Descripteur
Termes IGN > imagerie
imagerie
Commentaire :
Terme regroupant photographies et images issues de différents capteurs.
|
Documents disponibles dans cette catégorie (7809)
![](./images/expand_all.gif)
![](./images/collapse_all.gif)
Etendre la recherche sur niveau(x) vers le bas
Point cloud registration and mitigation of refraction effects for geomonitoring using long-range terrestrial laser scanning / Ephraim Friedli (2020)
![]()
Titre : Point cloud registration and mitigation of refraction effects for geomonitoring using long-range terrestrial laser scanning Type de document : Thèse/HDR Auteurs : Ephraim Friedli, Auteur Editeur : Zurich : Eidgenossische Technische Hochschule ETH - Ecole Polytechnique Fédérale de Zurich EPFZ Année de publication : 2020 Note générale : bibliographie
A dissertation submitted to attain the degree of Doctor of Sciences of ETH ZurichLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] réfraction atmosphérique
[Termes IGN] scène
[Termes IGN] scène intérieure
[Termes IGN] semis de points
[Termes IGN] surveillance géologique
[Termes IGN] télémétrie laser terrestreRésumé : (auteur) Monitoring of man-made structures and regions posing potential natural hazards plays a pivotal role in preventing human and economic losses and thus, has been a central topic in geodesy for a long time. However, while the monitored objects (e.g. landslides) often are areal phenomena, classic geodetic monitoring still applies point-based measurement systems. Over the past few years, area-based methods (e.g. terrestrial laser scanning) are closing this gap and allow the acquisition of object geometry or surfaces with high spatial resolution and high accuracy. However, with the use of terrestrial laser scanning (TLS) for monitoring, new challenges arise. Two examples of such challenges are the scan registration and the mitigation of time-varying artefacts. When TLS is used for monitoring, scans over a sequence of epochs have to be acquired. The different scans have to be transformed into a common stable reference frame before changes between epochs can be analysed. This process is called registration and well-established solutions exist for scanning at close-range or scenes without changes between the scans. However, the standard approaches are not applicable for scenes with significant deformations and observed from long-range, a scenario typically encountered in the monitoring of natural hazards. Thus, in such monitoring cases, the need for other approaches exists. Furthermore, when scanning over long ranges, time-varying artefacts affect the resulting point clouds. These artefacts can be caused e.g. by atmospheric refraction and may result in apparent displacements of up to a few decimetres. Due to the temporarily and spatially varying air density distribution during the time required for the individual scan acquisition, the resulting point clouds are distorted systematically, but non-linearly. To tackle these two challenges, a data-driven registration algorithm for scan pairs of scenes with significant changes between epochs and an investigation of the time-varying artifacts are presented. The core of the registration approach is a data-driven classification of the scene into stable and unstable areas and a registration based on the stable areas only. The proposed registration algorithm is successfully applied to two different scenarios (an indoor and an outdoor scene). For both scenarios, the algorithm performs well with a sensibly chosen set of parameters. In addition, the algorithm is successfully applied to scans from an experimental study carried out in the scope of the investigation of the time-varying artefacts. This investigation focuses on atmospheric refraction and is based on numerical simulation and an experimental study, that allows a clear detection and analysis of the atmospheric effects. The numerical simulation demonstrates that these effects can cause apparent displacements on a decimeter-level, resulting from a combination of the measurement ray curvature and the terrain inclination. The results are corroborated by the experimental study. Additionally, the data from the experiment show that the magnitude of the effects from atmospheric refraction varies with time of the day. Currently, there is no solution to a data-driven or forward-modeling based compensation available but the study herein indicates that the effects might be mostly negligible when using only scans acquired at certain times in the evening. Numéro de notice : 17655 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : Doctoral thesis : Sciences : ETH Zurich : 2020 En ligne : http://dx.doi.org/10.3929/ethz-b-000409052 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97915 Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests / Sadeepa Jayathunga in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)
