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ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction / Litu Rout in IEEE Transactions on geoscience and remote sensing, vol 58 n° 6 (June 2020)
[article]
Titre : ALERT: adversarial learning with expert regularization using Tikhonov operator for missing band reconstruction Type de document : Article/Communication Auteurs : Litu Rout, Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage dirigé
[Termes IGN] bande spectrale
[Termes IGN] cohérence géométrique
[Termes IGN] correction d'image
[Termes IGN] dégradation d'image
[Termes IGN] image Worldview
[Termes IGN] pollution acoustique
[Termes IGN] qualité d'image
[Termes IGN] régularisation de TychonoffRésumé : (auteur) The Earth observation using remote sensing is one of the most important technologies to assimilate key attributes about the Earth’s surface. To achieve tangible consequence, the internal building blocks of such a complex system must operate flawlessly. However, due to a dynamically changing environment, degradation in sensor electronics, and extreme weather condition remotely sensed images often miss essential information. As the sensors operate over several years in space the likelihood of sensor degradation persists. This results in commonly observed issues, such as stripe noise, missing partial data, and missing band. Various ground-based solutions have been developed to address these technological bottlenecks individually. In this article, we devise a method, which we call ALERT, to tackle missing band reconstruction. The proposed method reconstructs the missing band with the sole supervision of spectral and spatial priors. We compare the proposed framework with state-of-the-art methods and show compelling improvement both qualitatively and quantitatively. We provide both theoretical and empirical evidence of better performance by regularized adversarial learning as compared to complete supervision. Furthermore, we propose a new residual-dense-block (RDB) module to preserve geometric fidelity and assist in efficient gradient flow. We show that ALERT captures essential features such that the spatial and spectral characteristics of the reconstructed band remains preserved. To critically analyze the generalization we test the performance on two different satellite data sets: Resourcesat-2A and WorldView-2. As per our extensive experimentation, the proposed method achieves 20.72%, 13.81%, 1.05%, 15.91%, and 2.94% improvement in the root mean square error (RMSE), SAM, SSIM, PSNR, and SRE, respectively, over the state-of-the-art model. Numéro de notice : A2020-285 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2963818 Date de publication en ligne : 16/01/2020 En ligne : https://doi.org/10.1109/TGRS.2019.2963818 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95108
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 6 (June 2020)[article]Pedestrian network generation based on crowdsourced tracking data / Xue Yang in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)
[article]
Titre : Pedestrian network generation based on crowdsourced tracking data Type de document : Article/Communication Auteurs : Xue Yang, Auteur ; Luliang Tang, Auteur ; Chang Ren, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1051 - 1074 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique web
[Termes IGN] approche participative
[Termes IGN] base de données multi-représentation
[Termes IGN] correction géométrique
[Termes IGN] correction topographique
[Termes IGN] dimension fractale
[Termes IGN] données localisées des bénévoles
[Termes IGN] estimation par noyau
[Termes IGN] mobilité urbaine
[Termes IGN] navigation pédestre
[Termes IGN] regroupement de pointsRésumé : (auteur) Pedestrian networks play an important role in various applications, such as pedestrian navigation services and mobility modeling. This paper presents a novel method to extract pedestrian networks from crowdsourced tracking data based on a two-layer framework. This framework includes a walking pattern classification layer and a pedestrian network generation layer. In the first layer, we propose a multi-scale fractal dimension (MFD) algorithm in order to recognize the two different types of walking patterns: walking with a clear destination (WCD) or walking without a clear destination (WOCD). In the second layer, we generate the pedestrian network by combining the pedestrian regions and pedestrian paths. The pedestrian regions are extracted based on a modified connected component analysis (CCA) algorithm from the WOCD traces. We generate the pedestrian paths using a kernel density estimation (KDE)-based point clustering algorithm from the WCD traces. The pedestrian network generation results using two actual crowdsourced datasets show that the proposed method has good performance in both geometrical correctness and topological correctness. Numéro de notice : A2020-207 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/13658816.2019.1702197 Date de publication en ligne : 09/12/2019 En ligne : https://doi.org/10.1080/13658816.2019.1702197 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94888
in International journal of geographical information science IJGIS > vol 34 n° 5 (May 2020) . - pp 1051 - 1074[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 079-2020051 RAB Revue Centre de documentation En réserve L003 Disponible Refractive two-view reconstruction for underwater 3D vision / François Chadebecq in International journal of computer vision, vol 128 n° 5 (May 2020)
