Remote Sensing Imagery.
Title:
Remote Sensing Imagery.
Author:
Tupin , Florence.
ISBN:
9781118898963
Personal Author:
Edition:
1st ed.
Physical Description:
1 online resource (367 pages)
Contents:
Cover -- Title Page -- Contents -- Preface -- PART 1. SYSTEMS, SENSORS AND ACQUISITIONS -- Chapter 1. Systems and Constraints -- 1.1. Satellite systems -- 1.2. Kepler's and Newton's laws -- 1.3. The quasi-circular orbits of remote sensing satellites -- 1.3.1. The orbit in the terrestrial referential: the recurrence cycle -- 1.3.2. The effects of the Earth's flattening: the precession of the orbits -- 1.3.3. Heliosynchronous orbits -- 1.3.4. Tracking the orbits -- 1.3.5. Usual orbits for remote sensing satellite -- 1.4. Image acquisition and sensors -- 1.4.1. Perspective ray in optical imagery for a vertical viewing -- 1.4.2. Perspective ray in radar imaging -- 1.4.3. Resolution and footprint -- 1.4.4. The swath in satellite imagery -- 1.4.5. Images and motion -- 1.5. Spectral resolution -- 1.5.1. Introduction -- 1.5.2. Technological constraints -- 1.5.3. Calibration and corrections -- 1.5.4. Image transmission -- Chapter 2. Image Geometry and Registration -- 2.1. The digital image and its sampling -- 2.1.1. Swath sampling -- 2.1.2. The pixels in optical imagery and in radar imagery -- 2.2. Sensor agility and incidence angle -- 2.2.1. Agility of optical sensors -- 2.2.2. Agility of radar sensors -- 2.2.3. The effects of the incidence variation on the ground cell size -- 2.2.4. The consequences of agility -- 2.3. Georeferencing of remote sensing images -- 2.3.1. From an image to an orthoimage -- 2.3.2. The metaparameters of VHR optical images -- 2.3.3. The levels of the images -- 2.3.4. SAR image specificities -- 2.4. Image registration -- 2.4.1. The need for image registration -- 2.4.2. Modeling the problem -- 2.5. Conclusion -- Chapter 3. The Physics of Optical Remote Sensing -- 3.1. Radiometry.
3.1.1. Radiant energy, spectral energy, spectral sensitivity and equivalent energy -- 3.1.2. The flux -- 3.1.3. The irradiance -- 3.1.4. The radiance -- 3.1.5. Temperature and emissivity -- 3.1.6. Reflectance and albedo -- 3.1.7. Example of the use of photometric quantities -- 3.2. Geometric etendue, sensitivity of an instrument -- 3.2.1. Axis sensor -- 3.2.2. Scanners -- 3.2.3. Pushbrooms -- 3.3. Atmospheric effects -- 3.3.1. Absorption -- 3.3.2. Scattering -- 3.3.3. Radiative transfer in the atmosphere -- 3.3.4. Magnitude orders of the atmospheric effects -- 3.4. Spectral properties of the surfaces -- 3.5. Directional properties of the surfaces -- 3.6. Practical aspects: products, atmospheric corrections, directional corrections -- 3.6.1. Absorption correction -- 3.6.2. Scattering correction -- 3.6.3. Examples of atmospheric correction results -- Chapter 4. The Physics of Radar Measurement -- 4.1. Propagation and polarization of electromagnetic waves -- 4.1.1. Propagation of electromagnetic waves -- 4.1.2. Polarization of the electromagnetic waves -- 4.1.3. Partially polarized waves -- 4.1.4. The group of Pauli matrices and the Stokes parameters -- 4.2. Radar signatures -- 4.2.1. RCS of a point target -- 4.2.2. Radar signature for extended targets - the backscatter coefficient σo -- 4.3. The basics of radar measurement physics: interaction between waves and natural surfaces -- 4.3.1. Introduction -- 4.3.2. Bare soil scattering -- 4.3.3. Sea surface scattering -- 4.3.4. Volume scattering -- 4.3.5. The penetration properties of electromagnetic waves -- 4.3.6. The effects of slope on radiometry -- 4.4. Calibration of radar images -- 4.5. Radar polarimetry -- 4.5.1. Introduction -- 4.5.2. Operating principles of the polarimetric radar -- PART 2. PHYSICS AND DATA PROCESSING.
Chapter 5. Image Processing Techniques for Remote Sensing -- 5.1. Introduction -- 5.2. Image statistics -- 5.2.1. Statistics of optical images -- 5.2.2. Radar data statistics -- 5.3. Preprocessing -- 5.3.1. Sampling and deconvolution -- 5.3.2. Denoising -- 5.4. Image segmentation -- 5.4.1. Panorama of segmentation methods -- 5.4.2. MDL methods -- 5.4.3. Watershed -- 5.4.4. Mean-shift -- 5.4.5. Edge detection -- 5.5. Information extraction -- 5.5.1. Point target detection in radar imagery -- 5.5.2. Interest point detection and descriptors -- 5.5.3. Network detection -- 5.5.4. Detection and recognition of extended objects -- 5.5.5. Spatial reasoning -- 5.6. Classification -- 5.6.1. Bayesian approaches and optimization -- 5.6.2. Support Vector Machines -- 5.6.3. Neural networks -- 5.7. Dimensionality reduction -- 5.7.1. Motivation -- 5.7.2. Principal component analysis -- 5.7.3. Other linear methods -- 5.7.4. Nonlinear methods -- 5.7.5. Component selection -- 5.8. Information fusion -- 5.8.1. Probabilistic fusion -- 5.8.2. Fuzzy fusion -- 5.8.3. Evidence theory -- 5.8.4. Possibilistic fusion -- 5.9. Conclusion -- Chapter 6. Passive Optical Data Processing -- 6.1. Introduction -- 6.2. Pansharpening -- 6.2.1. Spectral methods: projection-substitution -- 6.2.2. Space-scale methods: multiresolution pansharpening -- 6.3. Spectral indices and spatial indices -- 6.3.1. Vegetation indices -- 6.3.2. Water-related indices -- 6.3.3. Indices relative to cloud properties -- 6.3.4. Surface texture: occurrence and co-occurrence -- 6.3.5. Geometrical indices of surfaces: morphological indices in urban areas -- 6.4. Products issued from passive optical images -- 6.4.1. Classification -- 6.4.2. Subpixel mixture analysis -- 6.5. Conclusion -- Chapter 7. Models and Processing of Radar Signals.
