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Bulletin Imagerie

Bulletin-imagerie

Guide de bonnes pratiques sur la gestion des données de la recherche / Groupe de travail Atelier Données (France) (2021)

Public

Titre :	Guide de bonnes pratiques sur la gestion des données de la recherche
Type de document :	Monographie
Auteurs :	Groupe de travail Atelier Données (France), Auteur
Mention d'édition :	version 1.0 - Janvier 2021
Editeur :	Strasbourg : Université de Strasbourg
Année de publication :	2021
Langues :	Français (fre)
Descripteur :	[Vedettes matières IGN] Société de l'information [Termes descripteurs IGN] accès aux données [Termes descripteurs IGN] données environnementales [Termes descripteurs IGN] recherche scientifique [Termes descripteurs IGN] reproductibilité [Termes descripteurs IGN] réutilisation des données
Résumé :	(auteur) En janvier 2021, le groupe de travail publie le « guide de bonnes pratiques sur les données de la recherche » qui présente les différentes actions faites par les réseaux autour de la gestion des données. Ce guide reprend les 7 étapes définies dans le document « Cartographie des actions des réseaux métiers autour de la gestion des données » proposé en 2017.
Numéro de notice :	17615
Affiliation des auteurs :	non IGN
Thématique :	FORET/GEOMATIQUE/IMAGERIE/POSITIONNEMENT/SOCIETE NUMERIQUE
Nature :	Recueil / ouvrage collectif
DOI :	sans
date de publication en ligne :	26/01/2021
En ligne :	https://mi-gt-donnees.pages.math.unistra.fr/guide/00-introduction.html
Format de la ressource électronique :	URL
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96950

Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification / Fei Chen in Geocarto international, vol 36 n° 3 ([01/03/2021])

Public

[article]
inGeocarto international > vol 36 n° 3 [01/03/2021] . - pp 262 - 280

Titre :	Assessing spatial-temporal evolution processes and driving forces of karst rocky desertification
Type de document :	Article/Communication
Auteurs :	Fei Chen, Auteur ; Shijie Wang, Auteur ; Xiaoyong Bai, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 262 - 280
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse spatio-temporelle [Termes descripteurs IGN] carte d'utilisation du sol [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification et arbre de régression [Termes descripteurs IGN] désertification [Termes descripteurs IGN] données spatiotemporelles [Termes descripteurs IGN] image Landsat-OLI [Termes descripteurs IGN] image Landsat-TM [Termes descripteurs IGN] karst [Termes descripteurs IGN] lithologie
Résumé :	(auteur) Karst Rocky Desertification (KRD) has become the most serious ecological disaster in Southwest China. We used the data of Thematic Mapper (TM) images from 1990, 1995, 2000, 2004, and 2011 and the 2016 Operational Land Imager (OLI) image. These sensing images were pre-processed by removing non-karst areas based on lithology and cutting away the land types impossibly generating KRD from land use maps. Then, we used a Classification And Regression Tree (CART) to classify the KRD. We want to improve the interpretation accuracy of KRD through the above steps. The results were as follows: (1) The KRD experiences the evolution process of ‘first deterioration and then improvement’. The rate is −4.94 km2.a−1 over a period of 1990 to 2004, and the rate is 36.48 km2.a−1 from 2004 to 2016; (2) The most influential factors causing KRD formation are the lithology and the resident population density, with contribution rates of 30.17% and 25.86%, respectively.
Numéro de notice :	A2021-140
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1080/10106049.2019.1595175
date de publication en ligne :	18/07/2019
En ligne :	https://doi.org/10.1080/10106049.2019.1595175
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=97036

[article]

Characterizing urban land changes of 30 global megacities using nighttime light time series stacks / Qiming Zheng in ISPRS Journal of photogrammetry and remote sensing, Vol 173 (March 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 173 (March 2021) . - pp 10 - 23

