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Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest / Daniel Kükenbrink in International journal of applied Earth observation and geoinformation, vol 113 (September 2022)  

Public [article]  inInternational journal of applied Earth observation and geoinformation > vol 113 (September 2022) . - n° 102999 Titre : Benchmarking laser scanning and terrestrial photogrammetry to extract forest inventory parameters in a complex temperate forest Type de document : Article/Communication Auteurs : Daniel Kükenbrink, Auteur ; Mauro Marty, Auteur ; Ruedi Bösch, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 102999 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] caméra à bas coût [Termes IGN] cartographie et localisation simultanées [Termes IGN] détection d'arbres [Termes IGN] diamètre à hauteur de poitrine [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] forêt tempérée [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] lidar mobile [Termes IGN] lidar topographique [Termes IGN] photogrammétrie terrestre [Termes IGN] semis de points [Termes IGN] série temporelle [Termes IGN] structure-from-motion [Termes IGN] Zurich (Suisse) Résumé : (auteur) National forest inventories (NFI) are important for the assessment of the state and development of forests. Traditional NFIs often rely on statistical sampling approaches as well as expert assessment which may suffer from observer bias and may lack robustness for time series analysis. Over the course of the last decade, close-range remote sensing techniques such as terrestrial and mobile laser scanning became ever more established for the assessment of three-dimensional (3D) forest structure. With the ongoing trend to make the systems smaller, easier to use and more efficient, the pathway is being opened for an operational inclusion of such devices within the framework of an NFI to support the traditional field assessment. Close-range remote sensing could potentially speed up field inventory work as well as increase the area in which certain parameters are assessed. Benchmarks are needed to evaluate the performance of different close-range remote sensing devices and approaches, both in terms of efficiency as well as accuracy. In this study we evaluate the performance of two terrestrial (TLS), one handheld mobile (PLS) and two drone based (UAVLS) laser scanning systems to detect trees and extract the diameter at breast height (DBH) in three plots with a steep gradient in tree and understorey vegetation density. As a novelty, we also tested the acquisition of 3D point-clouds using a low-cost action camera (GoPro) in conjunction with the Structure from Motion (SfM) technique and compared its performance with those of the more costly LiDAR devices. Among the many parameters evaluated in traditional NFIs, the focus of the performance evaluation of this study is set on the automatic tree detection and DBH extraction. The results showed that TLS delivers the highest tree detection rate (TDR) of up to 94.6% under leaf-off and up to 82% under leaf-on conditions and a relative RMSE (rRMSE) for the DBH extraction between 2.5 and 9%, depending on the undergrowth complexity. The tested PLS system (leaf-on) achieved a TDR of up to 80% with an rRMSE between 3.7 and 5.8%. The tested UAVLS systems showed lowest TDR of less than 77% under leaf-off and less than 37% under leaf-on conditions. The novel GoPro approach achieved a TDR of up to 53% under leaf-on conditions. The reduced TDR can be explained by the reduced area coverage due to the chosen circular acquisition path taken with the GoPro approach. The DBH extraction performance on the other hand is comparable to those of the LiDAR devices with an rRMSE between 2 and 9%. Further benchmarks are needed in order to fully assess the applicability of these systems in the framework of an NFI. Especially the robustness under varying forest conditions (seasonality) and over a broader range of forest types and canopy structure has to be evaluated. Numéro de notice : A2022-787 Affiliation des auteurs : IGN (1940-2011) Thématique : FORET/IMAGERIE Nature : Article DOI : 10.1016/j.jag.2022.102999 Date de publication en ligne : 05/09/2022 En ligne : https://doi.org/10.1016/j.jag.2022.102999 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=101893 [article]

Calibration radiométrique et géométrique d'une caméra fish-eye pour la mesure de l'hémisphère de luminance incidente / Manchun Lei (2022)

