Descripteur
Documents disponibles dans cette catégorie (3880)
Ajouter le résultat dans votre panier
Visionner les documents numériques
Affiner la recherche Interroger des sources externes
Etendre la recherche sur niveau(x) vers le bas
Remote sensing and GIS / Basudeb Bhatta (2021)
Titre : Remote sensing and GIS Type de document : Guide/Manuel Auteurs : Basudeb Bhatta, Auteur Mention d'édition : 3ème édition Editeur : Oxford, Londres, ... : Oxford University Press Année de publication : 2021 Importance : 752 p. Format : 24 x 18 cm ISBN/ISSN/EAN : 978-0-19-949664-8 Note générale : Bibliographie
additional reading material with Oxford arealLangues : Anglais (eng) Descripteur : [Vedettes matières IGN] Télédétection
[Termes IGN] acquisition d'images
[Termes IGN] airborne multispectral scanner
[Termes IGN] analyse spatiale
[Termes IGN] Global Navigation Satellite System
[Termes IGN] image hyperspectrale
[Termes IGN] image thermique
[Termes IGN] interféromètrie par radar à antenne synthétique
[Termes IGN] Lidar
[Termes IGN] modèle numérique de surface
[Termes IGN] modèle numérique de terrain
[Termes IGN] modélisation 3D
[Termes IGN] orthorectification
[Termes IGN] Passive and Active L and S band Sensor
[Termes IGN] photographie aérienne
[Termes IGN] Satellite Microwave Radiometer
[Termes IGN] scène 3D
[Termes IGN] stéréoscopie
[Termes IGN] système d'information géographique
[Termes IGN] traitement d'image
[Termes IGN] visualisation 3DIndex. décimale : 35.00 Télédétection - généralités Résumé : (Editeur) Beginning with the history and basic concepts of remote sensing and GIS, the book gives an exhaustive coverage of optical, thermal, and microwave remote sensing, global navigation satellite systems (such as GPS and IRNSS), digital photogrammetry, visual image analysis, digital image processing, spatial and attribute data model, geospatial analysis, and planning, implementation, and management of GIS. It also presents the modern trends of remote sensing and GIS with an illustrated discussion on its numerous applications. Note de contenu : 1. Concept of Remote Sensing
1.1 Introduction
1.2 Distance of Remote Sensing
1.3 Definition of Remote Sensing
1.4 Remote Sensing: Art and/or Science
1.5 Data
1.6 Remote Sensing Process
1.7 Source of Energy
1.8 Interaction with Atmosphere
1.9 Interaction with Target
1.9.1 Hemispherical Absorptance, Transmittance, and Reflectan
1.10 Interaction with the Atmosphere Again
1.11 Recording of Energy by Sensor
1.12 Transmission, Reception, and Processing
1.13 Interpretation and Analysis
1.14 Applications of Remote Sensing
1.15 Advantages of Remote Sensing
1.16 Limitations of Remote Sensing
1.17 Ideal Remote Sensing System
2. Types of Remote Sensing and Sensor Characteristics
2.1 Introduction
2.2 Types of Remote Sensing
2.3 Characteristics of Images
2.4 Orbital Characteristics of Satellite
2.5 Remote Sensing Satellites
2.6 Concept of Swath
2.7 Concept of Nadir
2.8 Sensor Resolutions
2.9 Image Referencing System
2.9.1 Path
2.9.2 Row
2.9.3 Orbital Calendar
3. History of Remote Sensing and Indian Space Program
3.1 Introduction
3.2 The Early Age
3.3 The Middle Age
3.4 The Modern Age or Space Age
3.5 Indian Space Program
4. Photographic Imaging
4.1 Introduction
4.2 Camera Systems
4.3 Types of Camera
4.4 Filter
4.5 Film
4.6 Geometry of Aerial Photography
4.7 Ideal Time and Atmosphere for Aerial Remote Sensing
5. Digital Imaging
5.1 Introduction
5.2 Digital Image
5.3 Sensor
5.4 Imaging by Scanning Technique
5.5 Hyper-spectral Imaging
5.6 Imaging By Non-scanning Technique
5.7 Thermal Remote Sensing
5.8 Other Sensors
6. Microwave Remote Sensing
6.1 Introduction
6.2 Passive Microwave Remote Sensing
6.3 Active Microwave Remote Sensing
6.4 Radar Imaging
6.5 Airborne Versus Space-Borne Radars
6.6 Radar Systems
7. Ground-truth Data and Global Positioning System
7.1 Introduction
7.2 Requirements of Ground-Truth Data
7.3 Instruments for Ground Truthing
7.4 Parameters of Ground Truthing
