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Using electrical resistivity tomography to detect wetwood and estimate moisture content in silver fir (Abies alba Mill.) / Ludovic Martin in Annals of Forest Science [en ligne], vol 78 n° 3 (September 2021)  

Public [article]  inAnnals of Forest Science [en ligne] > vol 78 n° 3 (September 2021) . - n° 65 Titre : Using electrical resistivity tomography to detect wetwood and estimate moisture content in silver fir (Abies alba Mill.) Type de document : Article/Communication Auteurs : Ludovic Martin, Auteur ; Sébastien Cochard, Auteur ; Stefan Mayr, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 65 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] Abies alba [Termes IGN] bois sur pied [Termes IGN] filière bois - forêt [Termes IGN] forêt humide [Termes IGN] humidité du sol [Termes IGN] Massif central (France) [Termes IGN] résistivité [Termes IGN] teneur en eau de la végétation [Termes IGN] tomographie Résumé : (auteur) Key message : Using several experimental approaches, we have demonstrated that electrical resistivity tomography (ERT) is a reliable nondestructive tool for estimating the moisture content of heartwood in situ. ERT measurements show that water pockets in heartwood (wetwood) are present in a large majority (90%) of silver fir ( Abies alba Mill.) trunks. Context : For wood professionals, the presence of wetwood in wood logs leads to an increase in costs, especially during the drying process. Assessing these internal properties in situ with a nondestructive method will provide reliable information for improved management of respective forests. Aims : The objective of this study was to evaluate the efficiency of the electrical resistivity tomography (ERT) tool to detect wetwood in standing trees and to estimate the mean moisture content (MC) of silver fir trunks. Methods : The study was carried out in 3 forests located in the region “Massif Central” in France. We selected 58 silver fir trees, visually healthy and without visible default. Each tree has been subject to regular ERT measurements for more than a year. At the same time, one to three cores were taken from each tree in order to measure the actual MC of the wood. Results : 90% of the silver fir trees showed the presence of wetwood in their heartwood. Our results showed a significant correlation between the mean heartwood MC measured on cores and the mean electrical resistivity (ER) obtained with ERT. Conclusion : The presence of wetwood occurs in a high proportion of the silver fir trees studied, and (ii) ERT can be used to estimate the average MC of the heartwood of standing trees. However, the data provided by ERT vary seasonally and do not allow the precise location of wetwood. Numéro de notice : A2021-622 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s13595-021-01078-9 date de publication en ligne : 27/07/2021 En ligne : https://doi.org/10.1007/s13595-021-01078-9 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98244 [article]

Three-dimensional reconstruction of seismo-traveling ionospheric disturbances after March 11, 2011, Japan Tohoku earthquake / Changzhi Zhai in Journal of geodesy, vol 95 n° 7 (July 2021)  

Public [article]  inJournal of geodesy > vol 95 n° 7 (July 2021) . - n° 77 Titre : Three-dimensional reconstruction of seismo-traveling ionospheric disturbances after March 11, 2011, Japan Tohoku earthquake Type de document : Article/Communication Auteurs : Changzhi Zhai, Auteur ; Yibin Yao, Auteur ; Jian Kong, Auteur Année de publication : 2021 Article en page(s) : n° 77 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] diffusion de Rayleigh [Termes IGN] GeoNet [Termes IGN] modèle ionosphérique [Termes IGN] onde acoustique [Termes IGN] perturbation ionosphérique [Termes IGN] reconstruction 3D [Termes IGN] séisme [Termes IGN] signal GPS [Termes IGN] teneur totale en électrons [Termes IGN] Tohoku (Japon) [Termes IGN] tomographie Résumé : (auteur) The electron density structures of the seismo-traveling ionospheric disturbances (STIDs) during the Tohoku earthquake are reconstructed by applying the three-dimensional computerized ionospheric tomography (3DCIT) technique with a 30-s time resolution for the first time. The vertical distribution of 3DCIT results is consistent with the constellation observing system for meteorology, ionosphere and climate (COSMIC) observations. The horizontal speeds of STIDs at different altitudes are estimated, and the three types of STIDs related to Rayleigh waves, acoustic waves and gravity waves are identified by their propagation characters. The magnitude of STIDs related to Rayleigh waves decreased with altitude, and there was no significant difference between the speeds (~ 2500 m/s) at different altitudes. The STIDs caused by acoustic waves traveled faster at 300 km altitude (~ 666–724 m/s) than at 150 km altitude (~ 500–550 m/s). From 150 to 250 km altitudes, in the STIDs induced by gravity waves, the magnitude of positive and negative wave fronts showed the opposite trend. The speed at 300 km altitude (~ 332 m/s) was slightly larger than at 150 km altitude (~ 310 m/s). The Rayleigh waves related STIDs showed a conic-like geometry, whereas the acoustic waves and gravity waves induced STIDs showed inverted conic-like geometries. The possible propagation mechanisms of different types of STIDs are also discussed. Numéro de notice : A2021-524 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-021-01533-5 date de publication en ligne : 23/06/2021 En ligne : https://doi.org/10.1007/s00190-021-01533-5 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97961 [article]

