Catalogue en ligne IGN

Bulletin Imagerie

Bulletin-imagerie

Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al. / Guido Ceccherini in Annals of Forest Science [en ligne], vol 79 n° 1 (2022)

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[article]
inAnnals of Forest Science [en ligne] > vol 79 n° 1 (2022) . - n° 31

Titre :	Potentials and limitations of NFIs and remote sensing in the assessment of harvest rates: a reply to Breidenbach et al.
Type de document :	Article/Communication
Auteurs :	Guido Ceccherini, Auteur ; Grégory Duveiller, Auteur ; Giacomo Grassi, Auteur ; et al., Auteur
Année de publication :	2022
Article en page(s) :	n° 31
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes IGN] analyse comparative [Termes IGN] exploitation forestière [Termes IGN] Finlande [Termes IGN] foresterie [Termes IGN] image Landsat [Termes IGN] inventaire forestier étranger (données) [Termes IGN] placette d'échantillonnage [Termes IGN] récolte de bois [Termes IGN] ressources forestières [Termes IGN] Suède [Termes IGN] surface forestière [Termes IGN] Union Européenne
Résumé :	(auteur) The timely and accurate monitoring of forest resources is becoming of increasing importance in light of the multi-functionality of these ecosystems and their increasing vulnerability to climate change. Remote sensing observations of tree cover and systematic ground observations from National Forest Inventories (NFIs) represent the two major sources of information to assess forest area and use. The specificity of two methods is calling for an in-depth analysis of their strengths and weaknesses and for the design of novel methods emerging from the integration of satellite and surface data. On this specific debate, a recent paper by Breidenbach et al. published in this journal suggests that the detection of a recent increase in EU forest harvest rate—as reported in Nature by Ceccherini et al.—is largely due to technical limitations of satellite-based mapping. The article centers on the difficulty of the approaches to estimate wood harvest based on remote sensing. However, it does not discuss issues with the robustness of validation approaches solely based on NFIs. Here we discuss the use of plot data as a validation set for remote sensing products, discussing potentials and limitations of both NFIs and remote sensing, and how they can be used synergistically. Finally, we highlight the need to collect in situ data that is both relevant and compatible with remote sensing products within the European Union.
Numéro de notice :	A2022-630
Affiliation des auteurs :	non IGN
Thématique :	FORET/IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1186/s13595-022-01150-y
Date de publication en ligne :	13/07/2022
En ligne :	https://doi.org/10.1186/s13595-022-01150-y
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101393

[article]

Challenging the link between functional and spectral diversity with radiative transfer modeling and data / Javier Pacheco-Labradora in Remote sensing of environment, vol 280 (October 2022)

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[article]
inRemote sensing of environment > vol 280 (October 2022)