7.5 Factors of Spectral Measurement
7.6 Global Navigation Satellite System
8. Photogrammetry
8.1 Introduction
8.2 Development of Photogrammetry
8.3 Classification of Photogrammetry
8.4 Photogrammetric Process
8.5 Acquisition of Imagery and its Support Data
8.6 Orientation and Triangulation
8.7 Stereo Model Compilation
8.8 Stereoscopic 3D Viewing
8.9 Stereoscopic Measurement
8.10 DTM/DEM Generation
8.11 Contour Map Generation
8.12 Orthorectification
8.13 3D Feature Extraction
8.14 3D Scene Modelling
8.15 Photogrammetry and LiDAR
8.16 Radargrammetry and Radar Interferometry
8.17 Limitations of Photogrammetry
9. Visual Image Interpretation
9.1 Introduction
9.2 Information Extraction by Human and Computer
9.3 Remote Sensing Data Products
9.4 Border or Marginal Information
9.5 Image Interpretation
9.6 Elements of Visual Image Interpretation
9.7 Interpretation Keys
9.8 Generation of Thematic Maps
9.9 Thermal Image Interpretation
9.10 Radar Image Interpretation
10. Digital Image Processing
10.1 Introduction
10.2 Categorization of Image Processing
10.3 Image Processing Systems
10.4 Digital Image
10.5 Media for Digital Data Recording, Storage, and Distribution
10.6 Data Formats of Digital Image
10.7 Header Information
10.8 Display of Digital Image
10.9 Pre-processing
10.10 Image Enhancement
10.11 Image Transformation
10.12 Image Classification
11. Data Integration, Analysis, and Presentation
11.1 Introduction
11.2 Multi-approach of Remote Sensing
11.3 Integration with Ground Truth and Other Ancillary Data
11.4 Integration of Transformed Data
11.5 Integration with GIS
11.6 Process of Remote Sensing Data Analysis
11.7 The Level of Detail
11.8 Limitations of Remote Sensing Data Analysis
11.9 Presentation
12. Applications of Remote Sensing
12.1 Introduction
12.2 Land Cover and Land Use
12.3 Agriculture
12.4 Forestry
12.5 Geology
12.6 Geomorphology
12.7 Urban Applications
12.8 Hydrology
12.9 Mapping
12.10 Oceans and Coastal Monitoring
12.11 Monitoring of Atmospheric Constituents
PART II Geographic Information Systems and Geospatial Analysis
13. Concept of Geographic Information Systems
13.1 Introduction
13.2 Definitions of GIS
13.3 Key Components of GIS
13.4 GIS-An Integration of Spatial and Attribute Information
13.5 GIS-Three Views of Information System
13.6 GIS and Related Terms
13.7 GIS-A Knowledge Hub
13.8 GIS-A Set of Interrelated Subsystems
13.9 GIS-An Information Infrastructure
13.10 Origin of GIS
14. Functions and Advantages of GIS
14.1 Introduction
14.2 Functions of GIS
14.3 Application Areas of GIS
14.4 Advantages of GIS
14.5 Functional Requirements of GIS
14.6 Limitations of GIS
15. Spatial Data Model
15.1 Introduction
15.2 Spatial, Thematic, and Temporal Dimensions of Geographic Data
15.3 Spatial Entity and Object
15.4 Spatial Data Model
15.5 Raster Data Model
15.6 Vector Data Model
15.7 Raster versus Vector
15.8 Object-Oriented Data Model
15.9 File Formats of Spatial Data
16. Attribute Data Management and Metadata Concept
16.1 Introduction
16.2 Concept of Database and DBMS
16.3 Advantages of DBMS
16.4 Functions of DBMS
16.5 File and Data Access
16.6 Data Models
16.7 Database Models
16.8 Data Models in GIS
16.9 Concept of SQL
16.10 Concept of Metadata
17. Process of GIS
17.1 Introduction
17.2 Data Capture
17.3 Data Sources
17.4 Data Encoding Methods
17.5 Linking of Spatial and Attribute Data
17.6 Organizing Data for Analysis
18. Geospatial Analysis
18.1 Introduction
18.2 Geospatial Data Analysis
18.3 Integration and Modelling of Spatial Data
18.4 Geospatial Data Analysis Methods
18.5 Database Query
18.6 Geospatial Measurements
18.7 Overlay Operations
18.8 Network Analysis
18.9 Surface Analysis
18.10 Geostatistics
18.11 Geovisualization
19. Planning, Implementation, and Management of GIS
19.1 Introduction
19.2 Planning of Project
19.3 Implementation of Project
19.4 Management of Project
19.5 Keys for Successful GIS
19.6 Reasons for Unsuccessful GIS
20. Modern Trends of GIS
20.1 Introduction
