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With the recent advances in remote sensing technologies for Earth observation, many different remote sensors are collecting data with distinctive properties. The obtained data are so large and complex that analyzing them manually becomes impractical or even impossible. Therefore, understanding remote sensing images effectively, in connection with physics, has been the primary concern of the remote sensing research community in recent years. For this purpose, machine learning is thought to be a promising technique because it can make the system learn to improve itself. With this distinctive characteristic, the algorithms will be more adaptive, automatic, and intelligent. This book introduces some of the most challenging issues of machine learning in the field of remote sensing, and the latest advanced technologies developed for different applications. It integrates with multi-source/multi-temporal/multi-scale data, and mainly focuses on learning to understand remote sensing images. Particularly, it presents many more effective techniques based on the popular concepts of deep learning and big data to reach new heights of data understanding. Through reporting recent advances in the machine learning approaches towards analyzing and understanding remote sensing images, this book can help readers become more familiar with knowledge frontier and foster an increased interest in this field.
Remote Sensing of Sea Ice in the Northern Sea Route: Studies and Applications initially provides a history of the Northern Sea Route as an important strategic transport route for supporting the northern regions of Russia and cargo transportation between Europe and the Northern Pacific Basin. The authors then describe sea ice conditions in the Eurasian Arctic Seas and, using microwave satellite data, provide a detailed analysis of difficult sea ice conditions. Remote sensing techniques and the basic principles of SAR image formation are described, as well as the major satellite radar systems used for ice studies in the Arctic. The authors take a good look at the use of sensing equipment in experiments, including the ICE WATCH project used for monitoring the Northern Sea Route. The possibilities of using SAR remote sensing for ice navigation in the Northern Sea Route is also detailed, analysing techniques of automatic image processing and interpretation. A study is provided of regional drifting ice, fast ice and river ice in the coastal areas of the Arctic Seas. The book concludes with a review of the practical experience using SAR images for supporting navigation and offshore industrial activity, based on a series of experiments conducted with the Murmansk Shipping Company on board nuclear icebreakers.
DEEP LEARNING FOR THE EARTH SCIENCES Explore this insightful treatment of deep learning in the field of earth sciences, from four leading voices Deep learning is a fundamental technique in modern Artificial Intelligence and is being applied to disciplines across the scientific spectrum; earth science is no exception. Yet, the link between deep learning and Earth sciences has only recently entered academic curricula and thus has not yet proliferated. Deep Learning for the Earth Sciences delivers a unique perspective and treatment of the concepts, skills, and practices necessary to quickly become familiar with the application of deep learning techniques to the Earth sciences. The book prepares readers to be ready to use the technologies and principles described in their own research. The distinguished editors have also included resources that explain and provide new ideas and recommendations for new research especially useful to those involved in advanced research education or those seeking PhD thesis orientations. Readers will also benefit from the inclusion of: An introduction to deep learning for classification purposes, including advances in image segmentation and encoding priors, anomaly detection and target detection, and domain adaptation An exploration of learning representations and unsupervised deep learning, including deep learning image fusion, image retrieval, and matching and co-registration Practical discussions of regression, fitting, parameter retrieval, forecasting and interpolation An examination of physics-aware deep learning models, including emulation of complex codes and model parametrizations Perfect for PhD students and researchers in the fields of geosciences, image processing, remote sensing, electrical engineering and computer science, and machine learning, Deep Learning for the Earth Sciences will also earn a place in the libraries of machine learning and pattern recognition researchers, engineers, and scientists.
This book reviews popular data-assimilation methods, such as weak and strong constraint variational methods, ensemble filters and smoothers. The author shows how different methods can be derived from a common theoretical basis, as well as how they differ or are related to each other, and which properties characterize them, using several examples. Readers will appreciate the included introductory material and detailed derivations in the text, and a supplemental web site.
Published by the American Geophysical Union as part of the Geophysical Monograph Series, Volume 68. Human activities in the polar regions have undergone incredible changes in this century. Among these changes is the revolution that satellites have brought about in obtaining information concerning polar geophysical processes. Satellites have flown for about three decades, and the polar regions have been the subject of their routine surveillance for more than half that time. Our observations of polar regions have evolved from happenstance ship sightings and isolated harbor icing records to routine global records obtained by those satellites. Thanks to such abundant data, we now know a great deal about the ice-covered seas, which constitute about 10% of the Earth's surface. This explosion of information about sea ice has fascinated scientists for some 20 years. We are now at a point of transition in sea ice studies; we are concerned less about ice itself and more about its role in the climate system. This change in emphasis has been the prime stimulus for this book.
Describes the types of information available from spaceborne images of the ocean.