Titre :	Challenging the link between functional and spectral diversity with radiative transfer modeling and data
Type de document :	Article/Communication
Auteurs :	Javier Pacheco-Labradora, Auteur ; Mirco Migliavacca, Auteur ; Xuanlong Ma, Auteur ; Miguel D. Mahecha, Auteur ; Nuno Carvalhais, Auteur ; Ulrich Weber, Auteur ; Raquel Benavides, Auteur ; Olivier Bouriaud , Auteur ; et al., Auteur
Année de publication :	2022
Projets :	3-projet - voir note /
Note générale :	bibliographie JPL, MMi, and MMa acknowledge the German Aerospace Center (DLR) project OBEF-Accross2 “The Potential of Earth Observations to Capture Patterns of Biodiversity” (Contract No. 50EE1912, German Aerospace Center). JPL, MMi, AH, CW, MMa, GK, FJB, and UW acknowledge the German Aerospace Center (DLR) for providing DESIS imagery through the Announcement of Opportunity “EBioIDEA: Enhancing Biodiversity Inventories with DESIS Imagery Analysis”. FunDivEUROPE data collection was supported by the European Union Seventh Framework Programme (FP7/2007-2013) (grant agreement number: 265171) and the EU H2020 project Soil4Europe (Bioidversa 2017-2019). The in-situ plant traits data collected over Romanian and Spanish sites were supported by a Marie-Curie Fellowship (DIVERFOR, FP7-PEOPLE-2011-IEF. No. 302445) to R. Benavides. OB acknowledges funding from project 10PFE/2021 Ministry of Research, Innovation and Digitalization within Program 1 - Development of national research and development system, Subprogram 1.2 - Institutional Performance - RDI excellence funding projects. XM was supported by the National Natural Science Foundation of China (42171305), the Director Fund of the International Research Center of Big Data for Sustainable Development Goals (CBAS2022DF006), and the Open Fund of State Key Laboratory of Remote Sensing Science (OFSLRSS202229). We thank Prof. Dr. Michael Scherer-Lorenzen for coordinating the interaction with the FunDivEUROPE network and Dr. Fernando Valladares for coordinating data production in FunDivEUROPE sites in Spain. We thank Yuhan Li for helping collect and process Sentinel-2 data in 2020 for the verification task. ESA's Copernicus Open Access Hub enabled the free use of Sentinel-2 data.
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes IGN] biodiversité végétale [Termes IGN] image hyperspectrale [Termes IGN] image optique [Termes IGN] image Sentinel-MSI [Termes IGN] limite de résolution spectrale [Termes IGN] modèle de transfert radiatif [Termes IGN] variabilité
Résumé :	(auteur) In a context of accelerated human-induced biodiversity loss, remote sensing (RS) is emerging as a promising tool to map plant biodiversity from space. Proposed approaches often rely on the Spectral Variation Hypothesis (SVH), linking the heterogeneity of terrestrial vegetation to the variability of the spectroradiometric signals. Yet, due to observational limitations, the SVH has been insufficiently tested, remaining unclear which metrics, methods, and sensors could provide the most reliable estimates of plant biodiversity. Here we assessed the potential of RS to infer plant biodiversity using radiative transfer simulations and inversion. We focused specifically on “functional diversity,” which represents the spatial variability in plant functional traits. First, we simulated vegetation communities and evaluated the information content of different functional diversity metrics (FDMs) derived from their optical reflectance factors (R) or the corresponding vegetation “optical traits,” estimated via radiative transfer model inversion. Second, we assessed the effect of the spatial resolution, the spectral characteristics of the sensor, and signal noise on the relationships between FDMs derived from field and remote sensing datasets. Finally, we evaluated the plausibility of the simulations using Sentinel-2 (multispectral, 10 m pixel) and DESIS (hyperspectral, 30 m pixel) imagery acquired over sites of the Functional Significance of Forest Biodiversity in Europe (FunDivEUROPE) network. We demonstrate that functional diversity can be inferred both by reflectance and optical traits. However, not all the FDMs tested were suited for assessing plant functional diversity from RS. Rao's Q index, functional dispersion, and functional richness were the best-performing metrics. Furthermore, we demonstrated that spatial resolution is the most limiting RS feature. In agreement with simulations, Sentinel-2 imagery provided better estimates of plant diversity than DESIS, despite the coarser spectral resolution. However, Sentinel-2 offered inaccurate results at DESIS spatial resolution. Overall, our results identify the strengths and weaknesses of optical RS to monitor plant functional diversity. Future missions and biodiversity products should consider and benefit from the identified potentials and limitations of the SVH.
Numéro de notice :	A2022-582
Affiliation des auteurs :	LIF+Ext (2020- )
Thématique :	FORET/IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.rse.2022.113170
Date de publication en ligne :	18/07/2022
En ligne :	https://doi.org/10.1016/j.rse.2022.113170
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101343

[article]

Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning / J.F. Roberts in Computers & geosciences, vol 167 (October 2022)

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[article]
inComputers & geosciences > vol 167 (October 2022) . - n° 105192

