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Improving GNSS-acoustic positioning by optimizing the ship’s track lines and observation combinations / Guanxu Chen in Journal of geodesy, vol 94 n° 6 (June 2020)  

Public [article]  inJournal of geodesy > vol 94 n° 6 (June 2020) Titre : Improving GNSS-acoustic positioning by optimizing the ship’s track lines and observation combinations Type de document : Article/Communication Auteurs : Guanxu Chen, Auteur ; Yang Liu, Auteur ; Yanxiong Liu, Auteur ; et al., Auteur Année de publication : 2020 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de géodésie spatiale [Termes descripteurs IGN] contrainte géométrique [Termes descripteurs IGN] fond marin [Termes descripteurs IGN] GNSS-Acoustique [Termes descripteurs IGN] navire [Termes descripteurs IGN] positionnement par GNSS [Termes descripteurs IGN] précision du positionnement [Termes descripteurs IGN] profondeur [Termes descripteurs IGN] station GNSS [Termes descripteurs IGN] trajectoire Résumé : (auteur) The position of a seafloor geodetic station can be determined by combining Global Navigation Satellite System (GNSS) and acoustic technologies, called GNSS-acoustic positioning. The precision of GNSS-acoustic positioning, a technique that employs the distance intersection, is determined by the positioning geometry formed by the ship’s track lines with respect to the seafloor station and the errors in the measurements. In the context of a shallow sea trial, we studied three key techniques in GNSS-acoustic positioning: the optimal geometric configuration, differencing techniques for acoustic observations and depth constraints offered by pressure gauges. The results showed that the optimal geometric configuration is a circular track with a radius of 2‾√ times the depth plus an overhead cross-track with a length of the circle diameter. Differenced observations can improve the horizontal positioning precision but will worsen the vertical positioning precision due to the change in the geometric configuration and the elimination of vertical information if the number of observations is limited. The proposed difference strategy, that is, applying a symmetric location difference operator to the circular track and an undifference operator to the cross-track, can effectively improve the horizontal precision and avoid vertical defects. By using relative depth observations from two pressure gauges as constraints, the vertical defects of GNSS-acoustic positioning can be improved, achieving a better vertical positioning precision. Applying the proposed methods to high-quality GNSS and acoustic observations, the positioning precision of a shallow seafloor geodetic station can be better than 2 cm. Numéro de notice : A2020-377 Affiliation des auteurs : non IGN Thématique : POSITIONNEMENT Nature : Article nature-HAL : ArtAvecCL-RevueIntern date de publication en ligne : 27/06/2020 En ligne : https://doi.org/10.1007/s00190-020-01389-1 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95369 [article]

An indoor spatial accessible area generation approach considering distance constraints / Lina Yang in Annals of GIS, Vol 26 n° 1 (January 2020)  

Public [article]  inAnnals of GIS > Vol 26 n° 1 (January 2020) . - pp 25 - 34 Titre : An indoor spatial accessible area generation approach considering distance constraints Type de document : Article/Communication Auteurs : Lina Yang, Auteur ; Hongru Bi, Auteur ; Xiaojing Yao, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 25 - 34 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique [Termes descripteurs IGN] analyse spatiale [Termes descripteurs IGN] contrainte géométrique [Termes descripteurs IGN] distance [Termes descripteurs IGN] espace intérieur [Termes descripteurs IGN] modèle conceptuel de données localisées [Termes descripteurs IGN] modèle géométrique [Termes descripteurs IGN] zone tampon Résumé : (auteur) Indoor objects’ accessible area generation considering distance constraints is of important realistic significance for spatial analysis. To the best of our knowledge, few studies have been conducted on indoor accessible area generation considering distance constraints. And because of different spatial characteristics between indoor and outdoor environment, the commonly used approach in outdoor space is not suitable for indoor space. In this paper, based on the hybrid spatial data model of geometric and symbolic model, an accessible area generation approach considering distance constraints for indoor environment is proposed by improving traditional spatial buffer zone generation technique. The buffer zone generation with a predefined distance around indoor objects within their located subspace is executed first; then, based on the indoor spatial connectivity, buffer generation around exit points is successively executed in its next connected subspaces until the distance decreases to zero. The merge of these generated buffer zones is the result of accessible area generation with a predefined distance constraint. During the process, two kinds of spatial search strategies, depth-first search and breadth-first search, are presented. Two sets of experiments are conducted to validate the correctness and efficiency of the proposed approach. Results show that the approach can be effectively used to solve the problem of indoor objects’ accessible area generation with distance constraints. Moreover, the potential use, as well as the limitation of the proposed approach is discussed in this paper. Numéro de notice : A2020-118 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article DOI : 10.1080/19475683.2019.1680575 date de publication en ligne : 18/10/2019 En ligne : https://doi.org/10.1080/19475683.2019.1680575 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94737 [article]

Learning stereo reconstruction with deep neural networks / Stepan Tulyakov (2020)  