[article]
Titre : Refractive two-view reconstruction for underwater 3D vision Type de document : Article/Communication Auteurs : François Chadebecq, Auteur ; Francisco Vasconcelos, Auteur ; René Lacher, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 1101 - 1117 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s)
[Termes IGN] correction d'image
[Termes IGN] estimation de pose
[Termes IGN] étalonnage d'instrument
[Termes IGN] image sous-marine
[Termes IGN] reconstruction 3D
[Termes IGN] réfraction de l'eau
[Termes IGN] structure-from-motion
[Termes IGN] temps de pose
[Termes IGN] vision stéréoscopiqueRésumé : (auteur) Recovering 3D geometry from cameras in underwater applications involves the Refractive Structure-from-Motion problem where the non-linear distortion of light induced by a change of medium density invalidates the single viewpoint assumption. The pinhole-plus-distortion camera projection model suffers from a systematic geometric bias since refractive distortion depends on object distance. This leads to inaccurate camera pose and 3D shape estimation. To account for refraction, it is possible to use the axial camera model or to explicitly consider one or multiple parallel refractive interfaces whose orientations and positions with respect to the camera can be calibrated. Although it has been demonstrated that the refractive camera model is well-suited for underwater imaging, Refractive Structure-from-Motion remains particularly difficult to use in practice when considering the seldom studied case of a camera with a flat refractive interface. Our method applies to the case of underwater imaging systems whose entrance lens is in direct contact with the external medium. By adopting the refractive camera model, we provide a succinct derivation and expression for the refractive fundamental matrix and use this as the basis for a novel two-view reconstruction method for underwater imaging. For validation we use synthetic data to show the numerical properties of our method and we provide results on real data to demonstrate its practical application within laboratory settings and for medical applications in fluid-immersed endoscopy. We demonstrate our approach outperforms classic two-view Structure-from-Motion method relying on the pinhole-plus-distortion camera model. Numéro de notice : A2020-508 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s11263-019-01218-9 Date de publication en ligne : 18/11/2019 En ligne : https://doi.org/10.1007/s11263-019-01218-9 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96972
in International journal of computer vision > vol 128 n° 5 (May 2020) . - pp 1101 - 1117[article]Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images / Hao Cui in IEEE Transactions on geoscience and remote sensing, vol 58 n° 4 (April 2020)
[article]
Titre : Multiscale Intensity Propagation to Remove Multiplicative Stripe Noise From Remote Sensing Images Type de document : Article/Communication Auteurs : Hao Cui, Auteur ; Peng Jia, Auteur ; Guo Zhang, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 2308 - 2323 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] analyse des correspondances
[Termes IGN] bande spectrale
[Termes IGN] capteur à balayage
[Termes IGN] correction d'image
[Termes IGN] dégradation d'image
[Termes IGN] délignage
[Termes IGN] filtrage du bruit
[Termes IGN] filtrage du rayonnement
[Termes IGN] image hyperspectrale
[Termes IGN] intensité lumineuse
[Termes IGN] itération
[Termes IGN] méthode robuste
[Termes IGN] pollution acoustiqueRésumé : (auteur) Sensor instability, dark currents, and other factors often cause stripe noise corruption in hyperspectral remote sensing images and severely limit their application in practical purposes. Previous studies have proposed numerous destriping algorithms that have yielded impressive results. Although most destriping algorithms are based on the premise of additive noise, a few studies have focused directly on multiplicative stripe noise. This article fully analyzes the characteristics of the stripe noise of OHS-01 images and proposes a multiplicative stripe noise removal method. Specifically, stripe noise is tackled by performing radiometric normalization of different columns in the image. First, the relative gain coefficients of adjacent columns are separated based on prior knowledge. Second, the local relative intensity correspondence of the image columns are established by means of intensity propagation, intensity connection, and so on. Finally, the above-mentioned process is iterated in multiscale space, and the accumulated gain correction coefficient maps were used to correct the radiation of the original image. The results of extensive experiments on simulated and real remote sensing image data demonstrate that the proposed method can, in most cases, yield desirable results. In certain cases, the results are even better, visually, and quantitatively, than those obtained using classical algorithms. Moreover, the proposed method has high robustness and efficiency. Thus, it can conform to the requirements of engineering applications. Numéro de notice : A2020-194 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2947599 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2947599 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94861