Titre :	Pyeo: A Python package for near-real-time forest cover change detection from Earth observation using machine learning
Type de document :	Article/Communication
Auteurs :	J.F. Roberts, Auteur ; R. Mwangi, Auteur ; F. Mukabi, Auteur ; et al., Auteur
Année de publication :	2022
Article en page(s) :	n° 105192
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage automatique [Termes IGN] carte thématique [Termes IGN] déboisement [Termes IGN] détection de changement [Termes IGN] image Sentinel-MSI [Termes IGN] informatique en nuage [Termes IGN] Kenya [Termes IGN] langage de programmation [Termes IGN] observation de la Terre [Termes IGN] Python (langage de programmation) [Termes IGN] surveillance forestière
Résumé :	(auteur) Monitoring forest cover change from Earth observation data streams in near-real-time presents a challenge for automated change detection by way of a continuously updated big dataset. Even though deforestation is a significant global problem, forest cover changes in pairs of subsequent images happen relatively infrequently. Detecting a change can require the download and processing of tens, hundreds or even thousands of images. In geoscientific applications of Earth observation, machine learning algorithms are increasingly used. Once trained, a machine learning model can be applied to new images automatically. This paper introduces the open-access Python 3 package Pyeo - “Python for Earth Observation”. Pyeo provides a set of portable, extensible and modular Python functions for the automation of machine learning applications from Earth observation data streams, including automated search and download functionality, pre-processing and atmospheric correction, re-projection, creation of thematic base layers and machine learning classification or regression. Pyeo enables users to train their own machine learning models and then apply the models to newly downloaded imagery over their area of interest. This paper describes in detail how Pyeo works, its requirements, benefits, and a description of the libraries used. An application to the automated forest cover change detection in a region in Kenya is given. Pyeo can be used on cloud computing architectures such as Amazon Web Services, Microsoft Azure and Google Colab to provide scalable applications and processing solutions for the geosciences.
Numéro de notice :	A2022-706
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
DOI :	10.1016/j.cageo.2022.105192
Date de publication en ligne :	09/07/2022
En ligne :	https://doi.org/10.1016/j.cageo.2022.105192
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101575

[article]

Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling / Han Hu in ISPRS Journal of photogrammetry and remote sensing, vol 192 (October 2022)

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[article]
inISPRS Journal of photogrammetry and remote sensing > vol 192 (October 2022) . - pp 215 - 231

Titre :	Semi-supervised adversarial recognition of refined window structures for inverse procedural façade modelling
Type de document :	Article/Communication
Auteurs :	Han Hu, Auteur ; Xinrong Liang, Auteur ; Yulin Ding, Auteur ; et al., Auteur
Année de publication :	2022
Article en page(s) :	pp 215 - 231
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification semi-dirigée [Termes IGN] échantillonnage de données [Termes IGN] extraction de traits caractéristiques [Termes IGN] façade [Termes IGN] fenêtre (bâtiment) [Termes IGN] modélisation 3D du bâti BIM [Termes IGN] photographie aérienne oblique [Termes IGN] réseau antagoniste génératif
Résumé :	(auteur) Deep learning methods are typically data-hungry and require many labelled samples. Unfortunately, the amount of effort required to label the data has significantly hindered the application of deep learning methods, especially in 3D modelling tasks requiring heterogeneous samples. This paper proposes a semi-supervised adversarial recognition strategy embedded in the inverse procedural modelling engine to reduce data annotation costs for learning to model 3D façades. Beginning with textured level-of-details models, we use convolutional neural networks to recognise the types and estimate the parameters of windows from image patches. The window types and parameters are then assembled into the procedural grammar. A simple procedural engine is built inside off-the-shelf 3D modelling software, producing fine-grained window geometries. To obtain a useful model from a few labelled samples, we leverage a generative adversarial network to train the feature extractor in a semi-supervised manner. The adversarial training strategy exploits the unlabelled data to stabilise the training phase. Experiments using publicly available façade image datasets reveal that the proposed methods can improve classification accuracy and parameter estimation by approximately 10% and 50%, respectively, under the same network structure. In addition, performance gains are more pronounced when testing against unseen data featuring different façade styles.
Numéro de notice :	A2022-666
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2022.08.014
Date de publication en ligne :	30/08/2022
En ligne :	https://doi.org/10.1016/j.isprsjprs.2022.08.014
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101528

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Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts / Shengli Tao in Proceedings of the National Academy of Sciences of the United States of America PNAS, vol 119 n° 37 (2022)

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[article]
inProceedings of the National Academy of Sciences of the United States of America PNAS > vol 119 n° 37 (2022) . - n° e2116626119