Public Titre : Learning stereo reconstruction with deep neural networks Type de document : Thèse/HDR Auteurs : Stepan Tulyakov, Auteur ; François Fleuret, Directeur de thèse ; Anton Ivanov, Directeur de thèse Editeur : Lausanne : Ecole Polytechnique Fédérale de Lausanne EPFL Année de publication : 2020 Importance : 139 p. Format : 21 x 30 cm Note générale : bibliographie Thèse présentée à l'Ecole Polytechnique Fédérale de Lausanne pour l’obtention du grade de Docteur ès Sciences Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] apprentissage profond [Termes descripteurs IGN] classification semi-dirigée [Termes descripteurs IGN] contrainte géométrique [Termes descripteurs IGN] couple stéréoscopique [Termes descripteurs IGN] entropie [Termes descripteurs IGN] estimateur [Termes descripteurs IGN] étalonnage géométrique [Termes descripteurs IGN] modèle stéréoscopique [Termes descripteurs IGN] profondeur [Termes descripteurs IGN] réalité de terrain [Termes descripteurs IGN] reconstruction 3D [Termes descripteurs IGN] reconstruction d'image [Termes descripteurs IGN] vision par ordinateur [Termes descripteurs IGN] vision stéréoscopique Résumé : (auteur) Stereo reconstruction is a problem of recovering a 3d structure of a scene from a pair of images of the scene, acquired from different viewpoints. It has been investigated for decades and many successful methods were developed. The main drawback of these methods, is that they typically utilize a single depth cue, such as parallax, defocus blur or shading, and thus are not as robust as a human visual system that simultaneously relies on a range of monocular and binocular cues. This is mainly because it is hard to manually design a model, accounting for multiple depth cues. In this work, we address this problem by focusing on deep learning-based stereo methods that can discover a model for multiple depth cues directly from training data with ground truth depth. The complexity of deep learning-based methods, however, requires very large training sets with ground truth depth, which is often hard or costly to collect. Furthermore, even when training data is available it is often contaminated with noise, which reduces the effectiveness of supervised learning. In this work, in Chapter 3 we show that it is possible to alleviate this problem by using weakly supervised learning, that utilizes geometric constraints of the problem instead of ground truth depth. Besides the large training set requirement, deep stereo methods are not as application-friendlyas traditional methods. They have a large memory footprint and their disparity range is fixed at training time. For some applications, such as satellite stereo i magery, these are serious problems since satellite images are very large, often reaching tens of megapixels, and have a variable baseline, depending on a time difference between stereo images acquisition. In this work, in Chapter 4 we address these problems by introducing a novel network architecture with a bottleneck, capable of processing large images and utilizing more context, and an estimator that makes the network less sensitive to stereo matching ambiguities and applicable to any disparity range without re-training. Because deep learning-based methods discover depth cues directly from training data, they can be adapted to new data modalities without large modifications. In this work, in Chapter 5 we show that our method, developed for a conventional frame-based camera, can be used with a novel event-based camera, that has a higher dynamic range, smaller latency, and low power consumption. Instead of sampling intensity of all pixels with a fixed frequency, this camera asynchronously reports events of significant pixel intensity changes. To adopt our method to this new data modality, we propose a novel event sequence embedding module, that firstly aggregates information locally, across time, using a novel fully-connected layer for an irregularly sampled continuous domain, and then across discrete spatial domain. One interesting application of stereo is a reconstruction of a planet’s surface topography from satellite stereo images. In this work, in Chapter 6 we describe a geometric calibration method, as well as mosaicing and stereo reconstruction tools that we developed in the framework of the doctoral project for Color and Stereo Surface Imaging System onboard of ESA’s Trace Gas Orbiter, orbiting Mars. For the calibration, we propose a novel method, relying on starfield images because large focal lengths and complex optical distortion of the instrument forbid using standard methods. Scientific and practical results of this work are widely used by a scientific community. Note de contenu : 1- Introduction 2- Background 3- Weakly supervised learning of deep patch-matching cost 4- Applications-friendly deep stereo 5- Dense deep event-based stereo 6- Calibration of a satellite stereo system 7- Conclusions Numéro de notice : 25795 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Thèse de Doctorat : Sciences : Lausanne : 2020 En ligne : https://infoscience.epfl.ch/record/275342?ln=fr Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95025

