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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)  

Public [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 [article]

Correntropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing / Xiaorun Li in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1453 - 1471 Titre : Correntropy-based spatial-spectral robust sparsity-regularized hyperspectral unmixing Type de document : Article/Communication Auteurs : Xiaorun Li, Auteur ; Risheng Huang, Auteur ; Liaolying Zhao, Auteur Année de publication : 2021 Article en page(s) : pp 1453 - 1471 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse des mélanges spectraux [Termes descripteurs IGN] corrélation [Termes descripteurs IGN] entropie [Termes descripteurs IGN] image hyperspectrale [Termes descripteurs IGN] méthode robuste [Termes descripteurs IGN] signature spectrale Résumé : (auteur) Hyperspectral unmixing (HU) is a crucial technique for exploiting remotely sensed hyperspectral data, which aims at estimating a set of spectral signatures, called endmembers and their corresponding proportions, called abundances. The performance of HU is often seriously degraded by various kinds of noise existing in hyperspectral images (HSIs). Most of existing robust HU methods are based on the assumption that noise or outlier only exists in one kind of formulation, e.g., band noise or pixel noise. However, in real-world applications, HSIs are unavoidably corrupted by noisy bands and noisy pixels simultaneously, which require robust HU in both the spatial dimension and spectral dimension. Meanwhile, the sparsity of abundances is an inherent property of HSIs and different regions in an HSI may possess various sparsity levels across locations. This article proposes a correntropy-based spatial-spectral robust sparsity-regularized unmixing model to achieve 2-D robustness and adaptive weighted sparsity constraint for abundances simultaneously. The updated rules of the proposed model are efficient to be implemented and carried out by a half-quadratic technique. The experimental results obtained by both synthetic and real hyperspectral data demonstrate the superiority of the proposed method compared to the state-of-the-art methods. Numéro de notice : A2021-116 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2999936 date de publication en ligne : 16/06/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2999936 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96930 [article]

Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control / Adolfo Lozano-Tello in European journal of remote sensing, vol 54 n° 1 (2021)  

Public [article]  inEuropean journal of remote sensing > vol 54 n° 1 (2021) . - pp 1 - 12 Titre : Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control Type de document : Article/Communication Auteurs : Adolfo Lozano-Tello, Auteur ; Marcos Fernández-Sellers, Auteur ; Elia Quirós, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1 - 12 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes descripteurs IGN] analyse diachronique [Termes descripteurs IGN] apprentissage automatique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] classification pixellaire [Termes descripteurs IGN] Estrémadure (Espagne) [Termes descripteurs IGN] image Sentinel-MSI [Termes descripteurs IGN] Normalized Difference Vegetation Index [Termes descripteurs IGN] politique agricole commune [Termes descripteurs IGN] réseau neuronal artificiel [Termes descripteurs IGN] surface cultivée [Termes descripteurs IGN] surveillance agricole Résumé : (auteur) The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results. Numéro de notice : A2021-138 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/22797254.2020.1858723 date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.1080/22797254.2020.1858723 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97012 [article]

Deep traffic light detection by overlaying synthetic context on arbitrary natural images / Jean Pablo Vieira de Mello in Computers and graphics, vol 94 n° 1 (February 2021)  

Public [article]  inComputers and graphics > vol 94 n° 1 (February 2021) . - pp 76 - 86 Titre : Deep traffic light detection by overlaying synthetic context on arbitrary natural images Type de document : Article/Communication Auteurs : Jean Pablo Vieira de Mello, Auteur ; Lucas Tabelini, Auteur ; Rodrigo F. Berriel, Auteur Année de publication : 2021 Article en page(s) : pp 76 - 86 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] analyse d'image orientée objet [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] détection d'objet [Termes descripteurs IGN] échantillonnage d'image [Termes descripteurs IGN] feu de circulation [Termes descripteurs IGN] image à haute résolution [Termes descripteurs IGN] navigation autonome [Termes descripteurs IGN] signalisation routière [Termes descripteurs IGN] trafic routier Résumé : (auteur) Deep neural networks come as an effective solution to many problems associated with autonomous driving. By providing real image samples with traffic context to the network, the model learns to detect and classify elements of interest, such as pedestrians, traffic signs, and traffic lights. However, acquiring and annotating real data can be extremely costly in terms of time and effort. In this context, we propose a method to generate artificial traffic-related training data for deep traffic light detectors. This data is generated using basic non-realistic computer graphics to blend fake traffic scenes on top of arbitrary image backgrounds that are not related to the traffic domain. Thus, a large amount of training data can be generated without annotation efforts. Furthermore, it also tackles the intrinsic data imbalance problem in traffic light datasets, caused mainly by the low amount of samples of the yellow state. Experiments show that it is possible to achieve results comparable to those obtained with real training data from the problem domain, yielding an average mAP and an average F1-score which are each nearly 4 p.p. higher than the respective metrics obtained with a real-world reference model. Numéro de notice : A2021-151 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cag.2020.09.012 date de publication en ligne : 09/10/2020 En ligne : https://doi.org/10.1016/j.cag.2020.09.012 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97027 [article]

