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Using quantum optical sensors for determining the Earth’s gravity field from space / Jurgen Müller in Journal of geodesy, vol 94 n° 8 (August 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 8 (August 2020) . - n° 71 Titre : Using quantum optical sensors for determining the Earth’s gravity field from space Type de document : Article/Communication Auteurs : Jurgen Müller, Auteur ; Hu Wu, Auteur Année de publication : 2020 Article en page(s) : n° 71 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géodésie physique [Termes descripteurs IGN] capteur optique [Termes descripteurs IGN] champ de pesanteur terrestre [Termes descripteurs IGN] données GOCE [Termes descripteurs IGN] données GRACE [Termes descripteurs IGN] gradient [Termes descripteurs IGN] gradiomètre [Termes descripteurs IGN] gravimétrie spatiale [Termes descripteurs IGN] horloge du satellite [Termes descripteurs IGN] incertitude temporelle [Termes descripteurs IGN] longueur d'onde [Termes descripteurs IGN] onde myriamétrique [Termes descripteurs IGN] optique quantique Résumé : (auteur) Quantum optical technology provides an opportunity to develop new kinds of gravity sensors and to enable novel measurement concepts for gravimetry. Two candidates are considered in this study: the cold atom interferometry (CAI) gradiometer and optical clocks. Both sensors show a high sensitivity and long-term stability. They are assumed on board of a low-orbit satellite like gravity field and steady-state ocean circulation explorer (GOCE) and gravity recovery and climate experiment (GRACE) to determine the Earth’s gravity field. Their individual contributions were assessed through closed-loop simulations which rigorously mapped the sensors’ sensitivities to the gravity field coefficients. Clocks, which can directly obtain the gravity potential (differences) through frequency comparison, show a high sensitivity to the very long-wavelength gravity field. In the GRACE orbit, clocks with an uncertainty level of 1.0×10−18 are capable to retrieve temporal gravity signals below degree 12, while 1.0×10−17 clocks are useful for detecting the signals of degree 2 only. However, it poses challenges for clocks to achieve such uncertainties in a short time. In space, the CAI gradiometer is expected to have its ultimate sensitivity and a remarkable stability over a long time (measurements are precise down to very low frequencies). The three diagonal gravity gradients can properly be measured by CAI gradiometry with a same noise level of 5.0 mE/Hz−−−√. They can potentially lead to a 2–5 times better solution of the static gravity field than that of GOCE above degree and order 50, where the GOCE solution is mainly dominated by the gradient measurements. In the lower degree part, benefits from CAI gradiometry are still visible, but there, solutions from GRACE-like missions are superior. Numéro de notice : A2020-537 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s00190-020-01401-8 date de publication en ligne : 24/07/2020 En ligne : https://doi.org/10.1007/s00190-020-01401-8 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95730 [article]

Low level feature detection in SAR images / Chenguang Liu (2020)  

Public Titre : Low level feature detection in SAR images Type de document : Thèse/HDR Auteurs : Chenguang Liu, Auteur ; Florence Tupin, Directeur de thèse ; Yann Gousseau, Directeur de thèse Editeur : Paris [France] : Télécom ParisTech Année de publication : 2020 Importance : 138 p. Format : 21 x 30 cm Note générale : bibliographie Thèse de doctorat de l’Institut Polytechnique de Paris préparée à Télécom Paris, Spécialité de doctorat : Signal, Images, Automatique et robotique Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] détection de contours [Termes descripteurs IGN] gradient [Termes descripteurs IGN] image radar moirée [Termes descripteurs IGN] modèle de Markov [Termes descripteurs IGN] segment de droite Résumé : (auteur) In this thesis we develop low level feature detectors for Synthetic Aperture Radar (SAR) images to facilitate the joint use of SAR and optical data. Line segments and edges are very important low level features in images which can be used for many applications like image analysis, image registration and object detection. Contrarily to the availability of many efficient low level feature detectors dedicated to optical images, there are very few efficient line segment detector and edge detector for SAR images mostly because of the strong multiplicative noise. In this thesis we develop a generic line segment detector and an efficient edge detector for SAR images.The proposed line segment detector which is named as LSDSAR, is based on a Markovian a contrario model and the Helmholtz principle, where line segments are validated according to their meaningfulness. More specifically, a line segment is validated if its expected number of occurences in a random image under the hypothesis of the Markovian a contrario model is small. Contrarily to the usual a contrario approaches, the Markovian a contrario model allows strong filtering in the gradient computation step, since dependencies between local orientations of neighbouring pixels are permitted thanks to the use of a first order Markov chain. The proposed Markovian a contrario model based line segment detector LSDSAR benefit from the accuracy and efficiency of the new definition of the background model, indeed, many true line segments in SAR images are detected with a control of the number of false detections. Moreover, very little parameter tuning is required in the practical applications of LSDSAR. The second work of this thesis is that we propose a deep learning based edge detector for SAR images. The contributions of the proposed edge detector are two fold: 1) under the hypothesis that both optical images and real SAR images can be divided into piecewise constant areas, we propose to simulate a SAR dataset using optical dataset; 2) we propose to train a classical CNN (convolutional neural network) edge detector, HED, directly on the graident fields of images. This, by using an adequate method to compute the gradient, enables SAR images at test time to have statistics similar to the training set as inputs to the network. More precisely, the gradient distribution for all homogeneous areas are the same and the gradient distribution for two homogeneous areas across boundaries depends only on the ratio of their mean intensity values. The proposed method, GRHED, significantly improves the state-of-the-art, especially in very noisy cases such as 1-look images. Note de contenu : 1- Context 2- SAR basics, statistics of SAR images and data used in this thesis I Line segment detection in SAR images 3- Introduction 4- LSD, a line segment detector with false detection control 5- LSDSAR, a generic line segment detector for SAR images 6- Experiments II Edge detection in SAR images using CNNs 7- Introduction 8- Presentation of the HED method and of the training dataset 9- GRHED, introducing a hand-crafted layer before the usual CNNs 10- Experiments 11- Summary of the thesis Numéro de notice : 25878 Thématique : IMAGERIE Nature : Thèse française Note de thèse : Thèse de Doctorat : Spécialité : Signal, Images, Automatique et robotique : Paris : 2020 En ligne : https://tel.archives-ouvertes.fr/tel-02861903/document Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95689