Public Titre : Calibration radiométrique et géométrique d'une caméra fish-eye pour la mesure de l'hémisphère de luminance incidente Type de document : Rapport Auteurs : Manchun Lei ; Christophe Meynard , Auteur ; Jean-Michaël Muller , Auteur ; Christian Thom , Auteur Editeur : Saint-Mandé : Institut national de l'information géographique et forestière - IGN Année de publication : 2022 Importance : 34 p. Note générale : bibliographie Langues : Français (fre) Descripteur : [Vedettes matières IGN] Acquisition d'image(s) et de donnée(s) [Termes IGN] caméra numérique [Termes IGN] étalonnage géométrique [Termes IGN] étalonnage radiométrique [Termes IGN] image hémisphérique [Termes IGN] MicMac Résumé : (auteur) [introduction] [...] Nous avons développé un imageur hémisphérique de luminance incidente basé sur une caméra légère fish-eye. La particularité de cet imageur est sa légèreté et donc sa mobilité, ce qui est pratique pour mesurer l’hémisphère de luminance incidente des différentes surfaces (sol et façade) dans l’environnement. Ce rapport a pour objectif de documenter la procédure de calibration radiométrique et géométrique réalisée, depuis la description théorique jusqu’aux considérations pratiques. La phase de validation est aussi présentée. Note de contenu : 1 Introduction 2 Description des instruments 3 Calibration de bruit d'obscurité 4 Calibration radiométrique 5 Calibration géométrique Numéro de notice : 17734 Affiliation des auteurs : UGE-LASTIG (2020- ) Thématique : IMAGERIE Nature : Rapport nature-HAL : Rapport DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100625

Event-driven feature detection and tracking for visual SLAM / Ignacio Alzugaray (2022)  

Public Titre : Event-driven feature detection and tracking for visual SLAM Type de document : Thèse/HDR Auteurs : Ignacio Alzugaray, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2022 Note générale : bibliographie thesis submitted to attain the degree of Doctor of Sciences of ETH Zurich Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] caméra d'événement [Termes IGN] cartographie et localisation simultanées [Termes IGN] détection d'objet [Termes IGN] extraction de traits caractéristiques [Termes IGN] image floue [Termes IGN] reconnaissance de formes [Termes IGN] séquence d'images [Termes IGN] vision par ordinateur Index. décimale : THESE Thèses et HDR Résumé : (auteur) Traditional frame-based cameras have become the de facto sensor of choice for a multitude of applications employing Computer Vision due to their compactness, low cost, ubiquity, and ability to provide information-rich exteroceptive measurements. Despite their dominance in the field, these sensors exhibit limitations in common, real-world scenarios where detrimental effects, such as motion blur during high-speed motion or over-/underexposure in scenes with poor illumination, are prevalent. Challenging the dominance of traditional cameras, the recent emergence of bioinspired event cameras has opened up exciting research possibilities for robust perception due to their high-speed sensing, High-Dynamic-Range capabilities, and low power consumption. Despite their promising characteristics, event cameras present numerous challenges due to their unique output: a sparse and asynchronous stream of events, only capturing incremental perceptual changes at individual pixels. This radically different sensing modality renders most of the traditional Computer Vision algorithms incompatible without substantial prior adaptation, as they are initially devised for processing sequences of images captured at fixed frame-rate. Consequently, the bulk of existing event-based algorithms in the literature have opted to discretize the event stream into batches and process them sequentially, effectively reverting to frame-like representations in an attempt to mimic the processing of image sequences from traditional sensors. Such event-batching algorithms have demonstrably outperformed other alternative frame-based algorithms in scenarios where the quality of conventional intensity images is severely compromised, unveiling the inherent potential of these new sensors and popularizing them. To date, however, many newly designed event-based algorithms still rely on a contrived discretization of the event stream for its processing, suggesting that the full potential of event cameras is yet to be harnessed by processing their output more naturally. This dissertation departs from the mere adaptation of traditional frame-based approaches and advocates instead for the development of new algorithms integrally designed for event cameras to fully exploit their advantageous characteristics. In particular, the focus of this thesis lies on describing a series of novel strategies and algorithms that operate in a purely event-driven fashion, \ie processing each event as soon as it gets generated without any intermediate buffering of events into arbitrary batches and thus avoiding any additional latency in their processing. Such event-driven processes present additional challenges compared to their simpler event-batching counterparts, which, in turn, can largely be attributed to the requirement to produce reliable results at event-rate, entailing significant practical implications for their deployment in real-world applications. The body of this thesis addresses the design of event-driven algorithms for efficient and asynchronous feature detection and tracking with event cameras, covering alongside crucial elements on pattern recognition and data association for this emerging sensing modality. In particular, a significant portion of this thesis is devoted to the study of visual corners for event cameras, leading to the design of innovative event-driven approaches for their detection and tracking as corner-events. Moreover, the presented research also investigates the use of generic patch-based features and their event-driven tracking for the efficient retrieval of high-quality feature tracks. All the developed algorithms in this thesis serve as crucial stepping stones towards a completely event-driven, feature-based Simultaneous Localization And Mapping (SLAM) pipeline. This dissertation extends upon established concepts from state-of-the-art, event-driven methods and further explores the limits of the event-driven paradigm in realistic monocular setups. While the presented approaches solely rely on event-data, the gained insights are seminal to future investigations targeting the combination of event-based vision with other, complementary sensing modalities. The research conducted here paves the way towards a new family of event-driven algorithms that operate efficiently, robustly, and in a scalable manner, envisioning a potential paradigm shift in event-based Computer Vision. Note de contenu : 1- Introduction 2- Contribution 3- Conclusion and outlook Numéro de notice : 28699 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD Thesis : Sciences : ETH Zurich : 2022 DOI : sans En ligne : https://www.research-collection.ethz.ch/handle/20.500.11850/541700 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100470

