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GeoDanceHive: An operational hive for honeybees dances recording / Sylvain Galopin in Animals, vol 13 n° 7 (April-1 2023)  

Public [article]  inAnimals > vol 13 n° 7 (April-1 2023) . - n° 1182 Titre : GeoDanceHive: An operational hive for honeybees dances recording Type de document : Article/Communication Auteurs : Sylvain Galopin , Auteur ; Guillaume Touya , Auteur ; Pierrick Aupinel, Auteur ; Freddie-Jeanne Richard, Auteur Année de publication : 2023 Projets : 3-projet - voir note / Article en page(s) : n° 1182 Note générale : bibliographie This research was funded by the french ministries of Agriculture and Food Sovereignty (MASA—FCPR program), Ecological Transition and Territorial Cohesion (MTECT), Health and Prevention (MSP) and Higher Education and Research (MESR) and by the French national facility for institutional procurement of VHR satellite imagery (DINAMIS) and by the Lune de Miel® Fondation. This research was financially supported by the French Office for Biodiversity, on the fee envelope for diffuse pollution of the Écophyto II+ coord plan. F-J Richard, partners P. Aupinel and G. Touya for the DANCE project. Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Géomatique [Termes IGN] alimentation [Termes IGN] comportement [Termes IGN] enregistrement de données [Termes IGN] Hymenoptera (ordre) Résumé : (auteur) Honeybees are known for their ability to communicate about resources in their environment. They inform the other foragers by performing specific dance sequences according to the spatial characteristics of the resource. The purpose of our study is to provide a new tool for honeybees dances recording, usable in the field, in a practical and fully automated way, without condemning the harvest of honey. We designed and equipped an outdoor prototype of a production hive, later called “GeoDanceHive”, allowing the continuous recording of honeybees’ behavior such as dances and their analysis. The GeoDanceHive is divided into two sections, one for the colony and the other serving as a recording studio. The time record of dances can be set up from minutes to several months. To validate the encoding and sampling quality, we used an artificial feeder and visual decoding to generate maps with the vector endpoints deduced from the dance information. The use of the GeoDanceHive is designed for a wide range of users, who can meet different objectives, such as researchers or professional beekeepers. Thus, our hive is a powerful tool for honeybees studies in the field and could highly contribute to facilitating new research approaches and a better understanding landscape ecology of key pollinators. Numéro de notice : A2023-087 Affiliation des auteurs : UGE-LASTIG+Ext (2020- ) Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ani13071182 En ligne : https://doi.org/10.3390/ani13071182 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102987 [article]

Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features / Bai Zhu in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 129 - 147 Titre : Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features Type de document : Article/Communication Auteurs : Bai Zhu, Auteur ; Yuanxin Ye, Auteur ; Liang Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 129 - 147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] algorithme ICP [Termes IGN] correction géométrique [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] élément d'orientation externe [Termes IGN] enregistrement de données [Termes IGN] filtre de Gabor [Termes IGN] image aérienne [Termes IGN] recalage d'image [Termes IGN] semis de points [Termes IGN] SIFT (algorithme) [Termes IGN] structure-from-motion Résumé : (auteur) Co-registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quite challenging because the different imaging mechanisms produce significant geometric and radiometric distortions between the two multimodal data sources. To address this problem, we propose a robust and effective coarse-to-fine registration method that is conducted in two stages utilizing spatial constraints and Gabor structural features. In the first stage, the LiDAR point cloud data is transformed into an intensity map that is used as the reference image. Then, coarse registration is completed by designing a partition-based Features from Accelerated Segment Test (FAST) operator to extract the uniformly distributed interest points in the aerial images and thereafter performing a local geometric correction based on the collinearity equations using the exterior orientation parameters (EoPs). The coarse registration aims to provide a reliable spatial geometry relationship for the subsequent fine registration and is designed to eliminate rotation and scale changes, as well as making only a few translation differences exist between the images. In the second stage, a novel feature descriptor called multi-Scale and multi-Directional Features of odd Gabor (SDFG) is first built to capture the multi-scale and multi-directional structural properties of the images. Then, the three-dimensional (3D) phase correlation (PC) of the SDFG descriptor is established to detect the control points (CPs) between the aerial and LiDAR intensity image in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT) technique. Finally, the obtained CPs not only are employed to refine the EoPs, but also are used to achieve the fine registration of the aerial images and LiDAR data. We conduct experiments to verify the robustness of the proposed registration method using three sets of aerial images and LiDAR data with different scene coverage. Experimental results show that the proposed method is robust to geometric distortions and radiometric changes. Moreover, it achieves the registration accuracy of less than 2 pixels for all cases, which outperforms the current four state-of-the-art methods, demonstrating its superior registration performance. Numéro de notice : A2021-773 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.09.010 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98830 [article]

