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Determining optimal photogrammetric adjustment of images obtained from a fixed-wing UAV / Karolina Pargiela in Photogrammetric record, Vol 36 n° 175 (September 2021)  

Public [article]  inPhotogrammetric record > Vol 36 n° 175 (September 2021) . - pp 285 - 302 Titre : Determining optimal photogrammetric adjustment of images obtained from a fixed-wing UAV Type de document : Article/Communication Auteurs : Karolina Pargiela, Auteur ; Antoni Rzonca, Auteur Année de publication : 2021 Article en page(s) : pp 285 - 302 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] acquisition d'images [Termes IGN] chevauchement [Termes IGN] compensation par faisceaux [Termes IGN] image captée par drone [Termes IGN] obturateur [Termes IGN] point d'appui [Termes IGN] Pologne [Termes IGN] positionnement cinématique en temps réel [Termes IGN] structure-from-motion [Termes IGN] système de numérisation mobile Résumé : (auteur) Photogrammetry with unmanned aerial vehicles (UAVs) has become a source of data with extensive applications. The accuracy is of utmost significance, yet the intention is also to find the best possible solutions for data acquisition in economic terms. The objective of the research was the analysis of various variants of the bundle block adjustment. The analysis concerns data which is diversified with respect to the type of shutter (rolling/global), the measurement of external orientation elements, the overlap and the number of ground control points (GCPs). Numéro de notice : A2021-691 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12377 date de publication en ligne : 07/08/2021 En ligne : https://doi.org/10.1111/phor.12377 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98487 [article]

A unified framework of bundle adjustment and feature matching for high-resolution satellite images / Xiao Ling in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 7 (July 2021)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 7 (July 2021) . - pp 485 - 490 Titre : A unified framework of bundle adjustment and feature matching for high-resolution satellite images Type de document : Article/Communication Auteurs : Xiao Ling, Auteur ; Xu Huang, Auteur ; Rongjun Qin, Auteur Année de publication : 2021 Article en page(s) : pp 485 - 490 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] appariement d'images [Termes IGN] compensation par faisceaux [Termes IGN] corrélation à l'aide de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] orientation du capteur [Termes IGN] précision radiométrique Résumé : (Auteur) Bundle adjustment (BA) is a technique for refining sensor orientations of satellite images, while adjustment accuracy is correlated with feature matching results. Feature matching often contains high uncertainties in weak/repeat textures, while BA results are helpful in reducing these uncertainties. To compute more accurate orientations, this article incorporates BA and feature matching in a unified framework and formulates the union as the optimization of a global energy function so that the solutions of the BA and feature matching are constrained with each other. To avoid a degeneracy in the optimization, we propose a comprised solution by breaking the optimization of the global energy function into two-step suboptimizations and compute the local minimums of each suboptimization in an incremental manner. Experiments on multi-view high-resolution satellite images show that our proposed method outperforms state-of-the-art orientation techniques with or without accurate least-squares matching. Numéro de notice : A2021-571 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.87.7.485 date de publication en ligne : 01/07/2021 En ligne : https://doi.org/10.14358/PERS.87.7.485 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98163 [article]

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Multi-modal learning in photogrammetry and remote sensing / Michael Ying Yang in ISPRS Journal of photogrammetry and remote sensing, vol 176 (June 2021)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 176 (June 2021) . - pp 54 - 54 Titre : Multi-modal learning in photogrammetry and remote sensing Type de document : Article/Communication Auteurs : Michael Ying Yang, Auteur ; Loïc Landrieu , Auteur ; Devis Tuia, Auteur ; Charles Toth, Auteur Année de publication : 2021 Projets : 1-Pas de projet / Article en page(s) : pp 54 - 54 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection [Termes IGN] acquisition d'images [Termes IGN] apprentissage automatique [Termes IGN] données multisources Résumé : (Auteur) [Editorial] There is a growing interest in the photogrammetry and remote sensing community for multi-modal data, i. e., data simultaneously acquired from a variety of platforms, including satellites, aircraft, UAS/UGS, autonomous vehicles, etc., by different sensors, such as radar, optical, LiDAR. Thanks to their different spatial, spectral, or temporal resolutions, the use of complementary data sources leads to richer and more robust information extraction. We expect that the use of multiple modalities will rapidly become a standard approach in the future. The main difficulty of jointly processing multi-modal data is due to the differences in structure among modalities. Another issue is the unbalanced number of labelled samples available across modalities, resulting in a significant gap in performance when models are trained separately. Clearly, the photogrammetry and remote sensing community has not yet exploited the full potential of multi-modal data. Neural networks seem well suited for accommodating different data sources, thanks to their capabilities to learn representations adapted to each task in an end-to-end fashion. In this context, there is a strong need for research and development of approaches for multi-sensory and multi-modal deep learning within the geospatial domain. Numéro de notice : A2021-364 Affiliation des auteurs : UGE-LaSTIG+Ext (2020- ) Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.03.022 date de publication en ligne : 23/04/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.03.022 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97660 [article]

Spherically optimized RANSAC aided by an IMU for Fisheye Image Matching / Anbang Liang in Remote sensing, vol 13 n°10 (May 2021)  