20.2 Local to Global Concept in GIS
20.3 Increase in Dimensions in GIS
20.4 Linear to Non-linear Techniques in GIS
20.5 Development in Relation between Geometry and Algebra in GIS
20.6 Development of Common Techniques in GIS
20.7 Integration of GIS and Remote Sensing
20.8 Integration of GIS and Multimedia
20.9 3D GIS
20.9.1 Virtual Reality in GIS
20.10 Integration of 3D GIS and Web GIS
20.11 4D GIS and Real-time GIS
20.12 Mobile GIS
20.12.1 Mobile mapping
20.13 Collaborative GIS (CGIS)
21. Change Detection and Geosimulation
21.1 Visual change detection
21.2 Thresholding
21.3 Image difference
21.4 Image regression
21.5 Image ratioing
21.6 Vegetation index differencing
21.7 Principal component differencing
21.8 Multi-temporal image stock classification
21.9 Post classification comparison
21.10 Change vector analysis
21.12 Cellular automata simulation
21.13 Multi-agent simulation
21.14 ANN learning in simulation
Appendix A - Concept of Map, Coordinate System, and Projection
Appendix B - Concept on Mathematical TopicsNuméro de notice : 26518 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE/POSITIONNEMENT Nature : Manuel de cours DOI : sans Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97342 Réservation
Réserver ce documentExemplaires(1)
Code-barres Cote Support Localisation Section Disponibilité 26518-01 35.00 Livre Centre de documentation Télédétection Disponible Rendu basé image d'images historiques / Maria Scarlleth Gomes de Castro (2021)
Titre : Rendu basé image d'images historiques : Rapport de stage 3A Type de document : Mémoire Auteurs : Maria Scarlleth Gomes de Castro, Auteur ; Mathieu Brédif , Encadrant Editeur : Palaiseau : Ecole Polytechnique EP Année de publication : 2021 Projets : Alegoria / Gouet-Brunet, Valérie Importance : 34 p. Note générale : bibliographie
FICHIER VOLUMINEUX A DEMANDER AU CDOSLangues : Français (fre) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] appariement d'images
[Termes IGN] image ancienne
[Termes IGN] immersion
[Termes IGN] orthoimage
[Termes IGN] rendu (géovisualisation)
[Termes IGN] scène
[Termes IGN] texturageRésumé : (auteur) [introduction] Ce stage s’inscrit dans le cadre du projet ALEGORIA, qui vise à valoriser des fonds iconographiques du territoire français à des différents moments historiques, allant de l’entre deux-guerres à nos jours. Plus précisément, dans le cadre de ce stage, cette valorisation se fait par la visualisation de ces images historiques dans le contexte urbain 3D contemporain, de façon similaire à un street-view, dans le but de procurer une sensation d’immersion dans le passé. Pour atteindre cet objectif, la méthode scientifique utilisée est le rendu basé image, une technique d’informatique graphique qui consiste à utiliser des images réelles (photos) d’une scène particulière pour générer de nouvelles images de la même scène à partir de nouveaux points de vue, c’est-à-dire de positions à partir desquelles aucune photo réelle n’a été prise. Lorsqu’elle est associée à une connaissance partielle ou totale de la géométrie de la scène (maillages, nuages de points, cartes de profondeur), la technique du rendu basé image peut être utilisée pour déterminer l’apparence des objets présents, des bâtiments dans le cas d’une scène urbaine par exemple, par la projection de l’image sur la scène. [...] La technique de rendu basé image offre, en revanche, plusieurs avantages par rapport à cette méthode. Déjà, la correspondance entre un point géométrique et la coloration qui lui est attribuée par une image se fait très simplement, au moyen de la texturation projective, et peut être traitée individuellement pour chaque image et facilement modifiée par la suite selon nos besoins. De cette manière, nous pouvons manipuler au cas par cas les images que nous utilisons et la manière dont nous les utilisons, sans perdre leur individualité. [...] Dans ce rapport, nous commençons par présenter les méthodes les plus récentes dans le domaine du rendu basé image et leurs caractéristiques principales. Ensuite, nous décrivons les problèmes spécifiques qui ont été abordés au cours de ce stage. Nous présentons ensuite les méthodes scientifiques proposées, décrivons certains détails de mise en œuvre et discutons les résultats obtenus. Enfin, nous concluons en analysant les limites et les pistes possibles pour des futurs travaux. Note de contenu : 1 Introduction