7.1. Speckle and statistics of radar imagery -- 7.1.1. Physical origin -- 7.1.2. Statistics of fully developed speckle -- 7.1.3. Speckle noise in multi-look images -- 7.1.4. Estimating the number of looks in an image -- 7.2. Representation of polarimetric data -- 7.2.1. Canonical forms of the backscattering matrix -- 7.2.2. Taking depolarization mechanisms into account -- 7.2.3. Polarimetric analysis based on the coherence matrix -- 7.2.4. Synoptic representation of polarimetric information -- 7.3. InSAR interferometry and differential interferometry (D-InSAR) -- 7.3.1. Statistics of interferometric data -- 7.4. Processing of SAR data -- 7.5. Conclusion -- PART 3 APPLICATIONS: MEASURES, EXTRACTION, COMBINATION AND INFORMATION FUSION -- Chapter 8. Analysis of Multi-Temporal Series and Change Detection -- 8.1. Registration, calibration and change detection -- 8.2. Change detection based on two observations -- 8.2.1. Change measurements between homogeneous data -- 8.2.2. Change measurements between an image and a map -- 8.2.3. Change measurement between two classifications -- 8.2.4. Changes measurements between two heterogeneous images -- 8.3. Time series analysis -- 8.3.1. Temporal series with scalar data -- 8.3.2. Patterns in long time series with scalar or vectorial data -- 8.4. Conclusion -- Chapter 9. Elevation Measurements -- 9.1. Optic stereovision -- 9.1.1. The principle of stereoscopy -- 9.1.2. Epipolar geometry -- 9.1.3. Searching homologous points -- 9.1.4. Reconstruction of the digital terrain and elevation models -- 9.1.5. Multi-view stereoscopy -- 9.2. Radargrammetry -- 9.2.1. Geometric aspects -- 9.2.2. Correspondence -- 9.3. Interferometry -- 9.3.1. Geometric aspects -- 9.3.2. Topographic fringes -- 9.3.3. Orbital fringes -- 9.3.4. Interferogram processing -- 9.4. Radar tomography -- 9.5. Conclusion.
Chapter 10. Displacement Measurements -- 10.1. Introduction -- 10.2. Extraction of displacement information -- 10.2.1. Maximum of similarity -- 10.2.2. Differential interferometry -- 10.2.3. Corrections -- 10.3. Combination of displacement measurements -- 10.3.1. Analysis of time series -- 10.3.2. Reconstruction of 3D displacement field -- 10.4. Conclusion -- Chapter 11. Data Assimilation for the Monitoring of Continental Surfaces -- 11.1. Introduction to data assimilation in land surface models -- 11.2. Basic concepts in data assimilation -- 11.2.1. Elements of a data assimilation system -- 11.2.2. Notations and definitions -- 11.2.3. Data assimilation: an inverse problem -- 11.3. Different approaches -- 11.3.1. Brief history and classification -- 11.3.2. Sequential methods -- 11.3.3. Variational assimilation -- 11.3.4. Parameter identification -- 11.4. Assimilation into land surface models -- 11.4.1. Soil moisture -- 11.4.2. The surface temperature -- 11.4.3. The vegetation -- 11.5. Data assimilation - in practice -- 11.5.1. Overdetermined problem, underdetermined problem and ill-posed problem -- 11.5.2. The adjustment criterion -- 11.5.3. The analysis or control vector: observability and equifinality -- 11.5.4. Algorithmic parameters -- 11.6. Perspectives -- Bibliography -- List of Authors -- Index.
Abstract:
Dedicated to remote sensing images, from their acquisition to their use in various applications, this book covers the global lifecycle of images, including sensors and acquisition systems, applications such as movement monitoring or data assimilation, and image and data processing. It is organized in three main parts. The first part presents technological information about remote sensing (choice of satellite orbit and sensors) and elements of physics related to sensing (optics and microwave propagation). The second part presents image processing algorithms and their specificities for radar or optical, multi and hyper-spectral images. The final part is devoted to applications: change detection and analysis of time series, elevation measurement, displacement measurement and data assimilation. Offering a comprehensive survey of the domain of remote sensing imagery with a multi-disciplinary approach, this book is suitable for graduate students and engineers, with backgrounds either in computer science and applied math (signal and image processing) or geo-physics. About the Authors Florence Tupin is Professor at Telecom ParisTech, France. Her research interests include remote sensing imagery, image analysis and interpretation, three-dimensional reconstruction, and synthetic aperture radar, especially for urban remote sensing applications. Jordi Inglada works at the Centre National d'Études Spatiales (French Space Agency), Toulouse, France, in the field of remote sensing image processing at the CESBIO laboratory. He is in charge of the development of image processing algorithms for the operational exploitation of Earth observation images, mainly in the field of multi-temporal image analysis for land use and cover change. Jean-Marie Nicolas is Professor at Telecom ParisTech in the Signal and Imaging department. His research interests include the
modeling and processing of synthetic aperture radar images.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
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