Titre :	Increasing and widespread vulnerability of intact tropical rainforests to repeated droughts
Type de document :	Article/Communication
Auteurs :	Shengli Tao, Auteur ; Jérôme Chave, Auteur ; Pierre-Louis Frison , Auteur ; et al., Auteur
Année de publication :	2022
Projets :	3-projet - voir note /
Article en page(s) :	n° e2116626119
Note générale :	bibliographie This study was supported by an Investissement d’Avenir grant managed by the Agence Nationale de la Recherche (CEBA, ref. ANR-10-LABX-25-01; TULIP, ref. ANR-10-LABX-0041; ANAEE-France: ANR-11-INBS-0001), and by the National Natural Science Foundation of China (grant no. 31988102). This research was also supported by a Centre National d' Etudes Spatiales (CNES) postdoctoral fellowship to S.T., the CNES-BIOMASS pluriannual project, and the European Space Agency (ESA) Climate Change Initiative (CCI) Biomass project (contract no. 4000123662/18/I-NB).
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] forêt tropicale [Termes IGN] image radar [Termes IGN] sécheresse [Termes IGN] série temporelle [Termes IGN] vulnérabilité
Résumé :	(auteur) Intact tropical rainforests have been exposed to severe droughts in recent decades, which may threaten their integrity, their ability to sequester carbon, and their capacity to provide shelter for biodiversity. However, their response to droughts remains uncertain due to limited high-quality, long-term observations covering extensive areas. Here, we examined how the upper canopy of intact tropical rainforests has responded to drought events globally and during the past 3 decades. By developing a long pantropical time series (1992 to 2018) of monthly radar satellite observations, we show that repeated droughts caused a sustained decline in radar signal in 93%, 84%, and 88% of intact tropical rainforests in the Americas, Africa, and Asia, respectively. Sudden decreases in radar signal were detected around the 1997–1998, 2005, 2010, and 2015 droughts in tropical Americas; 1999–2000, 2004–2005, 2010–2011, and 2015 droughts in tropical Africa; and 1997–1998, 2006, and 2015 droughts in tropical Asia. Rainforests showed similar low resistance (the ability to maintain predrought condition when drought occurs) to severe droughts across continents, but American rainforests consistently showed the lowest resilience (the ability to return to predrought condition after the drought event). Moreover, while the resistance of intact tropical rainforests to drought is decreasing, albeit weakly in tropical Africa and Asia, forest resilience has not increased significantly. Our results therefore suggest the capacity of intact rainforests to withstand future droughts is limited. This has negative implications for climate change mitigation through forest-based climate solutions and the associated pledges made by countries under the Paris Agreement.
Numéro de notice :	A2022-681
Affiliation des auteurs :	UGE-LASTIG+Ext (2020- )
Thématique :	FORET/IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1073/pnas.2116626119
En ligne :	https://doi.org/10.1073/pnas.2116626119
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101538

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Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series / Tyler Susa in Marine geodesy, vol 45 n° 5 (September 2022)

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[article]
inMarine geodesy > vol 45 n° 5 (September 2022) . - pp 435 - 461

Titre :	Analytical method for high-precision seabed surface modelling combining B-spline functions and Fourier series
Type de document :	Article/Communication
Auteurs :	Tyler Susa, Auteur
Année de publication :	2022
Article en page(s) :	pp 435 - 461
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] bathymétrie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] Extreme Gradient Machine [Termes IGN] fond marin [Termes IGN] image Sentinel-MSI [Termes IGN] littoral [Termes IGN] modèle numérique de surface [Termes IGN] modélisation [Termes IGN] Porto Rico [Termes IGN] profondeur [Termes IGN] réflectance
Résumé :	(auteur) Accurate charting of nearshore bathymetry is critical to the safe and dependable use of coastal waterways frequented by the trading, fishing, tourism, and ocean energy industries. The accessibility of satellite imagery and the availability of various satellite-derived bathymetry (SDB) techniques have provided a cost-effective alternative to traditional in-situ bathymetric surveys. Furthermore, improved algorithms and the advancement of machine learning models have provided opportunity for higher quality bathymetric derivations. However, to date the relative accuracy and performance between traditional physics-based techniques, improved physics-based methods, and machine learning ensemble models have not been adequately quantified. In this study, nearshore bathymetry is derived from Sentinel-2 satellite imagery near La Parguera, Puerto Rico utilizing a traditional band-ratio algorithm, a band-ratio switching method, a random forest machine learning model, and the XGBoost machine learning model. The machine learning models returned comparable results and were markedly more accurate relative to other techniques; however, both machine learning models required an extensive training dataset. All models were constrained by environmental influences and image spatial resolution, which were assessed to be the limiting factors for routine use of satellite-derived bathymetry as a reliable method for hydrographic surveying.
Numéro de notice :	A2022-609
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/01490419.2022.2064572
Date de publication en ligne :	04/05/2022
En ligne :	https://doi.org/10.1080/01490419.2022.2064572
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101392

[article]

Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands / Niwen Li in Ecological indicators, vol 142 (September 2022)

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[article]
inEcological indicators > vol 142 (September 2022)