Titre :	Characterizing urban land changes of 30 global megacities using nighttime light time series stacks
Type de document :	Article/Communication
Auteurs :	Qiming Zheng, Auteur ; Qihao Weng, Auteur ; Ke Wang, Auteur
Année de publication :	2021
Article en page(s) :	pp 10 - 23
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] aménagement foncier [Termes descripteurs IGN] analyse harmonique [Termes descripteurs IGN] cartographie urbaine [Termes descripteurs IGN] changement d'utilisation du sol [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] détection du bâti [Termes descripteurs IGN] éclairage public [Termes descripteurs IGN] image infrarouge [Termes descripteurs IGN] image VIIRS [Termes descripteurs IGN] mégalopole [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] zone urbaine
Résumé :	(auteur) Worldwide urbanization has brought about diverse types of urban land use and land cover (LULC) changes. The diversity of urban land changes, however, have been greatly under studied, since the major focus of past research has been on urban growth. In this study, we proposed a framework to characterize diverse urban land changes of 30 global megacities using monthly nighttime light time series from VIIRS data. First, we developed a Logistic-Harmonic model to fit VIIRS time series. Second, by leveraging the uniqueness of urban land change and nighttime light data, we incorporated temporal information of VIIRS time series and proposed a new classification scheme to produce monthly maps of built-up areas and to disentangle urban land changes into five categories. Third, we provided an in-depth analysis and comparison of urban land change patterns of the selected megacities. Results demonstrated that the Logistic-Harmonic model yielded a robust performance in fitting VIIRS time series. Temporal features based classification can not only significantly improve the mapping accuracy of built-up areas, especially for regions with heterogeneous built-up and non-built-up landscapes, but also promoted temporal consistency and classification efficiency. Urban land changes occurred in 51% of the built-up pixels of the megacities. Compared with urban growth, other types of urban land change, particularly land use intensification, contributed to an unexpectedly large proportion of the changes (83%). The findings of this study offer an insightful understanding on global urbanization processes in megacities, and evoke further investigation on the environmental and ecological implications of urban land changes.
Numéro de notice :	A2021-101
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE/URBANISME
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2021.01.002
date de publication en ligne :	16/01/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2021.01.002
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96873

[article]

Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon / Tamer ElGharbawi in ISPRS Journal of photogrammetry and remote sensing, Vol 173 (March 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 173 (March 2021) . - pp 1 - 9

Titre :	Damage detection using SAR coherence statistical analysis, application to Beirut, Lebanon
Type de document :	Article/Communication
Auteurs :	Tamer ElGharbawi, Auteur ; Fawzi Zarzoura, Auteur
Année de publication :	2021
Article en page(s) :	pp 1 - 9
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] Beyrouth [Termes descripteurs IGN] catastrophe [Termes descripteurs IGN] corrélation [Termes descripteurs IGN] décorrélation [Termes descripteurs IGN] dommage matériel [Termes descripteurs IGN] étude d'impact [Termes descripteurs IGN] filtre passe-haut [Termes descripteurs IGN] image radar moirée
Résumé :	(auteur) Early well-coordinated response during unexpected catastrophes can define the near future of the stricken regions. Beirut city, Lebanon, was one of the unfortunate regions to endure the horrific ordeal of an unexpected explosion that caused thousands of human casualties, billions of dollars’ worth of property damage, and destroyed its main maritime entry point. In this paper, we identify damaged regions and classify their severity using a simple and robust SAR correlation technique. We employ phase coherence and amplitude correlation of a SAR stack to estimate pixels’ damage probability using hypothesis testing. We use a spatial phase filter applied in the frequency domain to improve the estimated coherence by removing the spatial decorrelation component of the total estimated coherence. Using this filter improved the coherence of nearly 44.2% of pixels identified with coherence less than 0.25 in our study area. The estimated damaged regions are presented and compared against a damage map issued by Advanced Rapid Imaging and Analysis (ARIA) which shows an average agreement of 68.3%. Also, a fine agreement was observed when compared to optical satellite images.
Numéro de notice :	A2021-100
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2021.01.00
date de publication en ligne :	15/01/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2021.01.001
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96871