Context pyramidal network for stereo matching regularized by disparity gradients / Junhua Kang in ISPRS Journal of photogrammetry and remote sensing, vol 157 (November 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 157 (November 2019) . - pp 201 - 215 Titre : Context pyramidal network for stereo matching regularized by disparity gradients Type de document : Article/Communication Auteurs : Junhua Kang, Auteur ; Lin Chen, Auteur ; Fei Deng, Auteur ; Christian Heipke, Auteur Année de publication : 2019 Article en page(s) : pp 201 - 215 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] appariement de formes [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] chaîne de traitement [Termes descripteurs IGN] classification par réseau neuronal convolutif [Termes descripteurs IGN] extraction de traits caractéristiques [Termes descripteurs IGN] gradient [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] vision stéréoscopique Résumé : (Auteur) Also after many years of research, stereo matching remains to be a challenging task in photogrammetry and computer vision. Recent work has achieved great progress by formulating dense stereo matching as a pixel-wise learning task to be resolved with a deep convolutional neural network (CNN). However, most estimation methods, including traditional and deep learning approaches, still have difficulty to handle real-world challenging scenarios, especially those including large depth discontinuity and low texture areas. To tackle these problems, we investigate a recently proposed end-to-end disparity learning network, DispNet (Mayer et al., 2015), and improve it to yield better results in these problematic areas. The improvements consist of three major contributions. First, we use dilated convolutions to develop a context pyramidal feature extraction module. A dilated convolution expands the receptive field of view when extracting features, and aggregates more contextual information, which allows our network to be more robust in weakly textured areas. Second, we construct the matching cost volume with patch-based correlation to handle larger disparities. We also modify the basic encoder-decoder module to regress detailed disparity images with full resolution. Third, instead of using post-processing steps to impose smoothness in the presence of depth discontinuities, we incorporate disparity gradient information as a gradient regularizer into the loss function to preserve local structure details in large depth discontinuity areas. We evaluate our model in terms of end-point-error on several challenging stereo datasets including Scene Flow, Sintel and KITTI. Experimental results demonstrate that our model decreases the estimation error compared with DispNet on most datasets (e.g. we obtain an improvement of 46% on Sintel) and estimates better structure-preserving disparity maps. Moreover, our proposal also achieves competitive performance compared to other methods. Numéro de notice : A2019-496 Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.09.012 date de publication en ligne : 27/09/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.09.012 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93729 [article]

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Automatic registration of images to untextured geometry using average shading gradients / Tobias Plötz in International journal of computer vision, vol 125 n° 1-3 (December 2017)  