![]()
[article]
Titre : Potential of UAV photogrammetry for characterization of forest canopy structure in uneven-aged mixed conifer–broadleaf forests Type de document : Article/Communication Auteurs : Sadeepa Jayathunga, Auteur ; Toshiaki Owari, Auteur ; Satoshi Tsuyuki, Auteur ; Yasumasa Hirata, Auteur Année de publication : 2020 Article en page(s) : pp 53 - 73 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection
[Termes IGN] analyse de groupement
[Termes IGN] couvert forestier
[Termes IGN] forêt de feuillus
[Termes IGN] gestion forestière
[Termes IGN] hauteur des arbres
[Termes IGN] image captée par drone
[Termes IGN] photogrammétrie aérienne
[Termes IGN] photographie aérienne latérale
[Termes IGN] Pinophyta
[Termes IGN] structure d'un peuplement forestierRésumé : (auteur) Forest canopy structure is an important parameter in multipurpose forest management. An understanding of forest structure plays a particularly important role in the management of uneven-aged forests. The identification of vertical and horizontal variations in forest canopy structure using a ground-based survey is resource intensive, hence often demands for alternative data sources. In this study, one of the advanced remote sensing (RS) techniques, i.e. digital aerial photogrammetry was used to characterize forest canopy structure in a mixed conifer–broadleaf forest. We used aerial imagery acquired with a fixed-wing unmanned aerial vehicle (UAV) platform to produce RS metrics that could be used to classify and map forest structure types at landscape scale. Our results demonstrated that few structural and spectral metrics derived from UAV photogrammetric data, e.g. mean height, standard deviation of height, canopy cover, and percentage broadleaf vegetation cover, could characterize the forest structure across landscapes, particularly at the forest management compartment level, in a limited amount of time. We used cluster analysis for classification of forest structure types and identified five forest structure classes with varying levels of forest canopy structural complexity: (1) short, open-canopy, conifer-dominated structure; (2) short, dense-canopy, broadleaf-dominated structure; (3) tall, closed-canopy, broadleaf-dominated structure; (4) very tall, closed-canopy, conifer-dominated structure with a relatively high degree of variation in canopy height; and (5) very tall, closed-canopy, conifer-dominated structure with a relatively low degree of variation in canopy height. These classes showed relationships with forest management activities (e.g. selection harvesting) and natural disturbances (e.g. typhoon damage). Spatial distribution of forest canopy structural complexity that was revealed in this study is capable of providing important information for forest management planning and habitat modelling. Further, the simple, and flexible data-driven method used in this study to characterize forest structure has the potential to be applied with necessary changes over larger landscapes and different forest types for characterizing and mapping forest structural complexity. Numéro de notice : A2020-210 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/01431161.2019.1648900 Date de publication en ligne : 01/08/2019 En ligne : https://doi.org/10.1080/01431161.2019.1648900 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94892
in International Journal of Remote Sensing IJRS > vol 41 n° 1 (01 - 08 janvier 2020) . - pp 53 - 73[article]
Titre : Processing and analysis of hyperspectral data Type de document : Monographie Auteurs : Jie Chen, Éditeur scientifique ; Yingying Song, Éditeur scientifique ; Hengchao Li, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 Importance : 140 p. ISBN/ISSN/EAN : 978-1-78985-109-0 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] apprentissage automatique
