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Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry / Wei Wang in Computers & geosciences, vol 166 (September 2022)  

Public [article]  inComputers & geosciences > vol 166 (September 2022) . - n° 105191 Titre : Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry Type de document : Article/Communication Auteurs : Wei Wang ; Wenbo Zhao, Auteur ; Bo Chai, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 105191 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] Chine [Termes IGN] discontinuité [Termes IGN] éboulement [Termes IGN] extraction de données [Termes IGN] front rocheux [Termes IGN] image à haute résolution [Termes IGN] image captée par drone [Termes IGN] matrice [Termes IGN] pente [Termes IGN] photogrammétrie aérienne [Termes IGN] profondeur [Termes IGN] risque naturel [Termes IGN] semis de points [Termes IGN] texture d'image Résumé : (auteur) Discontinuity extraction and interpretation of fractured masses is of high importance when analyzing rock slope stability. Regarding high-steep slopes, which are areas that are difficult to reach, traditional methods to obtain discontinuities, such as the sample window method (SWM), are unlikely to be implemented, resulting in challenges for the identification of potential rockfalls. With the development of the unmanned ariel vehicle (UAV) technology, discontinuity extraction can overcome by noncontact photogrammetry. However, there is still a lack of comprehensive and practical solutions to fulfill rockfall identification from field investigation to in-door analysis. For this purpose, a practical case study was carried out in Wanzhou, Chongqing, China, where a 400 m vertical rock slope prone to rockfall was collected as a typical example. The centimeter-level 3D Textured Digital Outcrop Model (TDOM) and dense Point Cloud (PC) were established using high-resolution photos acquired by nap-of-the-object photogrammetry. The discontinuity of the fractured mass was interpreted by fully taking advantage of both 2D images (texture information-dominated) and 3D PCs (depth information-dominated). Furthermore, a new parameter rock cavity rate (RCR) and the corresponding semiautomatic extraction method based on point clouds are proposed. Subsequently, the possibility of various failure modes and their joint combinations were determined by kinematic analysis. Finally, the rock slope stability was determined using a matrix that considers the slope mass rating (SMR) value and the parameter RCR. The proposed process flow and relevant techniques in this study provide an operable and practical solution for further application regarding discontinuity interpretation and potential rockfall identification on high-steep slopes. Numéro de notice : A2022-655 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.cageo.2022.105191 Date de publication en ligne : 08/07/2022 En ligne : https://doi.org/10.1016/j.cageo.2022.105191 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101504 [article]

Learning indoor point cloud semantic segmentation from image-level labels / Youcheng Song in The Visual Computer, vol 38 n° 9 (September 2022)  

Public [article]  inThe Visual Computer > vol 38 n° 9 (September 2022) . - pp 3253 - 3265 Titre : Learning indoor point cloud semantic segmentation from image-level labels Type de document : Article/Communication Auteurs : Youcheng Song, Auteur ; Zhengxing Sun, Auteur ; Qian Li, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 3253 - 3265 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] apprentissage dirigé [Termes IGN] données d'entrainement sans étiquette [Termes IGN] image RVB [Termes IGN] scène intérieure [Termes IGN] segmentation d'image [Termes IGN] segmentation sémantique [Termes IGN] semis de points Résumé : (auteur) The data-hungry nature of deep learning and the high cost of annotating point-level labels make it difficult to apply semantic segmentation methods to indoor point cloud scenes. Therefore, exploring how to make point cloud segmentation methods less rely on point-level labels is a promising research topic. In this paper, we introduce a weakly supervised framework for semantic segmentation on indoor point clouds. To reduce the labor cost in data annotation, we use image-level weak labels that only indicate the classes that appeared in the rendered images of point clouds. The experiments validate the effectiveness and scalability of our framework. Our segmentation results on both ScanNet and S3DIS datasets outperform the state-of-the-art method using a similar level of weak supervision. Numéro de notice : A2022-793 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article DOI : 10.1007/s00371-022-02569-0 Date de publication en ligne : 02/07/2022 En ligne : https://doi.org/10.1007/s00371-022-02569-0 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101917 [article]

