The Experts below are selected from a list of 1128885 Experts worldwide ranked by ideXlab platform
Jon Atli Benediktsson - One of the best experts on this subject based on the ideXlab platform.
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Mathematical Models and Methods for Remote Sensing Image Analysis: An Introduction
Mathematical Models for Remote Sensing Image Processing, 2018Co-Authors: Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia, Jon Atli BenediktssonAbstract:The current progress of remote sensing systems, based on airborne and spaceborne platforms and involving active and passive sensors, provides an unprecedented wealth of information about the Earth surface for environmental monitoring, sustainable resource management, disaster prevention, emergency response, and defense. In this framework, Mathematical Models for image processing and analysis play fundamental roles. Effectively exploiting the potential conveyed by the availability of remote sensing data requires automatic or semi-automatic techniques capable of suitably characterizing and extracting thematic information of interest while minimizing the need for user intervention. The current development of Mathematical Models and methods for image processing and computer vision allows multiple remote sensing information extraction problems to be addressed successfully, accurately, and efficiently. In this introductory chapter, first, general characteristics of sensors and systems for Earth observation are summarized to define the basic terminology that will be used consistently throughout the book. Remote sensing image acquisition through passive and active sensors on-board spaceborne and airborne platforms is recalled together with the basic concepts of spatial, spectral, temporal, and radiometric resolution. Then, an overview of the main families of Mathematical Models and methods within the scientific field of two-dimensional remote sensing image processing is presented. The overall structure and organization of the book are also described.
Gabriele Moser - One of the best experts on this subject based on the ideXlab platform.
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Mathematical Models and Methods for Remote Sensing Image Analysis: An Introduction
Mathematical Models for Remote Sensing Image Processing, 2018Co-Authors: Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia, Jon Atli BenediktssonAbstract:The current progress of remote sensing systems, based on airborne and spaceborne platforms and involving active and passive sensors, provides an unprecedented wealth of information about the Earth surface for environmental monitoring, sustainable resource management, disaster prevention, emergency response, and defense. In this framework, Mathematical Models for image processing and analysis play fundamental roles. Effectively exploiting the potential conveyed by the availability of remote sensing data requires automatic or semi-automatic techniques capable of suitably characterizing and extracting thematic information of interest while minimizing the need for user intervention. The current development of Mathematical Models and methods for image processing and computer vision allows multiple remote sensing information extraction problems to be addressed successfully, accurately, and efficiently. In this introductory chapter, first, general characteristics of sensors and systems for Earth observation are summarized to define the basic terminology that will be used consistently throughout the book. Remote sensing image acquisition through passive and active sensors on-board spaceborne and airborne platforms is recalled together with the basic concepts of spatial, spectral, temporal, and radiometric resolution. Then, an overview of the main families of Mathematical Models and methods within the scientific field of two-dimensional remote sensing image processing is presented. The overall structure and organization of the book are also described.
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Mathematical Models for Remote Sensing Image Processing
2018Co-Authors: Gabriele Moser, Josiane ZerubiaAbstract:This book maximizes reader insights into the field of Mathematical Models and methods for the processing of two-dimensional remote sensing images. It presents a broad analysis of the field, encompassing passive and active sensors, hyperspectral images, synthetic aperture radar (SAR), interferometric SAR, and polarimetric SAR data. At the same time, it addresses highly topical subjects involving remote sensing data types (e.g., very high-resolution images, multiangular or multiresolution data, and satellite image time series) and analysis methodologies (e.g., probabilistic graphical Models, hierarchical image representations, kernel machines, data fusion, and compressive sensing) that currently have primary importance in the field of Mathematical modelling for remote sensing and image processing. Each chapter focuses on a particular type of remote sensing data and/or on a specific methodological area, presenting both a thorough analysis of the previous literature and a methodological and experimental discussion of at least two advanced Mathematical methods for information extraction from remote sensing data. This organization ensures that both tutorial information and advanced subjects are covered. With each chapter being written by research scientists from (at least) two different institutions, it offers multiple professional experiences and perspectives on each subject. The book also provides expert analysis and commentary from leading remote sensing and image processing researchers, many of whom serve on the editorial boards of prestigious international journals in these fields, and are actively involved in international scientific societies. Providing the reader with a comprehensive picture of the overall advances and the current cutting-edge developments in the field of Mathematical Models for remote sensing image analysis, this book is ideal as both a reference resource and a textbook for graduate and doctoral students as well as for remote sensing scientists and practitioners.
Josiane Zerubia - One of the best experts on this subject based on the ideXlab platform.
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Mathematical Models and Methods for Remote Sensing Image Analysis: An Introduction
Mathematical Models for Remote Sensing Image Processing, 2018Co-Authors: Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia, Jon Atli BenediktssonAbstract:The current progress of remote sensing systems, based on airborne and spaceborne platforms and involving active and passive sensors, provides an unprecedented wealth of information about the Earth surface for environmental monitoring, sustainable resource management, disaster prevention, emergency response, and defense. In this framework, Mathematical Models for image processing and analysis play fundamental roles. Effectively exploiting the potential conveyed by the availability of remote sensing data requires automatic or semi-automatic techniques capable of suitably characterizing and extracting thematic information of interest while minimizing the need for user intervention. The current development of Mathematical Models and methods for image processing and computer vision allows multiple remote sensing information extraction problems to be addressed successfully, accurately, and efficiently. In this introductory chapter, first, general characteristics of sensors and systems for Earth observation are summarized to define the basic terminology that will be used consistently throughout the book. Remote sensing image acquisition through passive and active sensors on-board spaceborne and airborne platforms is recalled together with the basic concepts of spatial, spectral, temporal, and radiometric resolution. Then, an overview of the main families of Mathematical Models and methods within the scientific field of two-dimensional remote sensing image processing is presented. The overall structure and organization of the book are also described.