[article]

Enhanced trajectory estimation of mobile laser scanners using aerial images / Zille Hussnain in ISPRS Journal of photogrammetry and remote sensing, Vol 173 (March 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 173 (March 2021) . - pp 66 - 78

Titre :	Enhanced trajectory estimation of mobile laser scanners using aerial images
Type de document :	Article/Communication
Auteurs :	Zille Hussnain, Auteur ; Sander J. Oude Elberink, Auteur ; M. George Vosselman, Auteur
Année de publication :	2021
Article en page(s) :	pp 66 - 78
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] appariement de points [Termes descripteurs IGN] atténuation du signal [Termes descripteurs IGN] balayage laser [Termes descripteurs IGN] canyon urbain [Termes descripteurs IGN] centrale inertielle [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] erreur [Termes descripteurs IGN] image captée par drone [Termes descripteurs IGN] mesurage par GNSS [Termes descripteurs IGN] semis de points [Termes descripteurs IGN] trajectoire [Termes descripteurs IGN] trajet multiple
Résumé :	(auteur) Multipath effects and signal obstruction by buildings in urban canyons can lead to inaccurate GNSS measurements and therefore errors in the estimated trajectory of Mobile Laser Scanning (MLS) systems; consequently, derived point clouds are distorted and lose spatial consistency. We obtain decimetre-level trajectory accuracy making use of corresponding points between the MLS data and aerial images with accurate exterior orientations instead of using ground control points. The MLS trajectory is estimated based on observation equations resulting from these corresponding points, the original IMU observations, and soft constraints on the pitch and yaw rotations of the vehicle. We analyse the quality of the trajectory enhancement under several conditions where the experiments were designed to test the influence of the number and quality of corresponding points and to test different settings for a B-spline representation of the vehicle trajectory. The method was tested on two independently acquired MLS datasets in Rotterdam by enhancing the trajectories and evaluating them using checkpoints. The RMSE values of the original GNSS/IMU based Kalman filter results at the checkpoints were 0.26 m, 0.30 m, and 0.47 m for the X-, Y- and Z-coordinates in the first dataset and 1.10 m, 1.51 m, and 1.81 m in the second dataset. The latter dataset was recorded with a lower quality IMU in an area with taller buildings. After trajectory adjustment these RMSE values were reduced to 0.09 m, 0.11 m, and 0.16 m for the first dataset and 0.12 m, 0.14 m, and 0.18 m for the second dataset. The results confirmed that, if sufficient tie points between the point cloud and aerial imagery are available, the method supports geo-referencing of MLS point clouds in urban canyons with a near-decimetre accuracy.
Numéro de notice :	A2021-102
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2021.01.005
date de publication en ligne :	17/01/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2021.01.005
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96877

[article]

PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery / Xian Sun in ISPRS Journal of photogrammetry and remote sensing, Vol 173 (March 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 173 (March 2021) . - pp 50 - 65