Reliable image matching via photometric and geometric constraints structured by Delaunay triangulation / San Jiang in ISPRS Journal of photogrammetry and remote sensing, vol 153 (July 2019)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 153 (July 2019) . - pp 1 - 2O Titre : Reliable image matching via photometric and geometric constraints structured by Delaunay triangulation Type de document : Article/Communication Auteurs : San Jiang, Auteur ; Wanshou Jiang, Auteur Année de publication : 2019 Article en page(s) : pp 1 - 2O Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes descripteurs IGN] appariement d'images [Termes descripteurs IGN] cohérence photométrique [Termes descripteurs IGN] contrainte d'objet [Termes descripteurs IGN] contrainte géométrique [Termes descripteurs IGN] processus d'analyse hiérarchisée [Termes descripteurs IGN] triangulation de Delaunay [Termes descripteurs IGN] valeur aberrante Résumé : (Auteur) Image matching is a basic task in the field of photogrammetry and remote sensing. By using the advantages of the Delaunay triangulation, this paper proposes a novel image matching method. First, neighboring structures of randomly distributed feature points are formed with the assistance of the Delaunay triangulation and its corresponding graph, and the image planes are simultaneously divided into patches of near-regular triangles. Second, two constraints, a photometric constraint and a geometric constraint, are implemented based on the constructed neighboring structures, which incorporate the hierarchical elimination and left-right checking strategies to deliver the influences of outliers on the decision of inliers and ensure the high precision of the final matches. The former utilizes a line descriptor as a second-order photometric constraint, and the latter adopts the spatial angular order (SAO) to achieve a geometric constraint for the calculation of dissimilarity scores between correspondences. In addition, with the constraints between triangles of the refined Delaunay triangulation and its corresponding graph, a match expansion is designed to exploit as many inliers as possible. Finally, a reliable image matching algorithm is proposed by sequentially executing the three constraints for outlier elimination and match expansion. Under comprehensive analysis and comparison with five state-of-the-art algorithms, the performance of the proposed method is verified by using both rigid and non-rigid datasets. The experimental results demonstrate that the Delaunay triangulation is sufficient to construct neighboring structures for the implementation of local photometric and geometric constraints, and the proposed method can achieve good performance in terms of the precision, recall and number of inliers, and provide reliable matches for stereo image pairs with both rigid and non-rigid transformations. Numéro de notice : A2019-260 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2019.04.006 date de publication en ligne : 01/05/2019 En ligne : https://doi.org/10.1016/j.isprsjprs.2019.04.006 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93073 [article]

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Geometric reasoning with uncertain polygonal faces / Jochen Meidow in Photogrammetric Engineering & Remote Sensing, PERS, vol 84 n° 6 (juin 2018)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 84 n° 6 (juin 2018) . - pp 393- 401 Titre : Geometric reasoning with uncertain polygonal faces Type de document : Article/Communication Auteurs : Jochen Meidow, Auteur ; Wolfgang Förstner, Auteur Année de publication : 2018 Article en page(s) : pp 393- 401 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes descripteurs IGN] contrainte géométrique [Termes descripteurs IGN] incertitude géométrique [Termes descripteurs IGN] polyèdre [Termes descripteurs IGN] polygone [Termes descripteurs IGN] processus stochastique [Termes descripteurs IGN] raisonnement spatial [Termes descripteurs IGN] toit Résumé : (auteur) The reconstruction of urban areas suffers from the dilemma of modeling urban structures in a generic or specific way, thus being too unspecific or too restrictive. One approach to overcome this dilemma is to model and to instantiate buildings as arbitrarily shaped polyhedra and to recognize man-made structures in a subsequent stage by geometric reasoning. Thus, the existence of unconstrained boundary representations for buildings is assumed. To stay generic and to avoid the use of templates for pre-defined building primitives, no assumptions for the buildings' outlines and the planar roof areas are made. Typically, roof areas are derived interactively or in an automatic process based on given point clouds or digital surface models. Due to the measurement process and the assumption of planar boundaries, these planar faces are uncertain. Thus, a stochastic geometric reasoning process with statistical testing is appropriate to detected man-made structures followed by an adjustment to enforce the deduced geometric constraints. Unfortunately, city models usually do not feature information about the uncertainty of geometric entities. We present an approach to specify the uncertainty of the planes corresponding to the planar patches, i.e., polygons bounding a building, analytically. This paves the way to conduct the reasoning process with just a few assumptions. We describe and demonstrate the approach with real data. Numéro de notice : A2018-232 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.84.6.393 date de publication en ligne : 01/06/2018 En ligne : https://doi.org/10.14358/PERS.84.6.393 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=90174 [article]

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SDF-2-SDF registration for real-time 3D reconstruction from RGB-D data / Miroslava Slavcheva in International journal of computer vision, vol 126 n° 6 (June 2018)  

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Recognizing building groups for generalization : a comparative study / Min Deng in Cartography and Geographic Information Science, Vol 45 n° 3 (May 2018)  

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An immune genetic algorithm to buildings displacement in cartographic generalization / Yageng Sun in Transactions in GIS, vol 20 n° 4 (August 2016)  

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Camera self-calibration with lens distortion from a single image / Dan Liu in Photogrammetric Engineering & Remote Sensing, PERS, vol 82 n° 5 (May 2016)  

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Midrange geometric interactions for semantic segmentation / Julia Diebold in International journal of computer vision, vol 117 n° 3 (May 2016)  

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Filtering airborne lidar data by modified white top-hat transform with directional edge constraints / Yong Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 80 n° 2 (February 2014)  

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A fully constrained linear spectral unmixing algorithm based on distance geometry / Hanye Pu in IEEE Transactions on geoscience and remote sensing, vol 52 n° 2 (February 2014)  

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Mesure de précision dans un cadre hostile / Michel Ravelet in Géomètre, n° 2110 (janvier 2014)

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Boundary-constrained multi-scale segmentation method for remote sensing images / Xueliang Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)

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A comparison of dense matching algorithms for scaled surface reconstruction using stereo camera rigs / Ali Hosseininaveh Ahmadabadian in ISPRS Journal of photogrammetry and remote sensing, vol 78 (April 2013)

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