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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
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Code-barres Cote Support Localisation Section Disponibilité 26518-01 35.00 Livre Centre de documentation Télédétection Disponible Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México / Nelly L. Ramirez Serrato in Geocarto international, vol 35 n° 15 ([01/11/2020])
[article]
Titre : Topographic connection method for automated mapping of landslide inventories, study case: semi urban sub-basin from Monterrey, Northeast of México Type de document : Article/Communication Auteurs : Nelly L. Ramirez Serrato, Auteur ; Fabiola D. Yepez-Rincon, Auteur ; Adrian L. Ferrino Fierro, Auteur Année de publication : 2020 Article en page(s) : pp 1706 - 1721 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] analyse diachronique
[Termes IGN] cartographie des risques
[Termes IGN] chaîne de traitement
[Termes IGN] détection de contours
[Termes IGN] données lidar
[Termes IGN] données localisées 3D
[Termes IGN] effondrement de terrain
[Termes IGN] image satellite
[Termes IGN] inventaire
[Termes IGN] Mexique
[Termes IGN] modèle numérique de surface
[Termes IGN] semis de points
[Termes IGN] visualisation 3DRésumé : (auteur) By nature, slopes are conformed by forces that are in constant balance. Altering this natural balance causes the sliding of soil towards lower zones. Landslides are a constant danger that compromises the general welfare of society. Landslides mapping is especially important for urban areas or development plans. The innovative aspect of this study is the creation of the Topographic Connection Method (TPCM) to automatically map landslides using two types of landslides 1) falls and 2) flows. TPCM cartography results were compared to a previously proven method (Contour Connection Method), as well as to the manual inventory method. Each method was run four times to locate changes through time by using satellite imagery, digital elevations models and 3D relief visualizations with data covering a period from 2012 to 2017. Results showed both falls and flows with all three methods and demonstrated that TPCM can improve mapping accuracy by up to 14%. Numéro de notice : A2020-659 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1080/10106049.2019.1581269 Date de publication en ligne : 01/04/2019 En ligne : https://doi.org/10.1080/10106049.2019.1581269 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96132
in Geocarto international > vol 35 n° 15 [01/11/2020] . - pp 1706 - 1721[article]CityJSON in QGIS: Development of an open‐source plugin / Stelios Vitalis in Transactions in GIS, Vol 24 n° 5 (October 2020)
[article]
Titre : CityJSON in QGIS: Development of an open‐source plugin Type de document : Article/Communication Auteurs : Stelios Vitalis, Auteur ; Ken Arroyo Ohori, Auteur ; Jantien E. Stoter, Auteur Année de publication : 2020 Article en page(s) : pp 1147-1164 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Systèmes d'information géographique
[Termes IGN] CityGML
[Termes IGN] édition en libre accès
[Termes IGN] format JSON
[Termes IGN] implémentation (informatique)
[Termes IGN] modèle 3D de l'espace urbain
[Termes IGN] module d'extension
[Termes IGN] QGIS
[Termes IGN] visualisation 3DRésumé : (Auteur) When QGIS 3.0 was released in 2018, it added support for 3D visualisation. At the same time, CityJSON has been developing as an easy‐to‐use JavaScript Object Notation (JSON) encoding for 3D city models using the CityGML 2.0 data model. Together, this opened the possibility to support semantic 3D city models in the popular open‐source GIS software for the first time. In order to add support for 3D city models in QGIS, we have developed a plugin that enables CityJSON datasets to be loaded. The plugin parses a CityJSON file and analyses its tree structure to identify all city objects. Then, the geometry and attributes of every city object are transformed into QGIS features and divided into layers according to user preferences. CityJSON parsing was proven to be straightforward and consistent when tested against several open datasets. One of the biggest challenges we faced, though, was mapping CityJSON’s hierarchical data structure to the relational model of QGIS. We undertook this issue by providing various methods on how geometries from the model are loaded as QGIS features. We intend to use the plugin for educational purposes in our university and we believe it can be proven a worthy tool for researchers and practitioners. Numéro de notice : A2020-498 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/tgis.12657 Date de publication en ligne : 24/06/2020 En ligne : https://doi.org/10.1111/tgis.12657 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96198
