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Activity recognition in residential spaces with Internet of things devices and thermal imaging / Kshirasagar Naik in Sensors, vol 21 n° 3 (February 2021)  

Public [article]  inSensors > vol 21 n° 3 (February 2021) . - n° 988 Titre : Activity recognition in residential spaces with Internet of things devices and thermal imaging Type de document : Article/Communication Auteurs : Kshirasagar Naik, Auteur ; Tejas Pandit, Auteur ; Nitin Naik, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 988 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] compréhension de l'image [Termes IGN] contrôle par télédétection [Termes IGN] détection d'événement [Termes IGN] espace intérieur [Termes IGN] image RVB [Termes IGN] image thermique [Termes IGN] intelligence artificielle [Termes IGN] internet des objets [Termes IGN] itération [Termes IGN] modèle stéréoscopique [Termes IGN] objet mobile [Termes IGN] reconnaissance automatique [Termes IGN] reconnaissance d'objets [Termes IGN] scène 3D Résumé : (auteur) In this paper, we design algorithms for indoor activity recognition and 3D thermal model generation using thermal images, RGB images, captured from external sensors, and the internet of things setup. Indoor activity recognition deals with two sub-problems: Human activity and household activity recognition. Household activity recognition includes the recognition of electrical appliances and their heat radiation with the help of thermal images. A FLIR ONE PRO camera is used to capture RGB-thermal image pairs for a scene. Duration and pattern of activities are also determined using an iterative algorithm, to explore kitchen safety situations. For more accurate monitoring of hazardous events such as stove gas leakage, a 3D reconstruction approach is proposed to determine the temperature of all points in the 3D space of a scene. The 3D thermal model is obtained using the stereo RGB and thermal images for a particular scene. Accurate results are observed for activity detection, and a significant improvement in the temperature estimation is recorded in the 3D thermal model compared to the 2D thermal image. Results from this research can find applications in home automation, heat automation in smart homes, and energy management in residential spaces. Numéro de notice : A2021-159 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/s21030988 date de publication en ligne : 02/02/2021 En ligne : https://doi.org/10.3390/s21030988 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97075 [article]

Holographic SAR tomography 3-D reconstruction based on iterative adaptive approach and generalized likelihood ratio test / Dong Feng in IEEE Transactions on geoscience and remote sensing, vol 59 n° 1 (January 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 1 (January 2021) . - pp 305 - 315 Titre : Holographic SAR tomography 3-D reconstruction based on iterative adaptive approach and generalized likelihood ratio test Type de document : Article/Communication Auteurs : Dong Feng, Auteur ; Daoxiang An, Auteur ; Leping Chen, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 305 - 315 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] angle azimutal [Termes IGN] holographie [Termes IGN] image radar moirée [Termes IGN] itération [Termes IGN] ligne de visée [Termes IGN] reconstruction 3D [Termes IGN] scène 3D [Termes IGN] tomographie radar Résumé : (auteur) Holographic synthetic aperture radar (HoloSAR) tomography is an attractive imaging mode that can retrieve the 3-D scattering information of the observed scene over 360° azimuth angle variation. To improve the resolution and reduce the sidelobes in elevation, the HoloSAR imaging mode requires many passes in elevation, thus decreasing its feasibility. In this article, an imaging method based on iterative adaptive approach (IAA) and generalized likelihood ratio test (GLRT) is proposed for the HoloSAR with limited elevation passes to achieve super-resolution reconstruction in elevation. For the elevation reconstruction in each range-azimuth cell, the proposed method first adopts the nonparametric IAA to retrieve the elevation profile with improved resolution and suppressed sidelobes. Then, to obtain sparse elevation estimates, the GLRT is used as a model order selection tool to automatically recognize the most likely number of scatterers and obtain the reflectivities of the detected scatterers inside one range-azimuth cell. The proposed method is a super-resolving method. It does not require averaging in range and azimuth, thus it can maintain the range-azimuth resolution. In addition, the proposed method is a user parameter-free method, so it does not need the fine-tuning of any hyperparameters. The super-resolution power and the estimation accuracy of the proposed method are evaluated using the simulated data, and the validity and feasibility of the proposed method are verified by the HoloSAR real data processing results. Numéro de notice : A2021-034 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.2994201 date de publication en ligne : 22/05/2020 En ligne : https://doi.org/10.1109/TGRS.2020.2994201 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96736 [article]

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

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Visualization of 3D property data and assessment of the impact of rendering attributes / Stefan Seipel in Journal of Geovisualization and Spatial Analysis, vol 4 n° 2 (December 2020)  