Implementation of close range photogrammetry using modern non-metric digital cameras for architectural documentation / Mariem A. Elhalawani in Geodesy and cartography, vol 47 n° 1 (January 2021)  

Public [article]  inGeodesy and cartography > vol 47 n° 1 (January 2021) . - pp 45 - 53 Titre : Implementation of close range photogrammetry using modern non-metric digital cameras for architectural documentation Type de document : Article/Communication Auteurs : Mariem A. Elhalawani, Auteur ; Zaki M. Zeidan, Auteur ; Ashraf A. Beshr, Auteur Année de publication : 2021 Article en page(s) : pp 45 - 53 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique [Termes IGN] caméra numérique [Termes IGN] Egypte [Termes IGN] façade [Termes IGN] longueur focale [Termes IGN] monument historique [Termes IGN] mosquée [Termes IGN] photogrammétrie architecturale [Termes IGN] point d'appui [Termes IGN] reconstruction 3D [Termes IGN] station de travail de photogrammétrie numérique Résumé : (auteur) The development of applied geodetic techniques for mapping and documentation of historical structures, buildings and sites is an important and vital purpose for contribution of any recording of cultural heritage for any country such as Egypt. This is done to preserve and restore any valuable architectural or other cultural monument, as a support to architectural, archaeological and other art-historical research throughout the ages. The purpose of this paper is to use close range photogrammetry technique (CRP) to reconstruct 3D model of architectural and historical mosque facade and comparing the accuracy of using digital commercial non-metric cameras with different resolutions and metric camera with flatbed scanner and photogrammetric scanner for architectural building documentation. El-Nasr Mosque façade in Mansoura city, Egypt was chosen as a case study in this paper. At first, twenty five points were selected at mosque façade at different elevations and distributed at different façade surfaces and observed using total station. Some of these points were selected as control points and the others were selected as check points to validate the results. Effect of control point’s number on image processing and analysis is also studied. Three cameras positions were selected for imaging to get the full details of mosque façade. Close range Digital Workstation (CDW) program was used for processing and analysis of multiple images. The results are indicated that close range photogrammetry using metric camera with photogrammetry scanner instead of flatbed scanner in technique is accurate enough to be beneficial in 3D architectural building documentation. Digital cameras with CRP technique give up different accuracy that depends mainly on the resolution of cameras and camera specifications. Numéro de notice : A2021-452 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3846/gac.2021.11269 Date de publication en ligne : 26/04/2021 En ligne : https://doi.org/10.3846/gac.2021.11269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97873 [article]

Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain / R. Niederheiser in GIScience and remote sensing, vol 58 n° 1 (February 2021)  