2 État de l'art
3 Problématique
4 Approche proposée
5 Implémentation et résultats
6 ConclusionNuméro de notice : 17109 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Mémoire ingénieur Organisme de stage : LaSTIG (IGN) Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98506
Titre : Robust and fast global image orientation Type de document : Thèse/HDR Auteurs : Xin Wang, Auteur ; Christian Heipke, Directeur de thèse Editeur : Munich : Bayerische Akademie der Wissenschaften Année de publication : 2021 Collection : DGK - C, ISSN 0065-5325 num. 871 Importance : 141 p. Note générale : bibliographie
Diese Arbeit ist gleichzeitig veröffentlicht in: Wissenschaftliche Arbeiten der Fachrichtung Geodäsie und Geoinformatik der Leibniz Universität Hannover ISSN 0174-1454, Nr. 373, Hannover 2021Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Photogrammétrie numérique
[Termes IGN] appariement d'images
[Termes IGN] appariement dense
[Termes IGN] chaîne de traitement
[Termes IGN] estimation de pose
[Termes IGN] méthode robuste
[Termes IGN] orientation d'image
[Termes IGN] orientation relative
[Termes IGN] rotation
[Termes IGN] structure-from-motion
[Termes IGN] translation
[Termes IGN] valeur aberranteRésumé : (auteur) The estimation of image orientation (also called pose) has always played a crucial role in the field of photogrammetry since it is a fundamental prerequisite for the subsequent works of multi-view dense matching, generating DEM and DSM, etc. In the community of computer vision, the task is also well known as Structure-from-Motion (SfM), which reveals that image pose, while positions of object points are determined interdependently. Despite a lot of efforts over the last decades, it has recently gained the photogrammetrists’ interests again due to the fast-growing number of different resources of images. New challenges are posed for accurately and efficiently orienting various image datasets (e.g., unordered datasets with a large number of images, or images compromised of critical stereo pairs). In this thesis, the relevant ambition is to develop a new fast and robust method for the estimation of image orientation which is capable of coping with different types of datasets. To achieve this goal, the two most time-consuming steps of image orientation are in particular taken care of: (a) image matching and (b) the estimation process. To accelerate the image matching process, a new method employing a random k-d forest is proposed to quickly obtain pairs of overlapping images from an unordered image set. After that, image matching and the estimation of relative orientation parameters are performed only for pairs found to be very likely overlapping. On the other hand, to estimate the image poses in a time efficient manner, a global image orientation strategy is advocated. Its basic idea is to first simultaneously solve all available images’ poses, before a final bundle adjustment is carried out once for refinement. The conventional two-step global approach is pursued in this work, separating the determination of rotation matrices and translation parameters; the former is solved by an existing popular method of Chatterjee and Govindu [2013], and the latter are estimated globally using a newly developed method: translation estimation integrating both the relative translations and tie points. Tie points within triplets are adopted to firstly calculate global unified scale factors for each available pairwise relative translation. Then, analogous to rotation