A Bayesian displacement field approach to accurate registration of SAR images / Mingtao Ding in Geocarto international, vol 36 n° 9 ([15/05/2021])  

Public [article]  inGeocarto international > vol 36 n° 9 [15/05/2021] . - pp 1007 - 1026 Titre : A Bayesian displacement field approach to accurate registration of SAR images Type de document : Article/Communication Auteurs : Mingtao Ding, Auteur ; Hongyan Wang, Auteur ; Lichun Sui, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 1007 - 1026 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] arc [Termes IGN] enregistrement de données [Termes IGN] estimation bayesienne [Termes IGN] image radar moirée [Termes IGN] implémentation (informatique) [Termes IGN] inférence [Termes IGN] méthode de Monte-Carlo par chaînes de Markov [Termes IGN] méthode du maximum de vraisemblance (estimation) [Termes IGN] processeur graphique [Termes IGN] superposition d'images [Termes IGN] transformation Résumé : (auteur) Precise registration of synthetic aperture radar (SAR) images is a nontrivial task since a change in radar-acquisition geometry generates image shifts. In existing system, either the transformation functions are oversimplified, or external measures such as digital elevation model and flight track are required to be precise. In this paper, we proposed a generative Bayesian approach to modelling the displacement vectors that map the position of each pixel in the image, thus avoiding degradation of the transformation function. Rather than providing a point estimate for the transformation function, the proposed method yields a full posterior density function of the transformation function. Especially, the Bayesian model learns all the parameters adaptively, and the procedure is fully automatic. The proposed model is comparable in accuracy to state-of-the-art optical flow methods on the challenging Sintel benchmarks, and outperforms currently published SAR image registration methods on some real SAR data with critical scenes. Numéro de notice : A2021-343 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1633418 Date de publication en ligne : 07/07/2019 En ligne : https://doi.org/10.1080/10106049.2019.1633418 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97584 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
059-2021091	RAB	Revue	Centre de documentation	En réserve L003	Disponible

Benefiting from local rigidity in 3D point cloud processing / Zan Gojcic (2021)  

Public Titre : Benefiting from local rigidity in 3D point cloud processing Type de document : Thèse/HDR Auteurs : Zan Gojcic, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2021 Importance : 141 p. Format : 21 x 30 cm Note générale : bibliographie A thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Français (fre) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] apprentissage profond [Termes IGN] capteur actif [Termes IGN] champ vectoriel [Termes IGN] déformation d'image [Termes IGN] données lidar [Termes IGN] effondrement de terrain [Termes IGN] enregistrement de données [Termes IGN] filtrage du bruit [Termes IGN] flux [Termes IGN] image 3D [Termes IGN] navigation autonome [Termes IGN] orientation du capteur [Termes IGN] segmentation [Termes IGN] semis de points [Termes IGN] téléphone intelligent [Termes IGN] traitement de semis de points [Termes IGN] voxel Index. décimale : THESE Thèses et HDR Résumé : (auteur) Incorporating 3D understanding and spatial reasoning into (intelligent) algorithms is crucial for solving several tasks in fields such as engineering geodesy, risk assessment, and autonomous driving. Humans are capable of reasoning about 3D spatial relations even from a single 2D image. However, making the priors that we rely on explicit and integrating them into computer programs is very challenging. Operating directly on 3D input data, such as 3D point clouds, alleviates the need to lift 2D data into a 3D representation within the task-specific algorithm and hence reduces the complexity of the problem. The 3D point clouds are not only a better-suited input data representation, but they are also becoming increasingly easier to acquire. Indeed, nowadays, LiDAR sensors are even integrated into consumer devices such as mobile phones. However, these sensors often have a limited field of view, and hence multiple acquisitions are required to cover the whole area of interest. Between these acquisitions, the sensor has to be moved and pointed in a different direction. Moreover, the world that surrounds us is also dynamic and might change as well. Reasoning about the motion of both the sensor and the environment, based on point clouds acquired in two-time steps, is therfore an integral part of point cloud processing. This thesis focuses on incorporating rigidity priors into novel deep learning based approaches for dynamic 3D perception from point cloud data. Specifically, the tasks of point cloud registration, deformation analysis, and scene flow estimation are studied. At first, these tasks are incorporated into a common framework where the main difference is in the level of rigidity assumptions that are imposed on the motion of the scene or the acquisition sensor. Then, the tasks specific priors are proposed and incorporated into novel deep learning architectures. While the global rigidity can be assumed in point cloud registration, the motion patterns in deformation analysis and scene flow estimation are more complex. Therefore, the global rigidity prior has to be relaxed to local or instancelevel rigidity, respectively. Rigidity priors not only add structure to the aforementioned tasks, which prevents physically implausible estimates and improves the generalization of the algorithms, but in some cases also reduce the supervision requirements. The proposed approaches were quantitatively and qualitatively evaluated on several datasets, and they yield favorable performance compared to the state-of-the-art. Numéro de notice : 28660 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD : Sciences : ETH Zurich : 2021 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/523368 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99817