Public [article]  inRemote sensing > vol 13 n°10 (May 2021) . - n° 2017 Titre : Spherically optimized RANSAC aided by an IMU for Fisheye Image Matching Type de document : Article/Communication Auteurs : Anbang Liang, Auteur ; Qingquan Li, Auteur ; Zhipeng Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 2017 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] appariement d'images [Termes IGN] centrale inertielle [Termes IGN] coordonnées sphériques [Termes IGN] distorsion d'image [Termes IGN] estimation de pose [Termes IGN] étalonnage de chambre métrique [Termes IGN] géométrie épipolaire [Termes IGN] image hémisphérique [Termes IGN] Ransac (algorithme) Résumé : (auteur) Fisheye cameras are widely used in visual localization due to the advantage of the wide field of view. However, the severe distortion in fisheye images lead to feature matching difficulties. This paper proposes an IMU-assisted fisheye image matching method called spherically optimized random sample consensus (So-RANSAC). We converted the putative correspondences into fisheye spherical coordinates and then used an inertial measurement unit (IMU) to provide relative rotation angles to assist fisheye image epipolar constraints and improve the accuracy of pose estimation and mismatch removal. To verify the performance of So-RANSAC, experiments were performed on fisheye images of urban drainage pipes and public data sets. The experimental results showed that So-RANSAC can effectively improve the mismatch removal accuracy, and its performance was superior to the commonly used fisheye image matching methods in various experimental scenarios. Numéro de notice : A2021-416 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs13102017 date de publication en ligne : 20/05/2021 En ligne : https://doi.org/10.3390/rs13102017 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97757 [article]

Digital terrain models generated with low-cost UAV photogrammetry: Methodology and accuracy / Sergio Jiménez-Jiménez in ISPRS International journal of geo-information, vol 10 n° 5 (May 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 5 (May 2021) . - n° 285 Titre : Digital terrain models generated with low-cost UAV photogrammetry: Methodology and accuracy Type de document : Article/Communication Auteurs : Sergio Jiménez-Jiménez, Auteur ; Waldo Ojeda-Bustamante, Auteur ; Mariana de Jesús Marcial, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 285 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] capteur aérien [Termes IGN] estimation de précision [Termes IGN] étalonnage d'instrument [Termes IGN] géomorphologie [Termes IGN] image captée par drone [Termes IGN] modèle numérique de terrain [Termes IGN] modélisation géométrique de prise de vue [Termes IGN] photogrammétrie aérienne [Termes IGN] plan de vol [Termes IGN] point d'appui [Termes IGN] précision des mesures [Termes IGN] qualité des données [Termes IGN] structure-from-motion Résumé : (auteur) Digital terrain model (DTM) generation is essential to recreating terrain morphology once the external elements are removed. Traditional survey methods are still used to collect accurate geographic data on the land surface. Given the emergence of unmanned aerial vehicles (UAVs) equipped with low-cost digital cameras and better photogrammetric methods for digital mapping, efficient approaches are necessary to allow rapid land surveys with high accuracy. This paper provides a review, complemented with the authors’ experience, regarding the UAV photogrammetric process and field survey parameters for DTM generation using popular commercial photogrammetric software to process images obtained with fixed-wing or multicopter UAVs. We analyzed the quality and accuracy of the DTMs based on four categories: (i) the UAV system (UAV platforms and camera); (ii) flight planning and image acquisition (flight altitude, image overlap, UAV speed, orientation of the flight line, camera configuration, and georeferencing); (iii) photogrammetric DTM generation (software, image alignment, dense point cloud generation, and ground filtering); (iv) geomorphology and land use/cover. For flat terrain, UAV photogrammetry provided a horizontal root mean square error (RMSE) between 1 to 3 × the ground sample distance (GSD) and a vertical RMSE between 1 to 4.5 × GSD, and, for complex topography, a horizontal RMSE between 1 to 7 × GSD and a vertical RMSE between 1.5 to 5 × GSD. Finally, we stress that UAV photogrammetry can provide DTMs with high accuracy when the photogrammetric process variables are optimized. Numéro de notice : A2021-394 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/ijgi10050285 date de publication en ligne : 29/04/2021 En ligne : https://doi.org/10.3390/ijgi10050285 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97680 [article]

L'oeil de l'espace / Anonyme in Géomètre, n° 2190 (avril 2021)

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An improved approach based on terrain-dependent mathematical models for georeferencing pushbroom satellite images / Behrooz Moradi in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Correction radiométrique et recalage de nuages de points pour la reconstruction tridimensionnelle d'oeuvres du patrimoine culturel / Nathan Sanchiz (2021)  

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Exploiting multi-camera constraints within bundle block adjustment: an experimental comparison / Eleonora Maset (2021)  

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Georeferencing with self-calibration for airborne full-waveform Lidar data using digital elevation model / Qinghua Li in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 1 (January 2021)  

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Image matching from handcrafted to deep features: A survey / Jiayi Ma in International journal of computer vision, vol 29 n° 1 (January 2021)  

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Remote sensing and GIS / Basudeb Bhatta (2021)

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Robust and fast global image orientation / Xin Wang (2021)  

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The Influence of camera calibration on nearshore bathymetry estimation from UAV Vvdeos / Gonzalo Simarro in Remote sensing, vol 13 n° 1 (January 2021)  

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Bretagne, la végétation cartographiée / Marielle Mayo in Géomètre, n° 2185 (novembre 2020)

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