estimation, translations are determined by performing an averaging operation on the scaled relative translations. In order to improve the robustness of the solution, efforts in this thesis are also focused on coping with outliers in the relative orientations (ROs), which global image orientation approaches are particularly sensitive to. A general method based on triplet compatibility with respect to loop closure errors of relative rotations and translations is presented for detecting blunders in relative orientations. Although this procedure eliminated many gross errors in the input ROs, it typically cannot sort out blunders which are caused by repetitive structures and critical configurations, such as inappropriate baselines (very short baseline or baselines parallel to the viewing direction). Therefore, another new method is proposed to eliminate wrong ROs which have resulted from repetitive structures and very short baselines. Two corresponding criteria that indicate the quality of ROs are introduced. Repetitive structure is detected based on counts of conjugate points of the various image pairs, while very short baselines are found by inspecting the intersection angles of corresponding image rays. By analyzing these two criteria, incorrect ROs are detected and eliminated. As correct ROs of image pairs with a wider baseline nearly parallel to both viewing directions can be valuable, a method to identify and keep these ROs is also a part of this research. The validation and evaluation of the proposed method are thoroughly conducted on various benchmarks including ordered and unordered sets of images, images with repetitive structures and inappropriate baselines, etc. In particular, robustness is investigated by demonstrating the efficacy of the corresponding RO outlier detection methods. The performance and time efficiency of determining image orientation are evaluated and compared with several state-of-the-art global image orientation approaches. In summary, based on the experimental results, the developed methods demonstrateto be able to accomplish the image orientation taskfast and robustlyon different kinds of datasets. Numéro de notice : 17672 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Thèse étrangère Note de thèse : PhD dissertation : Fachrichtung Geodäsie und Geoinformatik : Hanovre : 2021 En ligne : https://dgk.badw.de/fileadmin/user_upload/Files/DGK/docs/c-871.pdf Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97997
Titre : Soil erosion : current challenges and future perspectives in a changing world Type de document : Monographie Auteurs : António Vieira, Éditeur scientifique Editeur : London [UK] : IntechOpen Année de publication : 2021 Importance : 152 p. ISBN/ISSN/EAN : 978-1-83962-300-4 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] acquisition d'images
[Termes IGN] Algérie
[Termes IGN] Bénin
[Termes IGN] changement d'occupation du sol
[Termes IGN] couvert végétal
[Termes IGN] érosion côtière
[Termes IGN] état du sol
[Termes IGN] Ethiopie
[Termes IGN] image captée par drone
[Termes IGN] image RVB
[Termes IGN] indice de végétation
[Termes IGN] Indonésie
[Termes IGN] modèle RUSLE
[Termes IGN] montagne
[Termes IGN] occupation du sol
[Termes IGN] orthoimage
[Termes IGN] photogrammétrie aérienne
[Termes IGN] Pix4D
[Termes IGN] protection des sols
[Termes IGN] risque naturel
[Termes IGN] Rwanda
[Termes IGN] système d'information géographiqueRésumé : (Editeur) Soil erosion is a major environmental issue with a worldwide impact and direct and indirect effects on soil productivity and consequently on human survival. Although a natural process, soil erosion has increased significantly due to human intervention, especially in the last centuries, through diverse activities such as intensive agriculture, overgrazing, urban sprawl, deforestation, and industrial and mining activities. Presently, soil erosion and degradation promoted by human action have reached extreme levels, necessitating urgent measures to promote soil conservation and rehabilitation. This book presents perspectives on soil erosion occurring in different parts of the world as well as some successful initiatives and strategies for soil conservation and rehabilitation. Note de contenu :