Reducing shadow effects on the co-registration of aerial image pairs / Matthew Plummer in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 3 (March 2020)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 86 n° 3 (March 2020) . - pp 177 - 186 Titre : Reducing shadow effects on the co-registration of aerial image pairs Type de document : Article/Communication Auteurs : Matthew Plummer, Auteur ; Douglas A. Stow, Auteur ; Emmanuel Storey, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : pp 177 - 186 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de données [Termes IGN] correction des ombres [Termes IGN] détection automatique [Termes IGN] détection de changement [Termes IGN] effet d'ombre [Termes IGN] enregistrement de données [Termes IGN] image à haute résolution [Termes IGN] image aérienne [Termes IGN] image multitemporelle [Termes IGN] intensité lumineuse [Termes IGN] masque [Termes IGN] Ransac (algorithme) [Termes IGN] SIFT (algorithme) Résumé : (auteur) Image registration is an important preprocessing step prior to detecting changes using multi-temporal image data, which is increasingly accomplished using automated methods. In high spatial resolution imagery, shadows represent a major source of illumination variation, which can reduce the performance of automated registration routines. This study evaluates the statistical relationship between shadow presence and image registration accuracy, and whether masking and normalizing shadows leads to improved automatic registration results. Eighty-eight bitemporal aerial image pairs were co-registered using software called Scale Invariant Features Transform (SIFT) and Random Sample Consensus (RANSAC) Alignment (SARA). Co-registration accuracy was assessed at different levels of shadow coverage and shadow movement within the images. The primary outcomes of this study are (1) the amount of shadow in a multi-temporal image pair is correlated with the accuracy/success of automatic co-registration; (2) masking out shadows prior to match point select does not improve the success of image-to-image co-registration; and (3) normalizing or brightening shadows can help match point routines find more match points and therefore improve performance of automatic co-registration. Normalizing shadows via a standard linear correction provided the most reliable co-registration results in image pairs containing substantial amounts of relative shadow movement, but had minimal effect for pairs with stationary shadows. Numéro de notice : A2020-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.86.4.177 Date de publication en ligne : 01/03/2020 En ligne : https://doi.org/10.14358/PERS.86.4.177 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94776 [article]

Low-frequency desert noise intelligent suppression in seismic data based on multiscale geometric analysis convolutional neural network / Yuxing Zhao in IEEE Transactions on geoscience and remote sensing, vol 58 n° 1 (January 2020)  

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Advances in airborne Lidar systems and data processing / Jie Shan (2018)  

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Pairwise registration of TLS point clouds using covariance descriptors and a non-cooperative game / Dawei Zai in ISPRS Journal of photogrammetry and remote sensing, vol 134 (December 2017)  

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An automated method to register airborne and terrestrial laser scanning point clouds / Bisheng Yang in ISPRS Journal of photogrammetry and remote sensing, vol 109 (November 2015)  

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vol 25 n° 8-9 - august - september 2011 - Data-Intensive Geospatial Computing (Bulletin de International journal of geographical information science IJGIS) / Jiang Bin

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Registration quality - towards integration of laser scanning and photogrammetry / Petri Rönnholm (2011)    

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Erfassung von Straßenverkehrsdaten mit elektro-optischem Sensor und automatischer Bildauswertung / T. Vogtle (1989)

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16th international congress ISPRS, B2. Commission 2 / International society for photogrammetry and remote sensing (1980 -) (1988)  

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Le chemin du tachéomètre électro-optique moderne, le tachéomètre électro-optique automatique RETA : rentabilité accrue du levé tachéométrique pour l'enregistrement des données / H. Glass in Revue d'Iena, vol 1983 n° 1 (janvier 1983)

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Initiation pratique à la télédétection, 1. Volume 1 / Charles Cazabat (1975)

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