[Termes IGN] apprentissage profond
[Termes IGN] classification dirigée
[Termes IGN] classification non dirigée
[Termes IGN] image à haute résolution
[Termes IGN] image hyperspectrale
[Termes IGN] image proche infrarouge
[Termes IGN] qualité des eaux
[Termes IGN] signature spectrale
[Termes IGN] turbidité des eauxRésumé : (Editeur) Hyperspectral imagery has received considerable attention in the last decade as it provides rich spectral information and allows the analysis of objects that are unidentifiable by traditional imaging techniques. It has a wide range of applications, including remote sensing, industry sorting, food analysis, biomedical imaging, etc. However, in contrast to RGB images from which information can be intuitively extracted, hyperspectral data is only useful with proper processing and analysis. This book covers theoretical advances of hyperspectral image processing and applications of hyperspectral processing, including unmixing, classification, super-resolution, and quality estimation with classical and deep learning methods. Note de contenu : Section One - Theoretical advances of hyperspectral image processing
Chapter 1 - Hyperspectral endmember extraction techniques
Chapter 2 - Hyperspectral image classification
Chapter 3 - Hyperspectral image super-resolution using optimization and DCNN-based methods
Chapter 4 - Fast chaotic encryption for hyperspectral images
Section Two - Applications of hyperspectral image processing
Chapter 5 - NIR hyperspectral imaging for mapping of moisture content distribution in tea buds during dehydration
Chapter 6 - Use of hyperspectral remote sensing to estimate water qualityNuméro de notice : 26560 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.78179 En ligne : http://doi.org/10.5772/intechopen.78179 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98243
Titre : Radar interferometry of unstable slopes : an application to rock glaciers Type de document : Mémoire Auteurs : Theeba Raveendran, Auteur Editeur : Champs-sur-Marne : Ecole nationale des sciences géographiques ENSG Année de publication : 2020 Importance : 31 p. Format : 21 x 30 cm Note générale : Bibliographie
Rapport de projet pluridisciplinaire, cycle ING2Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] Colorado (Etats-Unis)
[Termes IGN] données spatiotemporelles
[Termes IGN] image Sentinel-SAR
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] montagne
[Termes IGN] rocher
[Termes IGN] vitesse de déplacementIndex. décimale : PROJET Mémoires : Rapports de projet - stage des ingénieurs de 2e année Résumé : (Auteur) Le projet vise à calculer la vitesse de déplacement de glaciers rocheux par interférométrie radar à partir d’images radar acquises grâce à la constellation Sentinel-1 (ESA). Les images utilisées pour l’étude ont été acquises de juin à septembre 2018 et de juillet à octobre 2019. Les glaciers rocheux étudiés se situent au nord-est de Telluride, dans la chaîne de montagnes de San Juan, Colorado, Etats-Unis. La vitesse de déplacement des glaciers rocheux a déjà été quantifiée par photogrammétrie à partir d’images aériennes sur dix, vingt et trente ans. L’interférométrie radar (InSAR) apparaît donc comme une méthode complémentaire, permettant de mettre en évidence les déformations de l’ordre du millimètre à l’échelle d’une saison. De plus, ce projet permet aussi de manière générale d’évaluer la méthode InSAR pour l’étude des déplacements des glaciers rocheux. Les images ont été traitées à l’aide du logiciel libre de l’ESA, SNAP. La vitesse de déplacement des glaciers rocheux a été calculée sur une période de douze jours, aux étés 2018 et 2019. Six interférogrammes ont été réalisés pour cette étude. Les résultats finaux montrent que l’interférométrie radar permet bien de renforcer les résultats obtenus par photogrammétrie. Les glaciers rocheux étudiés ont des vitesses de déplacement allant de cinq millimètres à trois centimètres sur les périodes étudiées. On observe ainsi une accélération du déplacement à l’approche de l’hiver. Note de contenu :
Introduction
1. Context
1.1 Background
1.2 Study area
2. Source data