Titre :	PBNet: Part-based convolutional neural network for complex composite object detection in remote sensing imagery
Type de document :	Article/Communication
Auteurs :	Xian Sun, Auteur ; Peijin Wang, Auteur ; Cheng Wang, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 50 - 65
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse contextuelle [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] Chine [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] objet géographique complexe [Termes descripteurs IGN] rectangle englobant minimum
Résumé :	(auteur) In recent years, deep learning-based algorithms have brought great improvements to rigid object detection. In addition to rigid objects, remote sensing images also contain many complex composite objects, such as sewage treatment plants, golf courses, and airports, which have neither a fixed shape nor a fixed size. In this paper, we validate through experiments that the results of existing methods in detecting composite objects are not satisfying enough. Therefore, we propose a unified part-based convolutional neural network (PBNet), which is specifically designed for composite object detection in remote sensing imagery. PBNet treats a composite object as a group of parts and incorporates part information into context information to improve composite object detection. Correct part information can guide the prediction of a composite object, thus solving the problems caused by various shapes and sizes. To generate accurate part information, we design a part localization module to learn the classification and localization of part points using bounding box annotation only. A context refinement module is designed to generate more discriminative features by aggregating local context information and global context information, which enhances the learning of part information and improve the ability of feature representation. We selected three typical categories of composite objects from a public dataset to conduct experiments to verify the detection performance and generalization ability of our method. Meanwhile, we build a more challenging dataset about a typical kind of complex composite objects, i.e., sewage treatment plants. It refers to the relevant information from authorities and experts. This dataset contains sewage treatment plants in seven cities in the Yangtze valley, covering a wide range of regions. Comprehensive experiments on two datasets show that PBNet surpasses the existing detection algorithms and achieves state-of-the-art accuracy.
Numéro de notice :	A2021-105
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2020.12.015
date de publication en ligne :	16/01/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2020.12.015
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96891

[article]

Robust unsupervised small area change detection from SAR imagery using deep learning / Xinzheng Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 173 (March 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 173 (March 2021) . - pp 79 - 94

Titre :	Robust unsupervised small area change detection from SAR imagery using deep learning
Type de document :	Article/Communication
Auteurs :	Xinzheng Zhang, Auteur ; Hang Su, Auteur ; Ce Zhang, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 79 - 94
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] algorithme de superpixels [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification floue [Termes descripteurs IGN] classification non dirigée [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de changement [Termes descripteurs IGN] échantillonnage [Termes descripteurs IGN] filtre de déchatoiement [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] ondelette [Termes descripteurs IGN] regroupement de données
Résumé :	(auteur) Small area change detection using synthetic aperture radar (SAR) imagery is a highly challenging task, due to speckle noise and imbalance between classes (changed and unchanged). In this paper, a robust unsupervised approach is proposed for small area change detection using deep learning techniques. First, a multi-scale superpixel reconstruction method is developed to generate a difference image (DI), which can suppress the speckle noise effectively and enhance edges by exploiting local, spatially homogeneous information. Second, a two-stage centre-constrained fuzzy c-means clustering algorithm is proposed to divide the pixels of the DI into changed, unchanged and intermediate classes with a parallel clustering strategy. Image patches belonging to the first two classes are then constructed as pseudo-label training samples, and image patches of the intermediate class are treated as testing samples. Finally, a convolutional wavelet neural network (CWNN) is designed and trained to classify testing samples into changed or unchanged classes, coupled with a deep convolutional generative adversarial network (DCGAN) to increase the number of changed class within the pseudo-label training samples. Numerical experiments on four real SAR datasets demonstrate the validity and robustness of the proposed approach, achieving up to 99.61% accuracy for small area change detection.
Numéro de notice :	A2021-103
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2021.01.004
date de publication en ligne :	17/01/2021
En ligne :	https://doi.org/10.1016/j.isprsjprs.2021.01.004
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96879

[article]

An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds / Fei Su in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 172 (February 2021) . - pp 114 - 131