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Mathematical Models for Remote Sensing Image Processing
2018Co-Authors: Gabriele Moser, Josiane ZerubiaAbstract:This book maximizes reader insights into the field of Mathematical Models and methods for the processing of two-dimensional remote sensing images. It presents a broad analysis of the field, encompassing passive and active sensors, hyperspectral images, synthetic aperture radar (SAR), interferometric SAR, and polarimetric SAR data. At the same time, it addresses highly topical subjects involving remote sensing data types (e.g., very high-resolution images, multiangular or multiresolution data, and satellite image time series) and analysis methodologies (e.g., probabilistic graphical Models, hierarchical image representations, kernel machines, data fusion, and compressive sensing) that currently have primary importance in the field of Mathematical modelling for remote sensing and image processing. Each chapter focuses on a particular type of remote sensing data and/or on a specific methodological area, presenting both a thorough analysis of the previous literature and a methodological and experimental discussion of at least two advanced Mathematical methods for information extraction from remote sensing data. This organization ensures that both tutorial information and advanced subjects are covered. With each chapter being written by research scientists from (at least) two different institutions, it offers multiple professional experiences and perspectives on each subject. The book also provides expert analysis and commentary from leading remote sensing and image processing researchers, many of whom serve on the editorial boards of prestigious international journals in these fields, and are actively involved in international scientific societies. Providing the reader with a comprehensive picture of the overall advances and the current cutting-edge developments in the field of Mathematical Models for remote sensing image analysis, this book is ideal as both a reference resource and a textbook for graduate and doctoral students as well as for remote sensing scientists and practitioners.
Sebastiano B. Serpico - One of the best experts on this subject based on the ideXlab platform.
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Mathematical Models and Methods for Remote Sensing Image Analysis: An Introduction
Mathematical Models for Remote Sensing Image Processing, 2018Co-Authors: Gabriele Moser, Sebastiano B. Serpico, Josiane Zerubia, Jon Atli BenediktssonAbstract:The current progress of remote sensing systems, based on airborne and spaceborne platforms and involving active and passive sensors, provides an unprecedented wealth of information about the Earth surface for environmental monitoring, sustainable resource management, disaster prevention, emergency response, and defense. In this framework, Mathematical Models for image processing and analysis play fundamental roles. Effectively exploiting the potential conveyed by the availability of remote sensing data requires automatic or semi-automatic techniques capable of suitably characterizing and extracting thematic information of interest while minimizing the need for user intervention. The current development of Mathematical Models and methods for image processing and computer vision allows multiple remote sensing information extraction problems to be addressed successfully, accurately, and efficiently. In this introductory chapter, first, general characteristics of sensors and systems for Earth observation are summarized to define the basic terminology that will be used consistently throughout the book. Remote sensing image acquisition through passive and active sensors on-board spaceborne and airborne platforms is recalled together with the basic concepts of spatial, spectral, temporal, and radiometric resolution. Then, an overview of the main families of Mathematical Models and methods within the scientific field of two-dimensional remote sensing image processing is presented. The overall structure and organization of the book are also described.
Marion Aubault - One of the best experts on this subject based on the ideXlab platform.
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Processed 5G Signals Mathematical Models for Positioning considering a Non-Constant Propagation Channel
2019Co-Authors: Anne-marie Tobie, Paul Thevenon, Axel Garcia-pena, Marion AubaultAbstract:The objective of this paper is to determine the ranging performance of the upcoming fifth generation (5G) signal. In order to do so, it is required to define 5G correlator outputs Mathematical Models. 5G systems will use OFDM (Orthogonal Frequency Division Multiplexing) signals; in the literature, Mathematical Models of OFDM signals are developed at the different receiver signal processing stages. These Models assumed that the propagation channel is constant over an OFDM symbol; nevertheless, an in-depth study of QuaDRiGa, a 5G compliant propagation channel simulator, invalidates this hypothesis. Therefore, in this paper, Mathematical Models are developed that take into account the channel evolution. The focus is given on correlator outputs and results are applied to the computation of 5G based pseudo range accuracy.
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VTC-Fall - Processed 5G Signals Mathematical Models for Positioning Considering a Non-Constant Propagation Channel
2019 IEEE 90th Vehicular Technology Conference (VTC2019-Fall), 2019Co-Authors: Anne-marie Tobie, Paul Thevenon, Axel Garcia-pena, Marion AubaultAbstract:The objective of this paper is to determine the ranging performance of the upcoming fifth generation (5G) signal. In order to do so, it is required to define 5G correlator outputs Mathematical Models. 5G systems will use OFDM (Orthogonal Frequency Division Multiplexing) signals; in the literature, Mathematical Models of OFDM signals are developed at the different receiver signal processing stages. These Models assumed that the propagation channel is constant over an OFDM symbol; nevertheless, an in-depth study of QuaDRiGa, a 5G compliant propagation channel simulator, invalidates this hypothesis. Therefore, in this paper, Mathematical Models are developed that take into account the channel evolution. The focus is given on correlator outputs and results are applied to the computation of 5G based pseudo range accuracy.