1. RGB Spectral Indices for the Analysis of Soil Protection by Vegetation Cover against Erosive Processes / Henry Antonio Pacheco Gil and Argenis de Jesús Montilla Pacheco
2. Spatial Estimation of Soil Erosion Risk Using RUSLE/GIS Techniques and Practices Conservation Suggested for Reducing Soil Erosion in Wadi Mina Catchment (Northwest, Algeria) / Ahmed Benchettouh, Sihem Jebari and Lakhdar Kouri
3. Remote Sensing and GIS-Based Soil Loss Estimation Using RUSLE in Bahir Dar Zuria District, Ethiopia / Nurhussen Ahmed Mohammed and Desale Kidane Asmamaw
4. Determination of the Most Priority Conservation Areas Based on Population Pressure and Erosion Hazard Levels in Lesti Sub-Watershed, Malang Regency, Indonesia / Andi Setyo Pambudi
5. The Impacts of Soil Degradation Effects on Phytodiversity and Vegetation Structure on Atacora Mountain Chain in Benin (West Africa) / Farris Okou, Achille Assogbadjo and Brice Augustin Sinsin
6. Erosion Control Success Stories and Challenges in the Context of Sustainable Landscape Management, Rwanda Experience / Jules Rutebuka
7. Biochar: A Sustainable Approach for Improving Soil Health and Environment / Shreya Das, Samanyita Mohanty, Gayatri Sahu, Mausami Rana and Kiran PilliNuméro de notice : 26759 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Recueil / ouvrage collectif DOI : 10.5772/intechopen.91595 Date de publication en ligne : 12/05/2021 En ligne : https://doi.org/10.5772/intechopen.91595 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=99775 Structure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring / Edoardo Grottoli in Remote sensing, vol 13 n° 1 (January-1 2021)
[article]
Titre : Structure-from-motion-derived digital surface models from historical aerial photographs: A new 3D application for coastal dune monitoring Type de document : Article/Communication Auteurs : Edoardo Grottoli, Auteur ; Mélanie Biausque, Auteur ; David Rogers, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 95 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] carte de profondeur
[Termes IGN] données lidar
[Termes IGN] dune
[Termes IGN] érosion côtière
[Termes IGN] filtrage de points
[Termes IGN] image captée par drone
[Termes IGN] image numérisée
[Termes IGN] modèle numérique de surface
[Termes IGN] reconstruction 3D
[Termes IGN] semis de points
[Termes IGN] structure-from-motion
[Termes IGN] surveillance du littoralRésumé : (auteur) Recent advances in structure-from-motion (SfM) techniques have proliferated the use of unmanned aerial vehicles (UAVs) in the monitoring of coastal landform changes, particularly when applied in the reconstruction of 3D surface models from historical aerial photographs. Here, we explore a number of depth map filtering and point cloud cleaning methods using the commercial software Agisoft Metashape Pro to determine the optimal methodology to build reliable digital surface models (DSMs). Twelve different aerial photography-derived DSMs are validated and compared against light detection and ranging (LiDAR)- and UAV-derived DSMs of a vegetated coastal dune system that has undergone several decades of coastline retreat. The different studied methods showed an average vertical error (root mean square error, RMSE) of approximately 1 m, with the best method resulting in an error value of 0.93 m. In our case, the best method resulted from the removal of confidence values in the range of 0–3 from the dense point cloud (DPC), with no filter applied to the depth maps. Differences among the methods examined were associated with the reconstruction of the dune slipface. The application of the modern SfM methodology to the analysis of historical aerial (vertical) photography is a novel (and reliable) new approach that can be used to better quantify coastal dune volume changes. DSMs derived from suitable historical aerial photographs, therefore, represent dependable sources of 3D data that can be used to better analyse long-term geomorphic changes in coastal dune areas that have undergone retreat. Numéro de notice : A2021-079 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/rs13010095 Date de publication en ligne : 30/12/2020 En ligne : https://doi.org/10.3390/rs13010095 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96821