Titre :	An anchor-based graph method for detecting and classifying indoor objects from cluttered 3D point clouds
Type de document :	Article/Communication
Auteurs :	Fei Su, Auteur ; Haihong Zhu, Auteur ; Taoyi Chen, Auteur
Année de publication :	2021
Article en page(s) :	pp 114 - 131
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Lasergrammétrie [Termes descripteurs IGN] adjacence [Termes descripteurs IGN] appariement de graphes [Termes descripteurs IGN] arc [Termes descripteurs IGN] bloc d'ancrage [Termes descripteurs IGN] classification orientée objet [Termes descripteurs IGN] données lidar [Termes descripteurs IGN] données localisées 3D [Termes descripteurs IGN] jeu de données localisées [Termes descripteurs IGN] méthode du maximum de vraisemblance (estimation) [Termes descripteurs IGN] noeud [Termes descripteurs IGN] objet 3D [Termes descripteurs IGN] orientation [Termes descripteurs IGN] positionnement en intérieur [Termes descripteurs IGN] semis de points
Résumé :	(auteur) Most of the existing 3D indoor object classification methods have shown impressive achievements on the assumption that all objects are oriented in the upward direction with respect to the ground. To release this assumption, great effort has been made to handle arbitrarily oriented objects in terrestrial laser scanning (TLS) point clouds. As one of the most promising solutions, anchor-based graphs can be used to classify freely oriented objects. However, this approach suffers from missing anchor detection since valid detection relies heavily on the completeness of an anchor’s point clouds and is sensitive to missing data. This paper presents an anchor-based graph method to detect and classify arbitrarily oriented indoor objects. The anchors of each object are extracted by the structurally adjacent relationship among parts instead of the parts’ geometric metrics. In the case of adjacency, an anchor can be correctly extracted even with missing parts since the adjacency between an anchor and other parts is retained irrespective of the area extent of the considered parts. The best graph matching is achieved by finding the optimal corresponding node-pairs in a super-graph with fully connecting nodes based on maximum likelihood. The performances of the proposed method are evaluated with three indicators (object precision, object recall and object F1-score) in seven datasets. The experimental tests demonstrate the effectiveness of dealing with TLS point clouds, RGBD point clouds and Panorama RGBD point clouds, resulting in performance scores of approximately 0.8 for object precision and recall and over 0.9 for chair precision and table recall.
Numéro de notice :	A2021-087
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2020.12.007
date de publication en ligne :	29/12/2020
En ligne :	https://doi.org/10.1016/j.isprsjprs.2020.12.007
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96852

[article]

Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach / Frank S. Marzano in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)

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[article]
inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 915 - 928

Titre :	Coastal water remote sensing from sentinel-2 satellite data using physical, statistical, and neural network retrieval approach
Type de document :	Article/Communication
Auteurs :	Frank S. Marzano, Auteur ; Michele Iacobelli, Auteur ; Massimo Orlandi, Auteur ; et al., Auteur
Année de publication :	2021
Article en page(s) :	pp 915 - 928
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] Adriatique, mer [Termes descripteurs IGN] bathymétrie [Termes descripteurs IGN] chlorophylle [Termes descripteurs IGN] correction atmosphérique [Termes descripteurs IGN] couleur de l'océan [Termes descripteurs IGN] eaux côtières [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] incertitude spectrale [Termes descripteurs IGN] matière organique [Termes descripteurs IGN] Méditerranée, mer [Termes descripteurs IGN] réseau neuronal artificiel
Résumé :	(auteur) Recent optical remote sensing satellite missions, such as Sentinel-2 with the MultiSpectral Imager (MSI) onboard, allow the estimation of coastal water key parameters with very high spatial resolutions (down to 10 m). In this article, multiple approaches are proposed for retrieving chlorophyll-a (Chl-a) and total suspended matter (TSM) along the Adriatic and Tyrrhenian coasts in Italy, using both empirical and model-based frameworks to design regressive and neural network (NN) estimation methods. The latter proves to be more accurate on a regional scale, where standard ocean color physical models exhibit high uncertainty in their local parameterization due to the complex spectral characteristics of the observed scene. Retrieval results are encouraging for Chl-a with a coefficient of determination R2 up to 0.72 with a root-mean-square error (RMSE) of 0.33 mg m−3 , using an empirical NN. The TSM algorithms exhibit higher uncertainty, mainly due to scarcity of in situ measurements and model parameterizations, with R2=0.52 and RMSE = 1.95 g/m 3 using NNs. The bio-optical model, used for the development of model-based algorithms, shows some inadequacies in representing the inherent and apparent optical properties for the case study areas, especially considering the different spectral features between the oligotrophic Tyrrhenian Sea and the eutrophic Adriatic Sea. This study confirms the potential of Sentinel-2 MSI products for coastal water monitoring, but it also highlights key issues to be further tackled such as the atmospheric correction impact, the need of reliable in situ measurements, and possible bathymetry effects near the shores.
Numéro de notice :	A2021-110
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1109/TGRS.2020.2980941
date de publication en ligne :	09/12/2020
En ligne :	https://doi.org/10.1109/TGRS.2020.2980941
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96912