Public [article]  inInternational journal of computer vision > vol 125 n° 1-3 (December 2017) . - pp 65 - 81 Titre : Automatic registration of images to untextured geometry using average shading gradients Type de document : Article/Communication Auteurs : Tobias Plötz, Auteur ; Stefan Roth, Auteur Année de publication : 2017 Article en page(s) : pp 65 - 81 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes descripteurs IGN] attribut géomètrique [Termes descripteurs IGN] estimation de pose [Termes descripteurs IGN] gradient [Termes descripteurs IGN] maillage par triangles [Termes descripteurs IGN] ombre [Termes descripteurs IGN] SIFT (algorithme) [Termes descripteurs IGN] superposition d'images Résumé : (Auteur) Many existing approaches for image-to-geometry registration assume that either a textured 3D model or a good initial guess of the 3D pose is available to bootstrap the registration process. In this paper we consider the registration of photographs to 3D models even when no texture information is available. This is very challenging as we cannot rely on texture gradients, and even shading gradients are hard to estimate since the lighting conditions are unknown. To that end, we propose average shading gradients, a rendering technique that estimates the average gradient magnitude over all lighting directions under Lambertian shading. We use this gradient representation as the building block of a registration pipeline based on matching sparse features. To cope with inevitable false matches due to the missing texture information and to increase robustness, the pose of the 3D model is estimated in two stages. Coarse pose hypotheses are first obtained from a single correct match each, subsequently refined using SIFT flow, and finally verified. We apply our algorithm to registering images of real-world objects to untextured 3D meshes of limited accuracy. Moreover, we show that registration can be performed even for paintings despite lacking photo-realism. Numéro de notice : A2017-813 Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s11263-017-1022-x En ligne : https://doi.org/10.1007/s11263-017-1022-x Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89255 [article]

On the determination of the effect of horizontal ionospheric gradients on ranging errors in GNSS positioning / Ekaterina A. Danilogorskaya in Journal of geodesy, vol 91 n° 5 (May 2017)  

Public [article]  inJournal of geodesy > vol 91 n° 5 (May 2017) . - pp 503 – 517 Titre : On the determination of the effect of horizontal ionospheric gradients on ranging errors in GNSS positioning Type de document : Article/Communication Auteurs : Ekaterina A. Danilogorskaya, Auteur ; Nikolay N. Zernov, Auteur ; Vadim E. Gherm, Auteur ; Hal J. Strangeways, Auteur Année de publication : 2017 Article en page(s) : pp 503 – 517 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Navigation et positionnement [Termes descripteurs IGN] données GNSS [Termes descripteurs IGN] gradient [Termes descripteurs IGN] propagation ionosphérique [Termes descripteurs IGN] teneur totale en électrons Résumé : (auteur) An alternative approach to the traditionally employed method is proposed for treating the ionospheric range errors in transionospheric propagation such as for GNSS positioning or satellite-borne SAR. It enables the effects due to horizontal gradients of electron density (as well as vertical gradients) in the ionosphere to be explicitly accounted for. By contrast with many previous treatments, where the expansion of the solution for the phase advance is represented as the series in the inverse frequency powers and the main term of the expansion corresponds to the true line-of-sight distance from the transmitter to the receiver, in the alternative technique the zero-order term is the rigorous solution for a spherically layered ionosphere with any given vertical electron density profile. The first-order term represents the effects due to the horizontal gradients of the electron density of the ionosphere, and the second-order correction appears to be negligibly small for any reasonable parameters of the path of propagation and its geometry for VHF/UHF frequencies. Additionally, an “effective” spherically symmetric model of the ionosphere has been introduced, which accounts for the major contribution of the horizontal gradients of the ionosphere and provides very high accuracy in calculations of the phase advance. Numéro de notice : A2017-226 Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern En ligne : http://dx.doi.org/10.1007/s00190-016-0978-6 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=85107 [article]

Data science par la pratique : fondamentaux avec Python / Joël Grus (2017)

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Floristic composition and across-track reflectance gradient in Landsat images over Amazonian forests / Javier Muro in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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A methodology for near real-time change detection between Unmanned Aerial Vehicle and wide area satellite images / Anastasios L. Fytsilis in ISPRS Journal of photogrammetry and remote sensing, vol 119 (September 2016)  

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Genetic differentiation of European larch along an altitudinal gradient in the French Alps / Maxime Nardin in Annals of Forest Science [en ligne], vol 72 n° 5 (July 2015)  

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Un approche esthétique pour l'enseignement de la composition de gradients colorés / Laurent Jégou in Cartes & Géomatique, n° 222 (décembre 2014)

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A fast volume-gradient-based band selection method for hyperspectral image / X. Geng in IEEE Transactions on geoscience and remote sensing, vol 52 n° 11 tome 1 (November 2014)

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Bi-temporal texton forest for land cover transition detection on remotely sensed imagery / Zhen Lei in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)  

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A spectral gradient difference based approach for land cover change detection / Jun Chen in ISPRS Journal of photogrammetry and remote sensing, vol 85 (November 2013)

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Towards a 1 mGal accuracy and 1 min resolution altimetry gravity field / Lifeng Bao in Journal of geodesy, vol 87 n° 10-12 (October - December 2013)  

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Topological gradient connection analysis for feature detection / Chao-Yuan Lo in Photogrammetric record, vol 28 n° 141 (March - May 2013)  

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