Deep learning in denoising of micro-computed tomography images of rock samples / Mikhail Sidorenko in Computers & geosciences, vol 151 (June 2021)  

Public [article]  inComputers & geosciences > vol 151 (June 2021) . - n° 104716 Titre : Deep learning in denoising of micro-computed tomography images of rock samples Type de document : Article/Communication Auteurs : Mikhail Sidorenko, Auteur ; Denis Orlov, Auteur ; Mohammad Ebadi, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 104716 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] accentuation d'image [Termes IGN] apprentissage profond [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] filtrage du bruit [Termes IGN] filtre passe-bande [Termes IGN] roche [Termes IGN] tomographie Résumé : (auteur) Nowadays, the advantages of Digital Rock Physics (DRP) are well known and widely applied in comprehensive core analysis. It is also known that the quality of the 3D pore scale model drastically influences the results of rock properties simulation, which makes the preprocessing stage of DRP very important. In this work, we consider the application of Deep Convolutional Neural Networks (CNNs) for the preprocessing of CT images, specifically for denoising, in two setups - conventional fully-supervised learning and the self-supervised learning, when the only available data is the noisy images. To train CNNs in a supervised setup, we use images processed by a combination of bilateral and bandpass filters. We trained CNNs of the same architecture with different loss functions to find out how the choice of a loss function influences the model's performance. Some of the obtained CNNs yielded the highest quality in terms of full-reference and no-reference metrics and significant histogram effect (bimodal intensity distribution). Images denoised with these models were qualitatively and quantitatively better than the reference “ground truth” images used for training. We use the Deep Image Prior algorithm to train denoising models in a self-supervised setup. The obtained models are much better than ones obtained in fully-supervised setup, but are too slow, as they are optimization-based rather than feed-forward. Such an algorithm can be used in the dataset generation for feed-forward meta-models. These results could help to develop an AI-based instrument to build high-quality 3D segmented models of rocks for DRP applications. Numéro de notice : A2021-389 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1016/j.cageo.2021.104716 date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.1016/j.cageo.2021.104716 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97672 [article]

An improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm / Jian Kong in IEEE Transactions on geoscience and remote sensing, vol 59 n° 5 (May 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 5 (May 2021) . - pp 3725 - 3736 Titre : An improved computerized ionospheric tomography model fusing 3-D multisource ionospheric data enabled quantifying the evolution of magnetic storm Type de document : Article/Communication Auteurs : Jian Kong, Auteur ; Lulu Shan, Auteur ; Chen Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3725 - 3736 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes IGN] données GNSS [Termes IGN] erreur absolue [Termes IGN] filtre de Kalman [Termes IGN] fusion de données multisource [Termes IGN] modèle ionosphérique [Termes IGN] modèle stochastique [Termes IGN] perturbation ionosphérique [Termes IGN] tempête magnétique [Termes IGN] teneur totale en électrons [Termes IGN] tomographie Résumé : (auteur) Global Navigation Satellite System (GNSS) ionospheric tomography is a typical ill-posed problem. Joint inversion with external observation data is one of the effective ways to mitigate the problem. In this article, by fusing 3-D multisource ionospheric data, and improving the stochastic model, an improved GNSS tomographic algorithm MFCIT [computerized ionospheric tomography (CIT) using mapping function] is presented. The accuracy of the algorithm is validated by selected data under different geomagnetic and solar conditions acquired in Europe. The results show that the estimated, statistically significant uncertainty for each of the layers is about 0.50–3.0TECU, with the largest absolute error within 6.0TECU. The advantage of the MFCIT is that it is based on the Kalman filter, which enables efficient near real-time 3-D monitoring of ionosphere. The temporal resolution can reach ~1 min level. Here, we apply the ionospheric tomography inversion to the magnetic storm on January 7, 2015, in the European region, and quantified the evolution of the storm. The results show that the difference of the core region between the MFCIT and CODE GIM is less than 1TECU. More importantly, during the initial phase of the storm, when the ionospheric disturbance is not evident in the single layer CODE GIM model, the MFCIT shows obvious positive disturbances in the upper ionosphere, although there is no disturbance in the F2 layer. The MFCIT further tracks the evolution of the magnetic storm that the ionospheric disturbance expands from the upper to the lower ionosphere layers, and at UT12:00, the disturbance continues to spread to the F2 layer. Numéro de notice : A2021-396 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3022949 date de publication en ligne : 24/09/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3022949 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97686 [article]

