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The polar epipolar rectification / François Darmon in IPOL Journal, Image Processing On Line, vol 11 (2021)
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Titre : The polar epipolar rectification Type de document : Article/Communication Auteurs : François Darmon, Auteur ; Pascal Monasse, Auteur Année de publication : 2021 Article en page(s) : pp 56 - 75 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] couple stéréoscopique
[Termes IGN] disparité
[Termes IGN] géométrie épipolaire
[Termes IGN] orthorectification
[Termes IGN] points homologuesRésumé : (auteur) Epipolar rectification of a stereo pair is the process of resampling a pair of stereo images so that the apparent motion of corresponding points is horizontal. This is an important preliminary step in depth estimation, substituting depth by disparity estimation. Most methods rely on a perspective transform of both images, which has the advantage to simulate a different attitude of the pinhole cameras. A limitation is that when an epipole is inside the image domain, it has to be sent to infinity by the perspective transform, producing a strong distortion. On the contrary, relying on a polar transform centered at the epipole provides a method applicable universally to a pair of pinhole camera views. We present in detail the algorithm, filling in the information important for its implementation and missing in published articles. Numéro de notice : A2021-782 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.5201/ipol.2021.328 Date de publication en ligne : 02/03/2021 En ligne : https://doi.org/10.5201/ipol.2021.328 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98937
in IPOL Journal, Image Processing On Line > vol 11 (2021) . - pp 56 - 75[article]Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features / Bai Zhu in ISPRS Journal of photogrammetry and remote sensing, Vol 181 (November 2021)
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Titre : Robust registration of aerial images and LiDAR data using spatial constraints and Gabor structural features Type de document : Article/Communication Auteurs : Bai Zhu, Auteur ; Yuanxin Ye, Auteur ; Liang Zhou, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 129 - 147 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie
[Termes IGN] algorithme ICP
[Termes IGN] correction géométrique
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] élément d'orientation externe
[Termes IGN] enregistrement de données
[Termes IGN] filtre de Gabor
[Termes IGN] image aérienne
[Termes IGN] recalage d'image
[Termes IGN] semis de points
[Termes IGN] SIFT (algorithme)
[Termes IGN] structure-from-motionRésumé : (auteur) Co-registration of aerial imagery and Light Detection and Ranging (LiDAR) data is quite challenging because the different imaging mechanisms produce significant geometric and radiometric distortions between the two multimodal data sources. To address this problem, we propose a robust and effective coarse-to-fine registration method that is conducted in two stages utilizing spatial constraints and Gabor structural features. In the first stage, the LiDAR point cloud data is transformed into an intensity map that is used as the reference image. Then, coarse registration is completed by designing a partition-based Features from Accelerated Segment Test (FAST) operator to extract the uniformly distributed interest points in the aerial images and thereafter performing a local geometric correction based on the collinearity equations using the exterior orientation parameters (EoPs). The coarse registration aims to provide a reliable spatial geometry relationship for the subsequent fine registration and is designed to eliminate rotation and scale changes, as well as making only a few translation differences exist between the images. In the second stage, a novel feature descriptor called multi-Scale and multi-Directional Features of odd Gabor (SDFG) is first built to capture the multi-scale and multi-directional structural properties of the images. Then, the three-dimensional (3D) phase correlation (PC) of the SDFG descriptor is established to detect the control points (CPs) between the aerial and LiDAR intensity image in the frequency domain, where the image matching is accelerated by the 3D Fast Fourier Transform (FFT) technique. Finally, the obtained CPs not only are employed to refine the EoPs, but also are used to achieve the fine registration of the aerial images and LiDAR data. We conduct experiments to verify the robustness of the proposed registration method using three sets of aerial images and LiDAR data with different scene coverage. Experimental results show that the proposed method is robust to geometric distortions and radiometric changes. Moreover, it achieves the registration accuracy of less than 2 pixels for all cases, which outperforms the current four state-of-the-art methods, demonstrating its superior registration performance. Numéro de notice : A2021-773 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2021.09.010 Date de publication en ligne : 21/09/2021 En ligne : https://doi.org/10.1016/j.isprsjprs.2021.09.010 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98830
in ISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 129 - 147[article]Spectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method / Saket Gowravaram in Photogrammetric Engineering & Remote Sensing, PERS, vol 87 n° 10 (October 2021)
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Titre : Spectral reflectance estimation of UAS multispectral imagery using satellite cross-calibration method Type de document : Article/Communication Auteurs : Saket Gowravaram, Auteur ; Haiyang Chao, Auteur ; Andrew Molthan, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 735 - 746 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique
[Termes IGN] aéronef
[Termes IGN] distribution du coefficient de réflexion bidirectionnelle BRDF
[Termes IGN] étalonnage croisé
[Termes IGN] forêt
[Termes IGN] image captée par drone
[Termes IGN] image Landsat-8
[Termes IGN] image multibande
[Termes IGN] image proche infrarouge
[Termes IGN] Kansas (Etats-Unis ; état)
[Termes IGN] orthoimage
[Termes IGN] orthorectification
[Termes IGN] prairie
[Termes IGN] rayonnement proche infrarouge
[Termes IGN] réflectance spectraleRésumé : (Auteur) This paper introduces a satellite-based cross-calibration (SCC) method for spectral reflectance estimation of unmanned aircraft system (UAS) multispectral imagery. The SCC method provides a low-cost and feasible solution to convert high-resolution UAS images in digital numbers (DN) to reflectance when satellite data is available. The proposed method is evaluated using a multispectral data set, including orthorectified KHawk UAS DN imagery and Landsat 8 Operational Land Imager Level-2 surface reflectance (SR) data over a forest/grassland area. The estimated UAS reflectance images are compared with the National Ecological Observatory Network's imaging spectrometer (NIS) SR data for validation. The UAS reflectance showed high similarities with the NIS data for the near-infrared and red bands with Pearson's r values being 97 and 95.74, and root-mean-square errors being 0.0239 and 0.0096 over a 32-subplot hayfield. Numéro de notice : A2021-676 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.20-00091R2 En ligne : https://doi.org/10.14358/PERS.20-00091R2 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=98863
in Photogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 735 - 746[article]Réservation
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Code-barres Cote Support Localisation Section Disponibilité 105-2021101 SL Revue Centre de documentation Revues en salle Disponible Apport des méthodes : imagerie drone, LiDAR et imagerie hyperspectrale pour l’étude du littoral vendéen / Mathis Baudis (2021)
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Titre : Apport des méthodes : imagerie drone, LiDAR et imagerie hyperspectrale pour l’étude du littoral vendéen Type de document : Mémoire Auteurs : Mathis Baudis, Auteur Editeur : Le Mans : Ecole Supérieure des Géomètres et Topographes ESGT Année de publication : 2021 Importance : 58 p. Format : 21 x 30 cm Note générale : bibliographie
Mémoire présenté en vue d'obtenir le diplôme d'ingénieur ESGT, spécialité Géomètre et TopographeLangues : Français (fre) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] image captée par drone
[Termes IGN] image hyperspectrale
[Termes IGN] littoral atlantique (France)
[Termes IGN] modèle numérique de terrain
[Termes IGN] orthophotoplan numérique
[Termes IGN] orthorectification
[Termes IGN] semis de points
[Termes IGN] trait de côte
[Termes IGN] Vendée (85)Index. décimale : ESGT Mémoires d'ingénieurs de l'ESGT Résumé : (auteur) L’érosion des falaises soulève de plus en plus de problématiques. Il existe de nombreuses études qualitatives sur ce sujet. Ici, l’objectif est de faire une étude quantitative sur le littoral vendéen. Nous allons étudier l’évolution du trait de côte, un épisode érosif fort : la chute d’une arche et l’apport de l’orthorectification d’images hyperspectrales. L’objectif est de coupler les acquisitions issues de drone, de LiDAR terrestre et de caméra hyperspectrale dans le but d’étudier le littoral vendéen. Note de contenu : Introduction
1- Etat des connaissances sur le littoral vendéen
2- Outils et méthodes
3- Présentation des résultats des différents traitements
4- Discussion sur les résultats
ConclusionNuméro de notice : 28695 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Mémoire ingénieur ESGT En ligne : https://dumas.ccsd.cnrs.fr/MEMOIRES-CNAM/dumas-03533799v1 Format de la ressource électronique : URL Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100466
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
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Code-barres Cote Support Localisation Section Disponibilité 26518-01 35.00 Livre Centre de documentation Télédétection Disponible Télédétection synchronisée hyperspectrale et LiDAR à retour d’onde complet : application au suivi des littoraux sableux de la région Pays-de-la-Loire / Giovanni Frati (2021)
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