[article]

Comprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1 / Matthias Schlögl in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)

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[article]
inISPRS Journal of photogrammetry and remote sensing > Vol 172 (February 2021) . - pp 132 - 146

Titre :	Comprehensive time-series analysis of bridge deformation using differential satellite radar interferometry based on Sentinel-1
Type de document :	Article/Communication
Auteurs :	Matthias Schlögl, Auteur ; Barbara Widhalm, Auteur ; Michael Avian, Auteur
Année de publication :	2021
Article en page(s) :	pp 132 - 146
Note générale :	bibliographie
Langues :	Anglais (eng)
Descripteur :	[Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] coin réflecteur [Termes descripteurs IGN] déformation d'édifice [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] image Sentinel-SAR [Termes descripteurs IGN] interféromètrie par radar à antenne synthétique [Termes descripteurs IGN] lissage de données [Termes descripteurs IGN] pont [Termes descripteurs IGN] série temporelle [Termes descripteurs IGN] surveillance d'ouvrage [Termes descripteurs IGN] variation saisonnière [Termes descripteurs IGN] Vienne (capitale Autriche)
Résumé :	(auteur) We present a comprehensive methodological framework for structural deformation monitoring of critical infrastructure assets based on differential SAR interferometry. By employing persistent scatterer interferometry, deformation time series in line-of-sight are derived from freely available Sentinel-1 single look complex products. These raw time series are analysed and refined using an extensive post-processing chain to obtain daily rates for vertical and horizontal deformation components. The post-processing includes cleaning, smoothing and a temperature correction to account for different sensing times in ascending and descending orbits. Longitudinal clustering of time series is used to reveal spatial patterns in the single epoch deformation series. Seasonal trend decomposition of the aggregated time series is performed to separate deformation trends from seasonal deformations. The applicability of the framework is showcased at the example of an integral concrete bridge located in the port of Vienna. Results are validated against in situ deformation measurements. The presented framework constitutes a blueprint for the continuous monitoring of critical infrastructure assets using satellite interferometry, which may supplement conventional structural health monitoring.
Numéro de notice :	A2021-088
Affiliation des auteurs :	non IGN
Thématique :	IMAGERIE/POSITIONNEMENT
Nature :	Article
nature-HAL :	ArtAvecCL-RevueIntern
DOI :	10.1016/j.isprsjprs.2020.12.001
date de publication en ligne :	30/12/2020
En ligne :	https://doi.org/10.1016/j.isprsjprs.2020.12.001
Format de la ressource électronique :	url article
Permalink :	https://documentation.ensg.eu/index.php?lvl=notice_display&id=96855

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IGN

Centre dedocumentation
scientifique

Accueil

Sélection de la langue

Adresse

Se connecter

Actualité

L'actu-47

Agenda

Informations pratiques

Bulletin Imagerie

Bulletin-imagerie

IGN / ENSG

L'IGN a pour vocation

Accès directs

2014 IGN

IGN

Centre dedocumentationscientifique

Accueil

Sélection de la langue

Adresse

Se connecter

Actualité

L'actu-47

Agenda

Informations pratiques

Bulletin Imagerie

Bulletin-imagerie

IGN / ENSG

L'IGN a pour vocation

Accès directs

2014 IGN

Centre dedocumentation
scientifique