Fully convolutional neural network for impervious surface segmentation in mixed urban environment / Joseph McGlinchy in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 2 (February 2021) . - pp 117 - 123 Titre : Fully convolutional neural network for impervious surface segmentation in mixed urban environment Type de document : Article/Communication Auteurs : Joseph McGlinchy, Auteur ; Brian Muller, Auteur ; Brian Johnson, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 117 - 123 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] croissance urbaine [Termes descripteurs IGN] Denver [Termes descripteurs IGN] exactitude des données [Termes descripteurs IGN] image multibande [Termes descripteurs IGN] image Worldview [Termes descripteurs IGN] milieu urbain [Termes descripteurs IGN] segmentation [Termes descripteurs IGN] surface imperméable Résumé : (Auteur) The urgency of creating appropriate, high-resolution data products such as impervious cover information has increased as cities face rapid growth as well as climate change and other environmental challenges. This work explores the use of fully convolutional neural networks (FCNNs )—specifically UNet with a ResNet-152 encoder—in mapping impervious surfaces at the pixel level from WorldView-2 in a mixed urban/residential environment. We investigate three-, four-, and eight-band multispectral inputs to the FCNN. Resulting maps are promising in both qualitative and quantitative assessment when compared to automated land use/land cover products. Accuracy was assessed by F1 and average precision (AP) scores, as well as receiver operating characteristic curves, with area under the curve (AUC ) used as an additional accuracy metric. The four-band model shows the highest average test-set accuracies (F1, AP, and AUC of 0.709, 0.82, and 0.807, respectively), with higher AP and AUC than the automated land use/land cover products, indicating the utility of the blue-green-red-infrared channels for the FCNN. Improved performance was seen in residential areas, with worse performance in more densely developed areas. Numéro de notice : A2021-099 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.2.117 date de publication en ligne : 01/02/2021 En ligne : https://doi.org/10.14358/PERS.87.2.117 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97045 [article]

Geo-spatially modelling dengue epidemics in urban cities: a case study of Lahore, Pakistan / Muhammad Imran in Geocarto international, vol 36 n° 2 ([01/02/2021])  

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GTP-PNet: A residual learning network based on gradient transformation prior for pansharpening / Hao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)  

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Influence of flight altitude and control points in the georeferencing of images obtained by unmanned aerial vehicle / Lucas Santos Santana in European journal of remote sensing, vol 54 n° 1 (2021)  

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Monitoring the spatiotemporal dynamics of urban green space and Its impacts on thermal environment in Shenzhen city from 1978 to 2018 with remote sensing data / Yue Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)  

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Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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Optimizing flood mapping using multi-synthetic aperture radar images for regions of the lower mekong basin in Vietnam / Vu Anh Tuan in European journal of remote sensing, vol 54 n° 1 (2021)  

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Reclaimed-airport surface-deformation monitoring by improved permanent-scatterer interferometric synthetic-aperture radar: a case study of Shenzhen Bao'an international airport, China / Lu Miao in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 2 (February 2021)  

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SAR image speckle reduction based on nonconvex hybrid total variation model / Yuli Sun in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)  

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Semi-supervised joint learning for hand gesture recognition from a single color image / Chi Xu in Sensors, vol 21 n° 3 (February 2021)  

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Spruce budworm tree host species distribution and abundance mapping using multi-temporal Sentinel-1 and Sentinel-2 satellite imagery / Rajeev Bhattarai in ISPRS Journal of photogrammetry and remote sensing, Vol 172 (February 2021)  

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