Public [article]  inGIScience and remote sensing > vol 58 n° 1 (February 2021) . - pp 120 - 137 Titre : Using automated vegetation cover estimation from close-range photogrammetric point clouds to compare vegetation location properties in mountain terrain Type de document : Article/Communication Auteurs : R. Niederheiser, Auteur ; M. Winkler, Auteur ; V. Di Cecco, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 120 - 137 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie terrestre [Termes IGN] Alpes [Termes IGN] caméra numérique [Termes IGN] carte de la végétation [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] classification semi-dirigée [Termes IGN] couvert végétal [Termes IGN] distribution de Poisson [Termes IGN] données topographiques [Termes IGN] indice de végétation [Termes IGN] module linéaire [Termes IGN] montagne [Termes IGN] occupation du sol [Termes IGN] photogrammétrie métrologique [Termes IGN] semis de points Résumé : (auteur) In this paper we present a low-cost approach to mapping vegetation cover by means of high-resolution close-range terrestrial photogrammetry. A total of 249 clusters of nine 1 m2 plots each, arranged in a 3 × 3 grid, were set up on 18 summits in Mediterranean mountain regions and in the Alps to capture images for photogrammetric processing and in-situ vegetation cover estimates. This was done with a hand-held pole-mounted digital single-lens reflex (DSLR) camera. Low-growing vegetation was automatically segmented using high-resolution point clouds. For classifying vegetation we used a two-step semi-supervised Random Forest approach. First, we applied an expert-based rule set using the Excess Green index (ExG) to predefine non-vegetation and vegetation points. Second, we applied a Random Forest classifier to further enhance the classification of vegetation points using selected topographic parameters (elevation, slope, aspect, roughness, potential solar irradiation) and additional vegetation indices (Excess Green Minus Excess Red (ExGR) and the vegetation index VEG). For ground cover estimation the photogrammetric point clouds were meshed using Screened Poisson Reconstruction. The relative influence of the topographic parameters on the vegetation cover was determined with linear mixed-effects models (LMMs). Analysis of the LMMs revealed a high impact of elevation, aspect, solar irradiation, and standard deviation of slope. The presented approach goes beyond vegetation cover values based on conventional orthoimages and in-situ vegetation cover estimates from field surveys in that it is able to differentiate complete 3D surface areas, including overhangs, and can distinguish between vegetation-covered and other surfaces in an automated manner. The results of the Random Forest classification confirmed it as suitable for vegetation classification, but the relative feature importance values indicate that the classifier did not leverage the potential of the included topographic parameters. In contrast, our application of LMMs utilized the topographic parameters and was able to reveal dependencies in the two biomes, such as elevation and aspect, which were able to explain between 87% and 92.5% of variance. Numéro de notice : A2021-258 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1080/15481603.2020.1859264 Date de publication en ligne : 13/01/2021 En ligne : https://doi.org/10.1080/15481603.2020.1859264 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97295 [article]

Intelligent embedded camera for robust object tracking on mobile platform / Imane Salhi (2021)  

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Multispectral object detection / Heng Zhang (2021)  

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Vers un protocole de calibration de caméras statiques à l'aide d'un drone / Jean-François Villeforceix (2021)  

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Towards online UAS‐based photogrammetric measurements for 3D metrology inspection / Fabio Menna in Photogrammetric record, vol 35 n° 172 (December 2020)  

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Structure from motion for complex image sets / Mario Michelini in ISPRS Journal of photogrammetry and remote sensing, vol 166 (August 2020)  

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Improved depth estimation for occlusion scenes using a light-field camera / Changkun Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 86 n° 7 (July 2020)  

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Geometric modelling and calibration of a spherical camera imaging system / Derek D. Lichti in Photogrammetric record, vol 35 n° 170 (June 2020)  

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Epipolar rectification of a generic camera / Marc Pierrot-Deseilligny (2020)    

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Geographic Information Systems in geospatial intelligence / Rustam B. Rustamov (2020)  

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Improved camera distortion correction and depth estimation for lenslet light field camera / Changkun Yang in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 3 (March 2019)  

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