in Remote sensing > vol 13 n° 1 (January-1 2021) . - n° 95[article]Suivi des vignes par télédétection de proximité : le deep learning au service de l’agriculture de précision / Sami Beniaouf (2021)PermalinkTen years of digital documentation of the archaeological site of the monastery of Saint Hilarion in Tell Umm el-Amr, Gaza strip / Emmanuel Alby (2021)PermalinkThe potential of LiDAR and UAV-photogrammetric data analysis to interpret archaeological sites: A case study of Chun Castle in South-West England / Israa Kadhim in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)PermalinkPermalinkVers un protocole de calibration de caméras statiques à l'aide d'un drone / Jean-François Villeforceix (2021)PermalinkAdjusting the regular network of squares resolution to the digital terrain model surface shape / Dariusz Gościewski in ISPRS International journal of geo-information, vol 9 n° 12 (December 2020)PermalinkApplication of various strategies and methodologies for landslide susceptibility maps on a basin scale: the case study of Val Tartano, Italy / Vasil Yordanov in Applied geomatics, vol 12 n° 4 (December 2020)PermalinkAutomatic building footprint extraction from UAV images using neural networks / Zoran Kokeza in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)PermalinkDu drone LiDAR à un nuage de points précis et exact : une chaîne de traitement LiDAR adaptée et quasi automatique / Maxime Lafleur in XYZ, n° 165 (décembre 2020)PermalinkForest cover mapping based on a combination of aerial images and Sentinel-2 satellite data compared to National Forest Inventory data / Selina Ganz in Forests, vol 11 n° 12 (December 2020)PermalinkMapping of land cover with open-source software and ultra-high-resolution imagery acquired with unmanned aerial vehicles / Ned Horning in Remote sensing in ecology and conservation, vol 6 n° 4 (December 2020)PermalinkPossibility to determine highly precise geoid for Egypt territory / Moamen Awad Habib Gad in Geodetski vestnik, vol 64 n° 4 (December 2020 - February 2021)PermalinkQuality assessment of photogrammetric methods - A workflow for reproducible UAS orthomosaics / Marvin Ludwig in Remote sensing, vol 12 n° 22 (December-1 2020)PermalinkRemote sensing in urban planning: Contributions towards ecologically sound policies? / Thilo Wellmann in Landscape and Urban Planning, vol 204 (December 2020)PermalinkStereophotogrammetry for 2-D building deformation monitoring using Kalman Filter / J.O. Odumosu in Reports on geodesy and geoinformatics, vol 110 n° 1 (December 2020)PermalinkTowards online UAS‐based photogrammetric measurements for 3D metrology inspection / Fabio Menna in Photogrammetric record, vol 35 n° 172 (December 2020)PermalinkBuilding change detection using a shape context similarity model for LiDAR data / Xuzhe Lyu in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkCadastral development in Norway: the need for improvement / Leiv Bjarte Mjøs in Survey review, vol 52 n° 375 (November 2020)PermalinkCombination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of West and Central Java provinces, Indonesia / Sanjiwana Arjasakusuma in ISPRS International journal of geo-information, vol 9 n° 11 (November 2020)PermalinkA deep learning framework for matching of SAR and optical imagery / Lloyd Haydn Hughes in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)Permalink