Titre :	Classification of pine wilt disease at different infection stages by diagnostic hyperspectral bands
Type de document :	Article/Communication
Auteurs :	Niwen Li, Auteur ; Langning Huo, Auteur ; Xiaoli Zhang, Auteur
Année de publication :	2022
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes IGN] aiguille [Termes IGN] analyse discriminante [Termes IGN] image hyperspectrale [Termes IGN] Pinus densiflora [Termes IGN] Pinus koraiensis [Termes IGN] santé des forêts [Termes IGN] signature spectrale [Termes IGN] surveillance forestière
Mots-clés libres :	competitive adaptive reweighted sampling = échantillonnage compétitif adaptatif pondéré
Résumé :	(auteur) Pine wilt disease (PWD) is a very destructive forest disease that causes the mortality of pine. The infected trees usually die within three months, and the disease spreads fast with the long-horned beetle as the medium if the infected trees are not removed from the forest in time. Therefore, detecting the infected trees at different infection stage, especially the early infection, is crucial for preventing PWD spread. This study aims to exhibit the spectral differences of the pine needles between healthy pines and infected pines at different infection stages and reveal the diagnostic spectral bands for classifying the different infected stage trees. We collected needle samples from healthy, early-, middle-, late-stage infected trees in a Japanese pine (Pinus densiflora) forest and a Korean pine (Pinus koraiensis) forest in northern China to explore the spectral and biochemical properties differences of these four classes, and selected the sensitive bands combining competitive adaptive reweighted sampling (CARS) and successive projections algorithm (SPA). The selected bands were used for the four infection stages classification by linear discriminant analysis (LDA) algorithm. The results show that Chlorophyll a, chlorophyll b, carotenoids, and moisture content decreases with the aggravation of infection. The green (510–530 nm), red-edge (680–760 nm), and short-wave infrared (1400–1420 nm and 1925–1965 nm) bands are the sensitive bands, and the overall accuracy is 77 % and 78 % for the Japanese pine and Korean pine respectively when using these bands for classifying healthy, early-, middle-, late-stage infected trees. The results demonstrate that physiological parameters including Chlorophyll a, chlorophyll b, carotenoids, and moisture content can be used as the diagnostic parameters of PWD, and the selected sensitive spectral bands are feasible for detecting the stress symptoms of the Japanese pine and Korean pine.
Numéro de notice :	A2022-617
Affiliation des auteurs :	non IGN
Thématique :	FORET/IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.ecolind.2022.109198
Date de publication en ligne :	26/07/2022
En ligne :	https://doi.org/10.1016/j.ecolind.2022.109198
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101374

[article]

Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images / Ekrem Saralioglu in Geocarto international, vol 37 n° 18 ([01/09/2022])

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[article]
inGeocarto international > vol 37 n° 18 [01/09/2022] . - pp 5433 - 5452

Titre :	Crowdsourcing-based application to solve the problem of insufficient training data in deep learning-based classification of satellite images
Type de document :	Article/Communication
Auteurs :	Ekrem Saralioglu, Auteur ; Oguz Gungor, Auteur
Année de publication :	2022
Article en page(s) :	pp 5433 - 5452
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] acquisition d'images [Termes IGN] apprentissage profond [Termes IGN] approche participative [Termes IGN] classification dirigée [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] couleur (variable spectrale) [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] étiquette [Termes IGN] image multibande [Termes IGN] OpenStreetMap [Termes IGN] pixel [Termes IGN] plateforme collaborative [Termes IGN] texture d'image [Termes IGN] Worldview
Résumé :	(auteur) In order to solve insufficient training data problem in remote sensing, a web platform was created so that registered users can generate labeled data for various classes in a dynamic structure. Users were asked to select representative pixel groups for the forest, hazelnut, shadow, soil, tea, and building classes with the polygon tool, and then assign a class label corresponding to each created polygon thanks to the help document displaying descriptive information regarding the locations, colors, textures and distributions of the classes in the image. Crowdsourcing was again used to test the accuracy of the tagged data produced by crowdsourcing. The created data set was overlaid with the original WV-2 image, and the correctness of the labels of the polygons was once visually verified. Finally, the WV-2 image, consisting of 40 patches, was classified with CNN and an average of over 95% accuracy was achieved.
Numéro de notice :	A2022-702
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/10106049.2021.1917006
Date de publication en ligne :	26/05/2021
En ligne :	https://doi.org/10.1080/10106049.2021.1917006
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101561