SEA ICE The latest edition of the gold standard in sea ice references In the newly revised second edition of Sea Ice: Physics and Remote Sensing, a team of distinguished researchers delivers an in-depth review of the features and structural properties of ice, as well as the latest advances in geophysical sensors, ice parameter retrieval techniques, and remote sensing data. The book has been updated to reflect the latest scientific developments in macro- and micro-scale sea ice research. For this edition, the authors have included high-quality photographs of thin sections from cores of various ice types, as well as a comprehensive account of all major field expeditions that have systematically surveyed sea ice and its properties. Readers will also find: A thorough introduction to ice physics and physical processes, including ice morphology and age-based structural features Practical discussions of radiometric and radar-scattering observations from sea ice, including radar backscatter and microwave emission The latest techniques for the retrieval of sea ice parameters from space-borne and airborne sensor data New chapters on sea ice thermal microwave emissions and on the impact of climate change on polar sea ice Perfect for academic researchers working on sea ice, the cryosphere, and climatology, Sea Ice: Physics and Remote Sensing will also benefit meteorologists, marine operators, and high-latitude construction engineers.
"...this edition is useful and effective in teaching Bayesian inference at both elementary and intermediate levels. It is a well-written book on elementary Bayesian inference, and the material is easily accessible. It is both concise and timely, and provides a good collection of overviews and reviews of important tools used in Bayesian statistical methods." There is a strong upsurge in the use of Bayesian methods in applied statistical analysis, yet most introductory statistics texts only present frequentist methods. Bayesian statistics has many important advantages that students should learn about if they are going into fields where statistics will be used. In this third Edition, four newly-added chapters address topics that reflect the rapid advances in the field of Bayesian statistics. The authors continue to provide a Bayesian treatment of introductory statistical topics, such as scientific data gathering, discrete random variables, robust Bayesian methods, and Bayesian approaches to inference for discrete random variables, binomial proportions, Poisson, and normal means, and simple linear regression. In addition, more advanced topics in the field are presented in four new chapters: Bayesian inference for a normal with unknown mean and variance; Bayesian inference for a Multivariate Normal mean vector; Bayesian inference for the Multiple Linear Regression Model; and Computational Bayesian Statistics including Markov Chain Monte Carlo. The inclusion of these topics will facilitate readers' ability to advance from a minimal understanding of Statistics to the ability to tackle topics in more applied, advanced level books. Minitab macros and R functions are available on the book's related website to assist with chapter exercises. Introduction to Bayesian Statistics, Third Edition also features: Topics including the Joint Likelihood function and inference using independent Jeffreys priors and join conjugate prior The cutting-edge topic of computational Bayesian Statistics in a new chapter, with a unique focus on Markov Chain Monte Carlo methods Exercises throughout the book that have been updated to reflect new applications and the latest software applications Detailed appendices that guide readers through the use of R and Minitab software for Bayesian analysis and Monte Carlo simulations, with all related macros available on the book's website Introduction to Bayesian Statistics, Third Edition is a textbook for upper-undergraduate or first-year graduate level courses on introductory statistics course with a Bayesian emphasis. It can also be used as a reference work for statisticians who require a working knowledge of Bayesian statistics.
Many advances in spaceborne instrumentation, remote sensing, and data analysis have occurred in recent years, but until now there has been no book that reflects these advances while delivering a uniform treatment of the remote sensing of frozen regions. Remote Sensing of Snow and Ice identifies unifying themes and ideas in these fields and presents them in a single volume. This book provides a comprehensive introduction to the remote sensing of the Earth’s cryosphere. Explaining why cryospheric observations are important and why remote sensing observations are essential, it offers thorough surveys of the physical properties of ice and snow, and of current and emerging remote sensing techniques. Presenting a technical review of how the properties of snow and ice relate to remote sensing observations, the book focuses on principles by which useful geophysical information becomes encoded into the electromagnetic radiation detected during the remote sensing process. The author then discusses in detail the application of remote sensing methods to snow, freshwater ice, glaciers, and icebergs. The book concludes with a summary that examines what remote sensing has revealed about the cryosphere, where major technical problems still exist, and how these problems can be addressed.
ICe in the Ocean examines sea ice and icebergs and their role in the global climate system. It is comprehensive textbook suitablefor students, pure and applied researchers, and anyone interested in the polar oceans; the distribution of sea ice; the mechanisms of growth, development and decay; the thermodynamics and dynamics of sea ice; sea ice deformation and ridge-building; the role of marginal ice zones; the characteristics of icebergs; and the part played by sea ice in the climate system and in the transport of pollutants. An extensive reference list and recommendations for further reading and numerous illustrations, and add to the usefulness of the text.