in Transactions in GIS > Vol 24 n° 5 (October 2020) . - pp 1147-1164[article]3D reconstruction of internal wood decay using photogrammetry and sonic tomography / Junjie Zhang in Photogrammetric record, vol 35 n° 171 (September 2020)
[article]
Titre : 3D reconstruction of internal wood decay using photogrammetry and sonic tomography Type de document : Article/Communication Auteurs : Junjie Zhang, Auteur ; Kourosh Khoshelham, Auteur Année de publication : 2020 Article en page(s) : pp 357-374 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques
[Termes IGN] dépérissement
[Termes IGN] hauteur des arbres
[Termes IGN] interpolation spatiale
[Termes IGN] onde acoustique
[Termes IGN] qualité du bois
[Termes IGN] reconstruction 3D
[Termes IGN] temps de vol
[Termes IGN] tomographie
[Termes IGN] tronc
[Termes IGN] visualisation 3DRésumé : (Auteur) Knowledge of deteriorations within tree trunks is critical for arborists to conduct individual tree health assessments. Sonic tree tomography, a non‐destructive technique using sound waves, has been widely used to estimate the size and shape of internal decay based on sound wave velocity variations. However, it has commonly been applied to 2D horizontal or vertical cross sections and its accuracy is questionable due to the poor approximation of the shape of the cross section. This paper proposes an integration of close‐range photogrammetry and sonic tomography to enable accurate reconstruction of the exterior and interior of the tree trunk in 3D. The internal wood quality is represented by the spatially interpolated sound wave velocities, using the time of flight of the sound waves and the coordinates of the acoustic sensors obtained from the photogrammetric model. Experimental results show that the proposed approach provides a realistic 3D visualisation of the size, shape and location of the internal deteriorations. Numéro de notice : A2020-436 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1111/phor.12328 Date de publication en ligne : 06/08/2020 En ligne : https://doi.org/10.1111/phor.12328 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=95842
in Photogrammetric record > vol 35 n° 171 (September 2020) . - pp 357-374[article]Geometric distortion of historical images for 3D visualization / Evelyn Paiz-Reyes in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol V-2-2020 (August 2020)
[article]
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] GéovisualisationRésumé : (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
in ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences > vol V-2-2020 (August 2020) . - pp 649 - 655[article]Designing multi-scale maps: lessons learned from existing practices / Marion Dumont in International journal of cartography, Vol 6 n° 1 (March 2020)PermalinkSpatio-temporal mobility and Twitter: 3D visualisation of mobility flows / Joaquín Osorio Arjona in Journal of maps, vol 16 n° 1 ([02/01/2020])PermalinkApplication of machine learning techniques for evidential 3D perception, in the context of autonomous driving / Edouard Capellier (2020)PermalinkPermalinkPermalinkPermalinkPermalinkNumérisation, restitution et visualisation en 3D de sites patrimoniaux / Jonathan Chemla in XYZ, n° 161 (décembre 2019)PermalinkLes nouveautés de QGis 3.10 / Anonyme in Géomatique expert, n° 130-131 (octobre - décembre 2019)PermalinkReprésentation des éléments juridiques dans une maquette BIM / Bamba Ngom in Géomatique expert, n° 128 (juin - juillet 2019)PermalinkAn artificial bee colony-based algorithm to automatically create colour schemes for geovisualizations / Mingguang Wu in Cartographic journal (the), Vol 56 n° 2 (May 2019)PermalinkiTowns, le nouveau moteur de visualisation 3D de données géospatiales du Géoportail / Mirela Konini in Responsabilité et environnement, n° 94 (Avril 2019)PermalinkOrléans monte sa maquette virtuelle / Anonyme in Géomatique expert, n° 127 (avril - mai 2019)PermalinkUtilizing a discrete global grid system for handling point clouds with varying locations, times, and levels of detail / Neeraj Sirdeshmukh in Cartographica, vol 54 n° 1 (Spring 2019)PermalinkPermalinkPermalink3D WebGIS : from visualization to analysis. An efficient browser-based 3D line-of-sight analysis / Michael Auer in ISPRS International journal of geo-information, vol 7 n° 7 (July 2018)PermalinkLocal curvature entropy-based 3D terrain representation using a comprehensive Quadtree / Giyu Chen in ISPRS Journal of photogrammetry and remote sensing, vol 139 (May 2018)Permalink3D visualization of trees based on a sphere-board model / Jiangfeng She in ISPRS International journal of geo-information, vol 7 n° 2 (February 2018)PermalinkPermalink