Public [article]  inJournal of Geovisualization and Spatial Analysis > vol 4 n° 2 (December 2020) . - n° 23 Titre : Visualization of 3D property data and assessment of the impact of rendering attributes Type de document : Article/Communication Auteurs : Stefan Seipel, Auteur ; Martin Andrée, Auteur ; Karolina Larsson, Auteur ; et al., Auteur Année de publication : 2020 Article en page(s) : n° 23 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] attribut non spatial [Termes IGN] cadastre 3D [Termes IGN] cadastre étranger [Termes IGN] classification barycentrique [Termes IGN] couleur (rédaction cartographique) [Termes IGN] mesure de similitude [Termes IGN] propriété foncière [Termes IGN] rédaction cartographique [Termes IGN] rendu (géovisualisation) [Termes IGN] saillance [Termes IGN] scène 3D [Termes IGN] Stockholm (Suède) [Termes IGN] visualisation cartographique [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Visualizations of 3D cadastral information incorporating both intrinsically spatial and non-spatial information are examined here. The design of a visualization prototype is linked to real-case 3D property information. In an interview with domain experts, the functional and visual features of the prototype are assessed. The choice of rendering attributes was identified as an important aspect for further analysis. A computational approach to systematic assessment of the consequences of different graphical design choices is proposed. This approach incorporates a colour similarity metric, visual saliency maps, and k-nearest-neighbour (kNN) classification to estimate risks of confusing or overlooking relevant elements in a visualization. The results indicate that transparency is not an independent visual variable, as it affects the apparent colour of 3D objects and makes them inherently more difficult to distinguish. Transparency also influences visual saliency of objects in a scene. The proposed analytic approach was useful for visualization design and revealed that the conscious use of graphical attributes, like combinations of colour, transparency, and line styles, can improve saliency of objects in a 3D scene. Numéro de notice : A2020-796 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1007/s41651-020-00063-6 date de publication en ligne : 26/10/2020 En ligne : https://doi.org/10.1007/s41651-020-00063-6 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96612 [article]

Geometric distortion of historical images for 3D visualization / Evelyn Paiz-Reyes in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, V-2 (August 2020)  

Public [article]  inISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > V-2 (August 2020) . - pp 649 - 655 Titre : Geometric distortion of historical images for 3D visualization Type de document : Article/Communication Auteurs : Evelyn Paiz-Reyes , Auteur ; Mathieu Brédif , Auteur ; Sidonie Christophe , Auteur Année de publication : 2020 Projets : Alegoria / Gouet-Brunet, Valérie Conférence : ISPRS 2020, Commission 2, virtual Congress, Imaging today foreseeing tomorrow 31/08/2020 02/09/2020 Nice (en ligne) France Annals Commission 2 Article en page(s) : pp 649 - 655 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Termes IGN] base de données historiques [Termes IGN] distorsion d'image [Termes IGN] image numérisée [Termes IGN] modèle de déformation des images [Termes IGN] rendu (géovisualisation) [Termes IGN] scène 3D [Termes IGN] visualisation 3D [Vedettes matières IGN] Géovisualisation Résumé : (auteur) Archivists, historians and national mapping agencies, among others, are archiving large datasets of historical photographs. Nevertheless, the capturing devices used to acquire these images possessed a diversity of effects that influenced the quality of the final resulting picture, e.g. geometric distortion, chromatic aberration, depth of field variation, etc. This paper examines singularly the topic of geometric distortion for a co-visualization of historical photos within a 3D model of the photographed scene. A distortion function of an image is ordinarily estimated only on the image domain by adjusting its parameters to observations of point correspondences. This mathematical function may exhibit overfits, oscillations or may not be well defined outside of this domain. The contribution of this work is the description of a distortion model defined on the whole undistorted image plane. We extrapolate the distortion estimated only on the image domain and then transfer this distortion information to the view of the 3D scene. This enables to look at the scene through an estimated camera and zoom out to see the context around the original photograph with a well-defined and behaved distortion. These findings may be a significant addition to the overall purpose of creating innovative ways to examine and visualize old photographs. Numéro de notice : A2020-468 Affiliation des auteurs : UGE-LaSTIG (2020- ) Autre URL associée : vers HAL Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.5194/isprs-annals-V-2-2020-649-2020 date de publication en ligne : 03/08/2020 En ligne : https://doi.org/10.5194/isprs-annals-V-2-2020-649-2020 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95537 [article]

Creation of inspirational Web Apps that demonstrate the functionalities offered by the ArcGIS API for JavaScript / Arthur Genet (2020) 

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Mise à jour de carte 3D par mosaïquage de maillages [diaporama] / Bruno Vallet (2020)  

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A building label placement method for 3D visualizations based on candidate label evaluation and selection / Jiangfeng She in International journal of geographical information science IJGIS, vol 33 n° 10 (October 2019)  

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Preparing the holoLens for user studies : an augmented reality interface for the spatial adjustment of holographic objects in 3D indoor environments / Julian Keil in KN, Journal of Cartography and Geographic Information, vol 69 n° 3 (September 2019)  

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Complete 3D scene parsing from an RGBD image / Chuhang Zou in International journal of computer vision, vol 127 n° 2 (February 2019)  

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3D radiative transfer modeling over complex vegetation canopies and forest reconstruction from LIDAR measurements / Jianbo Qi (2019)  

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Détection et localisation d'objets 3D par apprentissage profond en topologie capteur / Pierre Biasutti (2019)    

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Simultaneous characterization of objects temperature and radiative properties through multispectral infrared thermography / Thibaud Toullier (2019)  

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The design and testing of 3DmoveR: an experimental tool for usability studies of interactive 3D maps / Lukas Herman in Cartographic perspectives, n° 90 ([01/10/2018])  

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Fusion of images and point clouds for the semantic segmentation of large-scale 3D scenes based on deep learning / Rui Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 143 (September 2018)  

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