Centre dedocumentation
scientifique

The polar epipolar rectification / François Darmon in IPOL Journal, Image Processing On Line, vol 11 (2021)  

Public [article]  inIPOL Journal, Image Processing On Line > vol 11 (2021) . - pp 56 - 75 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 homologues Résumé : (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 [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)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > Vol 181 (November 2021) . - pp 129 - 147 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-motion Résumé : (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 [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)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 87 n° 10 (October 2021) . - pp 735 - 746 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 spectrale Résumé : (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 [article]

Réservation

Réserver ce document

Exemplaires (1)

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)  

Public 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 Topographe Langues : 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 Conclusion Numéro 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

Remote sensing and GIS / Basudeb Bhatta (2021)

Public 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 areal Langues : 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 3D Index. 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 Topics Numéro 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 document

Exemplaires (1)

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)  

Permalink

A generic framework for improving the geopositioning accuracy of multi-source optical and SAR imagery / Niangang Jiao in ISPRS Journal of photogrammetry and remote sensing, vol 169 (November 2020)  

Permalink

Ambiguous use of geographical information systems for the rectification of large-scale geometric maps / Anders Wästfelt in Cartographic journal (the), Vol 57 n° 3 (August 2020)  

Permalink

Pedestrian network generation based on crowdsourced tracking data / Xue Yang in International journal of geographical information science IJGIS, vol 34 n° 5 (May 2020)  

Permalink

Some thoughts on measuring earthquake deformation using optical imagery / Min Huang in IEEE Transactions on geoscience and remote sensing, vol 58 n° 2 (February 2020)  

Permalink

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

Permalink

Le plug-in ACYOTB : l'orthorectification open source de précision / Valerio Baiocchi in Géomatique expert, n° 132-133 (janvier - septembre 2020)

Permalink

Geometric accuracy improvement of WorldView‐2 imagery using freely available DEM data / Mateo Gašparović in Photogrammetric record, vol 34 n° 167 (September 2019)  

Permalink

Télédétection radar : de l'image d'intensité initiale au choix du mode de calibration des coefficients de diffusion / Jean-Paul Rudant in Revue Française de Photogrammétrie et de Télédétection, n° 219-220 (juin - octobre 2019)  

Permalink

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)  

Permalink