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 4 (April 2020) . - pp 2308 - 2323[article]Improving operational radar rainfall estimates using profiler observations over complex terrain in Northern California / Haonan Chen in IEEE Transactions on geoscience and remote sensing, vol 58 n° 3 (March 2020)
[article]
Titre : Improving operational radar rainfall estimates using profiler observations over complex terrain in Northern California Type de document : Article/Communication Auteurs : Haonan Chen, Auteur ; Robert Cifelli, Auteur ; Allen White, Auteur Année de publication : 2020 Article en page(s) : pp 1821 - 1832 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Californie (Etats-Unis)
[Termes IGN] correction d'image
[Termes IGN] données radar
[Termes IGN] erreur d'approximation
[Termes IGN] faisceau
[Termes IGN] montagne
[Termes IGN] orographie
[Termes IGN] précipitation
[Termes IGN] prévision météorologique
[Termes IGN] télédétection en hyperfréquence
[Termes IGN] télédétection réflective
[Termes IGN] visée verticaleRésumé : (Auteur) Quantitative precipitation estimation (QPE) using operational weather radars in the western United States is still a challenging issue due to the beam blockage in the mountainous areas and complex rainfall microphysics induced by the orographic enhancement. This article aims to improve operational radar rainfall estimates in complex terrain by incorporating auxiliary remote sensing observations. An innovative vertical profile of reflectivity (VPR) correction scheme is developed for operational radar using observations from multiple vertically pointing profilers to represent the vertical structure of precipitation at various locations. A demonstration study in the Russian River basin in Northern California is detailed. Results show that the QPE performance is significantly improved after VPR correction, and this new VPR correction approach is superior to the conventional approach currently applied in the operational radar rainfall system. The normalized standard error of hourly rainfall estimates for the two precipitation events presented in this article is improved by ~20% after applying the proposed VPR correction scheme. Numéro de notice : A2020-090 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2949214 Date de publication en ligne : 12/11/2019 En ligne : https://doi.org/10.1109/TGRS.2019.2949214 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94664
in IEEE Transactions on geoscience and remote sensing > vol 58 n° 3 (March 2020) . - pp 1821 - 1832[article]Optimising drone flight planning for measuring horticultural tree crop structure / Yu-Hsuan Tu in ISPRS Journal of photogrammetry and remote sensing, vol 160 (February 2020)PermalinkSome thoughts on measuring earthquake deformation using optical imagery / Min Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)PermalinkPermalinkLe plug-in ACYOTB : l'orthorectification open source de précision / Valerio Baiocchi in Géomatique expert, n° 132-133 (janvier - septembre 2020)PermalinkSimulation d’éclairements des surfaces ombrées en zone urbaine par transfert radiatif 3D (modèle DART) / Yulu Xi (2020)PermalinkOn the value of corner reflectors and surface models in InSAR precise point positioning / Mengshi Yang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)PermalinkGeometric accuracy improvement of WorldView‐2 imagery using freely available DEM data / Mateo Gašparović in Photogrammetric record, vol 34 n° 167 (September 2019)PermalinkLettre : Existe-t-il des relations formelles entre coefficients de diffusion radar et facteurs de réflectance en optique ? / Jean-Paul Rudant in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkTélédétection radar : de l'image d'intensité initiale au choix du mode de calibration des coefficients de diffusion / Jean-Paul Rudant in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)PermalinkImproved camera distortion correction and depth estimation for lenslet light field camera / Changkun Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)PermalinkArchival aerial photogrammetric surveys, a data source to study land use/cover evolution over the last century : opportunities and issues / Arnaud Le Bris (2019)PermalinkCorrecting rural building annotations in OpenStreetMap using convolutional neural networks / John E. Vargas-Muñoz in ISPRS Journal of photogrammetry and remote sensing, vol 147 (January 2019)PermalinkImproving the spatial bias correction algorithm in SMOS image reconstruction processor : validation of soil moisture retrievals with in situ data / Ali Khazaal in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)PermalinkInfluences of environmental loading corrections on the nonlinear variations and velocity uncertainties for the reprocessed global positioning system height time series of the crustal movement observation network of China / Peng Yuan in Remote sensing, vol 10 n° 6 (June 2018)PermalinkCartographie des déformations de surface sur l’île de Taiwan par interférométrie RADAR Sentinel-1 / Miloud Fekaouni (2018)PermalinkDétection de changement par imagerie radar sur les zones naturelles et agricoles en milieu tropical / Jérôme Lebreton (2018)PermalinkDevelopment of a Protocol to Convert and Manage Underground Infrastructure Maps into Geographic Information Systems (GIS) Format / Guillemette Fonteix (2018)PermalinkPermalinkPermalinkPermalink