret, Auteur Année de publication : 2013 Article en page(s) : pp 556 - 559 Langues : Français (fre) Descripteur : [Vedettes matières IGN] Topographie [Termes IGN] auscultation d'ouvrage [Termes IGN] cible cachée [Termes IGN] lever tachéométrique [Termes IGN] surveillance d'ouvrage [Termes IGN] topométrie de précision [Termes IGN] topométrie industrielle [Termes IGN] vidéothéodolite Résumé : (Auteur) Pour détecter la provenance de l'ovalisation de trois groupes d'une centrale électrique souterraine, l'exploitant a contacté l'institut G2C de la HEIG-VD pour évaluer un procédé topométrique. L'objectif est de quantifier des mouvements transversaux de l'ordre de 0.15 [mm] par rapport à des références situées à près de 100 [m], soit l'épaisseur d'une feuille de papier observée depuis l'autre extrémité d'un terrain de football ! Après plusieurs travaux de recherche appliquée à cette problématique, une solution de mesures via un photo-théodolite, dédié initialement aux mesures astronomiques, a été privilégiée. Les pointés s'effectuent sur des LEDs qui font office d'«étoiles» positionnées sur les éléments à surveiller. Les résultats démontrent que, moyennant une matérialisation spécifique, un processus de mesure adapté et une grande répétition des observations, il est possible de déterminer des déplacements transversaux à la précision recherchée. Le développement des fonctionnalités du logiciel QDAEDALUS permet d'envisager de nombreuses utilisations dans le milieu industriel. Numéro de notice : A2013-620 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article DOI : sans En ligne : http://retro.seals.ch/digbib/view2?pid=geo-007:2013:111::1020 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32756 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
136-2013101	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Analyse des déformations dans un réseau géodésique d'auscultation d'ouvrage d'art / A. Belhadj in Bulletin des sciences géographiques, n° 28 (juin 2013)

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In situ estimation of water quality parameters in freshwater aquaculture ponds using hyperspectral imaging system / Amr Abd-Elrahman in ISPRS Journal of photogrammetry and remote sensing, vol 66 n° 4 (July - August 2011)  

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Géoréférencement des réseaux enterrés : des techniques de relevé à la gestion d’un cadastre du sous-sol / G. Cornette in XYZ, n° 127 (juin - août 2011) 

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Optimal manoeuvring of seismic sensors for localization of subsurface targets / Mubashir Alam in IEEE Transactions on geoscience and remote sensing, vol 45 n° 5 Tome 1 (May 2007)  

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Detection of stationary foliage-obscured targets by polarimetric millimeter-wave radar / A.Y. Nashashibi in IEEE Transactions on geoscience and remote sensing, vol 43 n° 1 (January 2005)  

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Detection of buried targets via active selection of labeled data: Application to sensing subsurface UXO / Y. Zhang in IEEE Transactions on geoscience and remote sensing, vol 42 n° 11 (November 2004)  

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