[article]

Deep image deblurring: A survey / Kaihao Zhang in International journal of computer vision, vol 130 n° 9 (September 2022)

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[article]
inInternational journal of computer vision > vol 130 n° 9 (September 2022) . - pp 2103 - 2130

Titre :	Deep image deblurring: A survey
Type de document :	Article/Communication
Auteurs :	Kaihao Zhang, Auteur ; Wenqi Ren, Auteur ; Wenhan Luo, Auteur ; et al., Auteur
Année de publication :	2022
Article en page(s) :	pp 2103 - 2130
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image optique [Termes IGN] accentuation d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] déconvolution [Termes IGN] estimation par noyau [Termes IGN] filtrage du bruit [Termes IGN] image floue [Termes IGN] qualité d'image [Termes IGN] réseau antagoniste génératif [Termes IGN] taxinomie [Termes IGN] vision par ordinateur
Résumé :	(auteur) Image deblurring is a classic problem in low-level computer vision with the aim to recover a sharp image from a blurred input image. Advances in deep learning have led to significant progress in solving this problem, and a large number of deblurring networks have been proposed. This paper presents a comprehensive and timely survey of recently published deep-learning based image deblurring approaches, aiming to serve the community as a useful literature review. We start by discussing common causes of image blur, introduce benchmark datasets and performance metrics, and summarize different problem formulations. Next, we present a taxonomy of methods using convolutional neural networks (CNN) based on architecture, loss function, and application, offering a detailed review and comparison. In addition, we discuss some domain-specific deblurring applications including face images, text, and stereo image pairs. We conclude by discussing key challenges and future research directions.
Numéro de notice :	A2022-638
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1007/s11263-022-01633-5
Date de publication en ligne :	25/06/2022
En ligne :	https://doi.org/10.1007/s11263-022-01633-5
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101444

[article]

Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)

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[article]
inComputers & geosciences > vol 166 (September 2022) . - n° 105191

Titre :	Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry
Type de document :	Article/Communication
Auteurs :	Wei Wang ; Wenbo Zhao, Auteur ; Bo Chai, Auteur ; et al., Auteur
Année de publication :	2022
Article en page(s) :	n° 105191
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] Chine [Termes IGN] discontinuité [Termes IGN] éboulement [Termes IGN] extraction de données [Termes IGN] front rocheux [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] matrice [Termes IGN] pente [Termes IGN] photogrammétrie aérienne [Termes IGN] profondeur [Termes IGN] risque naturel [Termes IGN] semis de points [Termes IGN] texture d'image
Résumé :	(auteur) Discontinuity extraction and interpretation of fractured masses is of high importance when analyzing rock slope stability. Regarding high-steep slopes, which are areas that are difficult to reach, traditional methods to obtain discontinuities, such as the sample window method (SWM), are unlikely to be implemented, resulting in challenges for the identification of potential rockfalls. With the development of the unmanned ariel vehicle (UAV) technology, discontinuity extraction can overcome by noncontact photogrammetry. However, there is still a lack of comprehensive and practical solutions to fulfill rockfall identification from field investigation to in-door analysis. For this purpose, a practical case study was carried out in Wanzhou, Chongqing, China, where a 400 m vertical rock slope prone to rockfall was collected as a typical example. The centimeter-level 3D Textured Digital Outcrop Model (TDOM) and dense Point Cloud (PC) were established using high-resolution photos acquired by nap-of-the-object photogrammetry. The discontinuity of the fractured mass was interpreted by fully taking advantage of both 2D images (texture information-dominated) and 3D PCs (depth information-dominated). Furthermore, a new parameter rock cavity rate (RCR) and the corresponding semiautomatic extraction method based on point clouds are proposed. Subsequently, the possibility of various failure modes and their joint combinations were determined by kinematic analysis. Finally, the rock slope stability was determined using a matrix that considers the slope mass rating (SMR) value and the parameter RCR. The proposed process flow and relevant techniques in this study provide an operable and practical solution for further application regarding discontinuity interpretation and potential rockfall identification on high-steep slopes.
Numéro de notice :	A2022-655
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
DOI :	10.1016/j.cageo.2022.105191
Date de publication en ligne :	08/07/2022
En ligne :	https://doi.org/10.1016/j.cageo.2022.105191
Format de la ressource électronique :	URL article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=101504

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