A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms / Dimitrios Bellos in Machine Vision and Applications, vol 32 n° 3 (May 2021)  

Public [article]  inMachine Vision and Applications > vol 32 n° 3 (May 2021) . - n° 75 Titre : A stacked dense denoising–segmentation network for undersampled tomograms and knowledge transfer using synthetic tomograms Type de document : Article/Communication Auteurs : Dimitrios Bellos, Auteur ; Mark Basham, Auteur ; Tony Pridmore, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] acquisition de connaissances [Termes IGN] apprentissage profond [Termes IGN] échantillonnage [Termes IGN] filtrage du bruit [Termes IGN] rapport signal sur bruit [Termes IGN] rayon X [Termes IGN] reconstruction d'image [Termes IGN] segmentation sémantique [Termes IGN] série temporelle [Termes IGN] tomographie Résumé : (auteur) Over recent years, many approaches have been proposed for the denoising or semantic segmentation of X-ray computed tomography (CT) scans. In most cases, high-quality CT reconstructions are used; however, such reconstructions are not always available. When the X-ray exposure time has to be limited, undersampled tomograms (in terms of their component projections) are attained. This low number of projections offers low-quality reconstructions that are difficult to segment. Here, we consider CT time-series (i.e. 4D data), where the limited time for capturing fast-occurring temporal events results in the time-series tomograms being necessarily undersampled. Fortunately, in these collections, it is common practice to obtain representative highly sampled tomograms before or after the time-critical portion of the experiment. In this paper, we propose an end-to-end network that can learn to denoise and segment the time-series’ undersampled CTs, by training with the earlier highly sampled representative CTs. Our single network can offer two desired outputs while only training once, with the denoised output improving the accuracy of the final segmentation. Our method is able to outperform state-of-the-art methods in the task of semantic segmentation and offer comparable results in regard to denoising. Additionally, we propose a knowledge transfer scheme using synthetic tomograms. This not only allows accurate segmentation and denoising using less real-world data, but also increases segmentation accuracy. Finally, we make our datasets, as well as the code, publicly available. Numéro de notice : A2021-456 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1007/s00138-021-01196-4 date de publication en ligne : 27/04/2021 En ligne : https://doi.org/10.1007/s00138-021-01196-4 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97902 [article]

3D reconstruction of internal wood decay using photogrammetry and sonic tomography / Junjie Zhang in Photogrammetric record, vol 35 n° 171 (September 2020)  

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An improved constrained simultaneous iterative reconstruction technique for ionospheric tomography / Yi Bin Yao in GPS solutions, Vol 24 n° 3 (July 2020)  

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Advances in digital image correlation (DIC) / Jean-Noël Perie (2020)  

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Optical Coherence Tomography and Its Non-medical Applications / Michael R. Wang (2020)  

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3D recording and interpretation for maritime Aachaeology / John K. McCarthy (2019)  

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Bayesian iterative reconstruction methods for 3D X-ray Computed Tomography / Camille Chapdelaine (2019)  

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X-ray microdensitometry of wood: A review of existing principles and devices / Philippe Jacquin in Dendrochronologia, vol 42 (March 2017)  

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Within-stem maps of wood density and water content for characterization of species: a case study on three hardwood and two softwood species / Fleur Longuetaud in Annals of Forest Science [en ligne], vol 73 n° 3 (September 2016)  

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A new computerized ionosphere tomography model using the mapping function and an application to the study of seismic-ionosphere disturbance / Jian Kong in Journal of geodesy, vol 90 n° 8 (August 2016)  

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Variations in the natural density of European oak wood affect thermal degradation during thermal modification / Joël Hamada in Annals of Forest Science [en ligne], vol 73 n° 2 (June 2016)  

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