2.1 Sentinel-1 mission
2.2 Images
3. Processing
3.1 Principle of interferometry
3.2 Calculation of rock glaciers’ creep
4. Results and discussion
4.1 Displaying results
4.2 Analysis
ConclusionNuméro de notice : 26391 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire de projet pluridisciplinaire Organisme de stage : Université d’Oslo Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96068 Documents numériques
Titre : Recent advances in image restoration with applications to real world problems Type de document : Monographie Auteurs : Chiman Kwan, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2020 ISBN/ISSN/EAN : 978-1-83968-356-5 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image
[Termes IGN] apprentissage non-dirigé
[Termes IGN] données spatiotemporelles
[Termes IGN] extraction de modèle
[Termes IGN] fusion d'images
[Termes IGN] image hyperspectrale
[Termes IGN] modèle numérique de terrain
[Termes IGN] reconstruction 3D
[Termes IGN] restauration d'imageRésumé : (Editeur) In the past few decades, imaging hardware has improved tremendously in terms of resolution, making widespread usage of images in many diverse applications on Earth and planetary missions. However, practical issues associated with image acquisition are still affecting image quality. Some of these issues such as blurring, measurement noise, mosaicing artifacts, low spatial or spectral resolution, etc. can seriously affect the accuracy of the aforementioned applications. This book intends to provide the reader with a glimpse of the latest developments and recent advances in image restoration, which includes image super-resolution, image fusion to enhance spatial, spectral resolution, and temporal resolutions, and the generation of synthetic images using deep learning techniques. Some practical applications are also included. Note de contenu :
1. Introductory Chapter: Recent Advances in Image Restoration
2. Resolution Enhancement of Hyperspectral Data Exploiting Real Multi-Platform Data
3. Application of Deep Learning Approaches for Enhancing Mastcam Images
4. Generative Adversarial Networks for Visible to Infrared Video Conversion
5. Style-Based Unsupervised Learning for Real-World Face Image Super-Resolution
6. Spatiotemporal Fusion in Remote Sensing
7. 3D Reconstruction through Fusion of Cross-View Images
8. Practical Digital Terrain Model Extraction Using Image Inpainting TechniquesNuméro de notice : 26695 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.90607 Date de publication en ligne : 04/11/2020 En ligne : https://doi.org/10.5772/intechopen.90607 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99081 Recherche multimodale d'images aériennes multi-date à l'aide d'un réseau siamois / Margarita Khokhlova (2020)
![]()
PermalinkReconnaissance automatique d’objets pour le jumeau numérique ferroviaire à partir d’imagerie aérienne / Valentin Desbiolles (2020)
PermalinkRegional-scale forest mapping over fragmented landscapes using global forest products and Landsat time series classification / Viktor Myroniuk in Remote sensing, vol 12 n° 1 (January 2020)
PermalinkRelevés par Lidar mobile de cours d’eau et intégration des profils aux relevés bathymétriques réalisés par sondeur mono-faisceau / Guillaume Didier (2020)
PermalinkPermalinkPermalinkPermalinkPermalinkPermalinkPermalinkSatellite image time series classification with pixel-set encoders and temporal self-attention / Vivien Sainte Fare Garnot (2020)
![]()
PermalinkSimplicial complexes reconstruction and generalisation of 3d lidar data in urban scenes / Stéphane Guinard (2020)
PermalinkSimulation and analysis of photogrammetric UAV image blocks - Influence of camera calibration error / Yilin Zhou in Remote sensing, vol 12 n° 1 (January 2020)
PermalinkSimulation d’éclairements des surfaces ombrées en zone urbaine par transfert radiatif 3D (modèle DART) / Yulu Xi (2020)
PermalinkPermalinkStreambank topography: an accuracy assessment of UAV-based and traditional 3D reconstructions / Benjamin U. Meinen in International Journal of Remote Sensing IJRS, vol 41 n° 1 (01 - 08 janvier 2020)
PermalinkSuperpixel-enhanced deep neural forest for remote sensing image semantic segmentation / Li Mi in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