Efficient dike monitoring using terrestrial SFM photogrammetry / Laurent Froideval in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2022 (2022 edition)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2022 (2022 edition) . - pp 359 - 366 Titre : Efficient dike monitoring using terrestrial SFM photogrammetry Type de document : Article/Communication Auteurs : Laurent Froideval, Auteur ; Christophe Conessa, Auteur ; Xavier Pellerin Le Bas, Auteur ; Laurent Benoit, Auteur ; Dominique Mouazé, Auteur Année de publication : 2022 Article en page(s) : pp 359 - 366 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] digue [Termes IGN] sable [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] surveillance d'ouvrage Résumé : (auteur) Nature based solutions are growing rapidly in order to mitigate in the near future the effects of climate change and rise of sea level on most anthropogenic coasts. In that frame, the CHERbourg bLOC (CHERLOC) project aims to study new coastal engineering solutions (overtopping, sediment transport) thanks to two new artificial units in two test sites (Normandy, France) considering biodiversity preservation but also societal acceptability. This study details an efficient method to monitor such coastal infrastructure using terrestrial Structure from Motion (SfM). In 2021, surveys were conducted to acquire pictures in April, May, June and November. A time series of 3D photogrammetric models was generated using open source SfM software. The first model was georeferenced using Ground Control Points (GCP) measured by Differential Global Navigation Satellite System (DGNSS) so that it could be used as a reference for the following point clouds using surrounding ripraps assumed to be non-mobile through the period of the study. The georeferencing Root Mean Square Error (RMSE) was found to be 1.8 cm for the April model whereas RMSEs of relative registrations of the following dates were found to be sub-centimetric. These results can be used to observe and measure blocks displacements as well as sand volumes evolution throughout the time series. The biggest displacement was found to be 23 cm between April and June. Sand topographic variation shows a continuous accumulation on selected cross-sections between April and November with an overall height accumulation of about 30 cm. Sand volumes measurements show consistent results with an added volume of 3.67 m3 on the previous areas. Numéro de notice : A2022-429 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5194/isprs-annals-V-2-2022-359-2022 Date de publication en ligne : 17/05/2022 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2022-359-2022 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100734 [article]

Hybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy / Norbert Haala in ISPRS Open Journal of Photogrammetry and Remote Sensing, vol 4 (April 2022)  

Public [article]  inISPRS Open Journal of Photogrammetry and Remote Sensing > vol 4 (April 2022) . - n° 100014 Titre : Hybrid georeferencing of images and LiDAR data for UAV-based point cloud collection at millimetre accuracy Type de document : Article/Communication Auteurs : Norbert Haala, Auteur ; Michael Kölle, Auteur ; Michael Cramer, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 100014 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] aérotriangulation automatisée [Termes IGN] appariement d'images [Termes IGN] collecte de données [Termes IGN] compensation par faisceaux [Termes IGN] données lidar [Termes IGN] géoréférencement direct [Termes IGN] image captée par drone [Termes IGN] orthoimage [Termes IGN] précision millimétrique [Termes IGN] semis de points [Termes IGN] zone d'intérêt Résumé : (auteur) During the last two decades, UAV emerged as standard platform for photogrammetric data collection. Main motivation in that early phase was the cost effective airborne image collection at areas of limited size. This was already feasible by rather simple payloads like an off-the-shelf, compact camera and a navigation-grade GNSS sensor. Meanwhile, dedicated sensor systems enable applications that have not been feasible in the past. One example is the airborne collection of dense 3D point clouds at millimetre accuracies, which will be discussed in our paper. For this purpose, we collect both LiDAR and image data from a joint UAV platform and apply a so-called hybrid georeferencing. This process integrates photogrammetric bundle block adjustment with direct georeferencing of LiDAR point clouds. By these means georeferencing accuracy is improved for the LiDAR point cloud by an order of magnitude. We demonstrate the feasibility of our approach in the context of a project, which aims on monitoring of subsidence of about 10 mm/year. The respective area of interest is defined by a ship lock and its vicinity of mixed use. In that area, multiple UAV flights were captured and evaluated for a period of three years. As our main contribution, we demonstrate that 3D point accuracies at sub-centimetre level can be achieved. This is realized by joint orientation of laser scans and images in a hybrid adjustment framework, which enables accuracies corresponding to the GSD of the captured imagery. Numéro de notice : A2022-236 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.ophoto.2022.100014Get rights and content Date de publication en ligne : 16/03/2022 En ligne : https://doi.org/10.1016/j.ophoto.2022.100014Get rights and content Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100146 [article]

Exploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces / Ali Karami in Photogrammetric record, vol 37 n° 177 (March 2022)  

Public [article]  inPhotogrammetric record > vol 37 n° 177 (March 2022) . - pp 111 - 138 Titre : Exploiting light directionality for image-based 3D reconstruction of non-collaborative surfaces Type de document : Article/Communication Auteurs : Ali Karami, Auteur ; Fabio Menna, Auteur ; Fabio Remondino, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 111 - 138 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] appariement d'images [Termes IGN] axe de prise de vue [Termes IGN] étalonnage [Termes IGN] figure géométrique [Termes IGN] point d'appui [Termes IGN] points homologues [Termes IGN] rayonnement lumineux [Termes IGN] reconstruction 3D [Termes IGN] reconstruction d'objet [Termes IGN] semis de points Résumé : (auteur) Three-dimensional (3D) measurement of non-collaborative surfaces is still an open research topic. This paper investigates and quantifies for the first time the effect of light directionality and fusion of multiple images as a method to improve the quality of photogrammetric 3D reconstruction. For this aim, an image acquisition system that employs multiple light sources was developed to highlight the roughness and microstructures of the object under investigation. Images were captured at various grazing angles to highlight the local surface roughness and microstructures. Individual point clouds, created using images taken at different grazing angles, were produced using dense image-matching techniques. These point clouds were then compared against different 3D photogrammetric reconstructions obtained from a pre-processing of the acquired images based on diffuse lighting, median and average images. Experiments showed that exploiting light directionality significantly improves image-matching quality. Furthermore, depending on the light direction, the root mean square (RMS) error of the 3D surfaces obtained using the proposed system were up to 50% less than those created by traditional diffuse lighting. Numéro de notice : A2022-208 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1111/phor.12400 Date de publication en ligne : 07/03/2022 En ligne : https://doi.org/10.1111/phor.12400 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100218 [article]

Automatic algorithm for georeferencing historical-to-nowadays aerial images acquired in natural environments / Daniela Craciun (2022)  

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Accurate mapping method for UAV photogrammetry without ground control points in the map projection frame / Jianchen Liu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 11 (November 2021)  

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Feature matching for multi-epoch historical aerial images / Lulin Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 182 (December 2021)    

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Fully automated pose estimation of historical images in the context of 4D geographic information systems utilizing machine learning methods / Ferdinand Maiwald in ISPRS International journal of geo-information, vol 10 n° 11 (November 2021)  

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Automatic detection of planted trees and their heights using photogrammetric rpa point clouds / Kênia Samara Mourão Santos in Boletim de Ciências Geodésicas, vol 27 n° 3 ([01/10/2021])  

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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)  

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Three-dimensional building change detection using object-based image analysis (case study: Tehran) / Fatemeh Tabib Mahmoudi in Applied geomatics, vol 13 n° 3 (September 2021)  

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Research on 3D model reconstruction based on a sequence of cross-sectional images / Zhiguo Dong in Machine Vision and Applications, vol 32 n°4 (July 2021)  

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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)  

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Automated calibration of smartphone cameras for 3D reconstruction of mechanical pipes / Reza Maalek in Photogrammetric record, vol 36 n° 174 (June 2021)  

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