Mathematical Principles of Remote Sensing, Milman, Andrew S
Автор: Rees Название: Physical Principles of Remote Sensing (2017) ISBN: 052118116X ISBN-13(EAN): 9780521181167 Издательство: Cambridge Academ Рейтинг: Цена: 43990.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Covering a wide range of remote sensing techniques and applications, this new edition is now more accessible to students, while retaining its focus on physical and mathematical principles. Chapter summaries, review questions, problem sets and supporting online material allow students to test their understanding and practise handling data for themselves.
Автор: Li Название: Digital Terrain Modeling ISBN: 0415324629 ISBN-13(EAN): 9780415324625 Издательство: Taylor&Francis Рейтинг: Цена: 163330.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Provides coverage of the developments in the field of Digital terrain models. This book includes an examination of data acquisition and a review of the presentation of DTMs in databases, in contour form, and in various forms of computer graphics. It also explores the theories, methods, and algorithms of digital terrain modeling.
Автор: Donald B. Percival, Andrew T. Walden Название: Spectral Analysis for Univariate Time Series ISBN: 1107028140 ISBN-13(EAN): 9781107028142 Издательство: Cambridge Academ Рейтинг: Цена: 97150.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Spectral analysis is an important technique for interpreting time series data. This book uses the R language and real world examples to show data analysts interested in time series in the environmental, engineering and physical sciences how to bridge the gap between the statistical theory behind spectral analysis and its application to actual data.
Автор: Gabriele Moser; Josiane Zerubia Название: Mathematical Models for Remote Sensing Image Processing ISBN: 3319663283 ISBN-13(EAN): 9783319663289 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: 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.
Автор: A.I. Kozlov; L.P. Ligthart; A.I. Logvin Название: Mathematical and Physical Modelling of Microwave Scattering and Polarimetric Remote Sensing ISBN: 9048158680 ISBN-13(EAN): 9789048158683 Издательство: Springer Рейтинг: Цена: 174130.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Radar technology is increasingly being used to monitor the environment. The subsequent eight chapters summarize applications of polarimetric radar monitoring for various types of earth environments, including vegetation and oceans.
Автор: Warnick, Karl F. (brigham Young University, Utah) Maaskant, Rob (chalmers University Of Technology, Gothenberg) Ivashina, Marianna V. (chalmers Univer Название: Phased arrays for radio astronomy, remote sensing, and satellite communications ISBN: 1108423922 ISBN-13(EAN): 9781108423922 Издательство: Cambridge Academ Рейтинг: Цена: 121440.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Discover a modern approach to the analysis and design of high sensitivity phased arrays for radio astronomy, remote sensing and satellite communications applications with this unique text. It covers the latest numerical methods and computational modeling tools, including beamforming, digital signal processing, and interferometric imaging.
Автор: Gabriele Moser; Josiane Zerubia Название: Mathematical Models for Remote Sensing Image Processing ISBN: 3319882198 ISBN-13(EAN): 9783319882192 Издательство: Springer Рейтинг: Цена: 149060.00 T Наличие на складе: Поставка под заказ. Описание: 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.
Автор: Baldi, Pierre (university Of California, Irvine) Название: Deep learning in science ISBN: 1108845355 ISBN-13(EAN): 9781108845359 Издательство: Cambridge Academ Рейтинг: Цена: 52790.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This is the first rigorous, self-contained treatment of the theory of deep learning. Aimed at scientists, instructors, and students interested in artificial intelligence and deep learning, it demonstrates many applications in physics, chemistry, and biomedicine. It includes a full set of exercises and encourages out-of-the-box thinking.
Автор: William W. Hsieh Название: Introduction to Environmental Data Science ISBN: 1107065550 ISBN-13(EAN): 9781107065550 Издательство: Cambridge Academ Рейтинг: Цена: 65470.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: Statistical and machine learning methods have many applications in the environmental sciences, including prediction and data analysis in meteorology, hydrology and oceanography; pattern recognition for satellite images from remote sensing; management of agriculture and forests; assessment of climate change; and much more. With rapid advances in machine learning in the last decade, this book provides an urgently needed, comprehensive guide to machine learning and statistics for students and researchers interested in environmental data science. It includes intuitive explanations covering the relevant background mathematics, with examples drawn from the environmental sciences. A broad range of topics is covered, including correlation, regression, classification, clustering, neural networks, random forests, boosting, kernel methods, evolutionary algorithms and deep learning, as well as the recent merging of machine learning and physics. End?of?chapter exercises allow readers to develop their problem-solving skills, and online datasets allow readers to practise analysis of real data.
Автор: Lloyd N. Trefethen Название: Approximation Theory and Approximation Practice: Extended Edition ISBN: 161197593X ISBN-13(EAN): 9781611975932 Издательство: Mare Nostrum (Eurospan) Рейтинг: Цена: 60990.00 T Наличие на складе: Нет в наличии. Описание: This is a textbook on classical polynomial and rational approximation theory for the twenty-first century. Aimed at advanced undergraduates and graduate students across all of applied mathematics, it uses MATLAB to teach the field’s most important ideas and results.
Approximation Theory and Approximation Practice, Extended Edition differs fundamentally from other works on approximation theory in a number of ways: its emphasis is on topics close to numerical algorithms; concepts are illustrated with Chebfun; and each chapter is a PUBLISHable MATLAB M-file, available online.
The book centers on theorems and methods for analytic functions, which appear so often in applications, rather than on functions at the edge of discontinuity with their seductive theoretical challenges. Original sources are cited rather than textbooks, and each item in the bibliography is accompanied by an editorial comment. In addition, each chapter has a collection of exercises, which span a wide range from mathematical theory to Chebfun-based numerical experimentation.
Автор: Burkholder Название: The 3D Global Spatial Data Model - ISBN: 1498722164 ISBN-13(EAN): 9781498722162 Издательство: Taylor&Francis Рейтинг: Цена: 158230.00 T Наличие на складе: Есть у поставщика Поставка под заказ. Описание: This new second edition demystifies the concepts of spatial data accuracy and provides mathematical clarity to issues of network accuracy and local accuracy. Ideal for both beginner and advanced levels, this book also provides guidance and insight on how to link to the data collected and stored in legacy systems.
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