PermalinkSurface soil moiture retrieval over irrigated wheat crops in semi-arid areas using Sentinel-1 data / Nadia Ouaadi (2020)
PermalinkA systematic evaluation of influence of image selection process on remote sensing-based burn severity indices in North American boreal forest and tundra ecosystems / Dong Chen in ISPRS Journal of photogrammetry and remote sensing, vol 159 (January 2020)
PermalinkSystème de traitement d’images temps réel dédié à la mesure de champs denses de déplacements et de déformations / Seyfeddine Boukhtache (2020)
PermalinkPermalinkTest du potentiel de l’imagerie satellite haute résolution pour le suivi des mouvements gravitaires des falaises crayeuses de Seine-Maritime / Zoé Stroebele (2020)
PermalinkTrajectoires paysagères des cônes de déjection torrentiels des Alpes du nord (Maurienne et Tarentaise) / Thérèse Hugerot (2020)
PermalinkUnderwater calibration in near real time: Focus on detection optimized by AI and selection of calibration patterns / Loïca Avanthey (2020)
PermalinkUnsupervised satellite image time series analysis using deep learning techniques / Ekaterina Kalinicheva (2020)
PermalinkUsing remote sensing to assess the effect of time of day on the spatial and temporal variation of LST in urban areas / Akram Abdulla (2020)
PermalinkUso de QGIS en la teledetección, Vol. 2. QGIS y sus aplicaciones en la agricultura y la silvicultura / Nicolas Baghdadi (2020)
PermalinkUso de QGIS en la teledetección, Vol. 4. QGIS y sus aplicaciones en agua y en gestion del riego / Nicolas Baghdadi (2020)
PermalinkVers une occupation du sol France entière par imagerie satellite à très haute résolution / Tristan Postadjian (2020)
PermalinkA versatile and efficient data fusion methodology for heterogeneous airborne LiDAR and optical imagery data acquired under unconstrained conditions / Thanh Huy Nguyen (2020)
PermalinkVery high resolution land cover mapping of urban areas at global scale with convolutional neural network / Thomas Tilak (2020)
PermalinkPermalinkWater stress detection over irrigated wheat crops in semi-arid areas using the diurnal differences of Sentinel-1 backscatter / Nadia Ouaadi (2020)
PermalinkPermalinkShip identification and characterization in Sentinel-1 SAR images with multi-task deep learning / Clément Dechesne in Remote sensing, Vol 11 n° 24 (December-2 2019)
PermalinkAn implicit radar convolutional burn index for burnt area mapping with Sentinel-1 C-band SAR data / Puzhao Zhang in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
PermalinkApplication of photogrammetry to generate quantitative geobody data in ephemeral fluvial systems / Charlotte L. Priddy in Photogrammetric record, vol 34 n° 168 (December 2019)
PermalinkCombining Sentinel-1 and Sentinel-2 Satellite image time series for land cover mapping via a multi-source deep learning architecture / Dino Lenco in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
PermalinkCombining thermal imaging with photogrammetry of an active volcano using UAV: an example from Stromboli, Italy / Zoë E. Wakeford in Photogrammetric record, vol 34 n° 168 (December 2019)
PermalinkContextual filtering methods based on the subbands and subspaces decomposition of complex SAR interferograms / Saoussen Belhadj-Aissa in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol 12 n° 12 (December 2019)
PermalinkHalf a percent of labels is enough: efficient animal detection in UAV imagery using deep CNNs and active learning / Benjamin Kellenberger in IEEE Transactions on geoscience and remote sensing, vol 57 n° 12 (December 2019)
PermalinkInside the ice shelf: using augmented reality to visualise 3D lidar and radar data of Antarctica / Alexandra L. Boghosian in Photogrammetric record, vol 34 n° 168 (December 2019)
PermalinkA learning approach to evaluate the quality of 3D city models / Oussama Ennafii in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 12 (December 2019)
![]()
PermalinkA low‐cost open‐source workflow to generate georeferenced 3D SfM photogrammetric models of rocky outcrops / Laurent Froideval in Photogrammetric record, vol 34 n° 168 (December 2019)
PermalinkMatching of TerraSAR-X derived ground control points to optical image patches using deep learning / Tatjana Bürgmann in ISPRS Journal of photogrammetry and remote sensing, Vol 158 (December 2019)
Permalink